Linear.Matrix:det44 from linear-1.19.1.3

Percentage Accurate: 30.0% → 40.0%

Time: 33.6s

Alternatives: 35

Speedup: 5.6×

• 89.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (+
  (-
   (+
    (+
     (-
      (* (- (* x y) (* z t)) (- (* a b) (* c i)))
      (* (- (* x j) (* z k)) (- (* y0 b) (* y1 i))))
     (* (- (* x y2) (* z y3)) (- (* y0 c) (* y1 a))))
    (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i))))
   (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a))))
  (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)))

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) - Float64(Float64(Float64(x * j) - Float64(z * k)) * Float64(Float64(y0 * b) - Float64(y1 * i)))) + Float64(Float64(Float64(x * y2) - Float64(z * y3)) * Float64(Float64(y0 * c) - Float64(y1 * a)))) + Float64(Float64(Float64(t * j) - Float64(y * k)) * Float64(Float64(y4 * b) - Float64(y5 * i)))) - Float64(Float64(Float64(t * y2) - Float64(y * y3)) * Float64(Float64(y4 * c) - Float64(y5 * a)))) + Float64(Float64(Float64(k * y2) - Float64(j * y3)) * Float64(Float64(y4 * y1) - Float64(y5 * y0))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * N[(N[(a * b), $MachinePrecision] - N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * j), $MachinePrecision] - N[(z * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * b), $MachinePrecision] - N[(y1 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * c), $MachinePrecision] - N[(y1 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * b), $MachinePrecision] - N[(y5 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * c), $MachinePrecision] - N[(y5 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * y1), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 30.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (+
  (-
   (+
    (+
     (-
      (* (- (* x y) (* z t)) (- (* a b) (* c i)))
      (* (- (* x j) (* z k)) (- (* y0 b) (* y1 i))))
     (* (- (* x y2) (* z y3)) (- (* y0 c) (* y1 a))))
    (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i))))
   (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a))))
  (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)))

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) - Float64(Float64(Float64(x * j) - Float64(z * k)) * Float64(Float64(y0 * b) - Float64(y1 * i)))) + Float64(Float64(Float64(x * y2) - Float64(z * y3)) * Float64(Float64(y0 * c) - Float64(y1 * a)))) + Float64(Float64(Float64(t * j) - Float64(y * k)) * Float64(Float64(y4 * b) - Float64(y5 * i)))) - Float64(Float64(Float64(t * y2) - Float64(y * y3)) * Float64(Float64(y4 * c) - Float64(y5 * a)))) + Float64(Float64(Float64(k * y2) - Float64(j * y3)) * Float64(Float64(y4 * y1) - Float64(y5 * y0))))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * N[(N[(a * b), $MachinePrecision] - N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * j), $MachinePrecision] - N[(z * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * b), $MachinePrecision] - N[(y1 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * c), $MachinePrecision] - N[(y1 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * b), $MachinePrecision] - N[(y5 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * c), $MachinePrecision] - N[(y5 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * y1), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 40.0% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right)\\ t_2 := \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right)\\ t_3 := \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right)\\ t_4 := \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right)\\ t_5 := \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right)\\ t_6 := \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right)\\ \mathbf{if}\;z \leq -2 \cdot 10^{+84}:\\ \;\;\;\;\left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c\\ \mathbf{elif}\;z \leq -9.5 \cdot 10^{-49}:\\ \;\;\;\;\left(-y3\right) \cdot \left(\mathsf{fma}\left(t\_4, j, t\_5 \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)\\ \mathbf{elif}\;z \leq -3.1 \cdot 10^{-218}:\\ \;\;\;\;\left(\mathsf{fma}\left(-y, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), t\_4 \cdot y2\right) + z \cdot t\_2\right) \cdot k\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{-104}:\\ \;\;\;\;\left(\mathsf{fma}\left(-y5, t\_6, t\_1 \cdot c\right) - t\_3 \cdot b\right) \cdot y0\\ \mathbf{elif}\;z \leq 2.45 \cdot 10^{+163}:\\ \;\;\;\;\left(\mathsf{fma}\left(-a, t\_1, t\_6 \cdot y4\right) + i \cdot t\_3\right) \cdot y1\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{+198}:\\ \;\;\;\;\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - t\_3 \cdot y1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-z\right) \cdot \left(\mathsf{fma}\left(t\_5, y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - t\_2 \cdot k\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (fma y2 x (* (- y3) z)))
        (t_2 (fma y0 b (* (- i) y1)))
        (t_3 (fma j x (* (- k) z)))
        (t_4 (fma y4 y1 (* (- y0) y5)))
        (t_5 (fma y0 c (* (- a) y1)))
        (t_6 (fma y2 k (* (- j) y3))))
   (if (<= z -2e+84)
     (* (* z (fma (- y0) y3 (* i t))) c)
     (if (<= z -9.5e-49)
       (* (- y3) (- (fma t_4 j (* t_5 z)) (* (fma y4 c (* (- a) y5)) y)))
       (if (<= z -3.1e-218)
         (* (+ (fma (- y) (fma y4 b (* (- i) y5)) (* t_4 y2)) (* z t_2)) k)
         (if (<= z 1.05e-104)
           (* (- (fma (- y5) t_6 (* t_1 c)) (* t_3 b)) y0)
           (if (<= z 2.45e+163)
             (* (+ (fma (- a) t_1 (* t_6 y4)) (* i t_3)) y1)
             (if (<= z 2.7e+198)
               (*
                (- i)
                (-
                 (fma (fma y x (* (- t) z)) c (* (fma j t (* (- k) y)) y5))
                 (* t_3 y1)))
               (*
                (- z)
                (- (fma t_5 y3 (* (fma b a (* (- c) i)) t)) (* t_2 k)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = fma(y2, x, (-y3 * z));
	double t_2 = fma(y0, b, (-i * y1));
	double t_3 = fma(j, x, (-k * z));
	double t_4 = fma(y4, y1, (-y0 * y5));
	double t_5 = fma(y0, c, (-a * y1));
	double t_6 = fma(y2, k, (-j * y3));
	double tmp;
	if (z <= -2e+84) {
		tmp = (z * fma(-y0, y3, (i * t))) * c;
	} else if (z <= -9.5e-49) {
		tmp = -y3 * (fma(t_4, j, (t_5 * z)) - (fma(y4, c, (-a * y5)) * y));
	} else if (z <= -3.1e-218) {
		tmp = (fma(-y, fma(y4, b, (-i * y5)), (t_4 * y2)) + (z * t_2)) * k;
	} else if (z <= 1.05e-104) {
		tmp = (fma(-y5, t_6, (t_1 * c)) - (t_3 * b)) * y0;
	} else if (z <= 2.45e+163) {
		tmp = (fma(-a, t_1, (t_6 * y4)) + (i * t_3)) * y1;
	} else if (z <= 2.7e+198) {
		tmp = -i * (fma(fma(y, x, (-t * z)), c, (fma(j, t, (-k * y)) * y5)) - (t_3 * y1));
	} else {
		tmp = -z * (fma(t_5, y3, (fma(b, a, (-c * i)) * t)) - (t_2 * k));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = fma(y2, x, Float64(Float64(-y3) * z))
	t_2 = fma(y0, b, Float64(Float64(-i) * y1))
	t_3 = fma(j, x, Float64(Float64(-k) * z))
	t_4 = fma(y4, y1, Float64(Float64(-y0) * y5))
	t_5 = fma(y0, c, Float64(Float64(-a) * y1))
	t_6 = fma(y2, k, Float64(Float64(-j) * y3))
	tmp = 0.0
	if (z <= -2e+84)
		tmp = Float64(Float64(z * fma(Float64(-y0), y3, Float64(i * t))) * c);
	elseif (z <= -9.5e-49)
		tmp = Float64(Float64(-y3) * Float64(fma(t_4, j, Float64(t_5 * z)) - Float64(fma(y4, c, Float64(Float64(-a) * y5)) * y)));
	elseif (z <= -3.1e-218)
		tmp = Float64(Float64(fma(Float64(-y), fma(y4, b, Float64(Float64(-i) * y5)), Float64(t_4 * y2)) + Float64(z * t_2)) * k);
	elseif (z <= 1.05e-104)
		tmp = Float64(Float64(fma(Float64(-y5), t_6, Float64(t_1 * c)) - Float64(t_3 * b)) * y0);
	elseif (z <= 2.45e+163)
		tmp = Float64(Float64(fma(Float64(-a), t_1, Float64(t_6 * y4)) + Float64(i * t_3)) * y1);
	elseif (z <= 2.7e+198)
		tmp = Float64(Float64(-i) * Float64(fma(fma(y, x, Float64(Float64(-t) * z)), c, Float64(fma(j, t, Float64(Float64(-k) * y)) * y5)) - Float64(t_3 * y1)));
	else
		tmp = Float64(Float64(-z) * Float64(fma(t_5, y3, Float64(fma(b, a, Float64(Float64(-c) * i)) * t)) - Float64(t_2 * k)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(y2 * x + N[((-y3) * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y0 * b + N[((-i) * y1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(j * x + N[((-k) * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(y4 * y1 + N[((-y0) * y5), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(y0 * c + N[((-a) * y1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(y2 * k + N[((-j) * y3), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2e+84], N[(N[(z * N[((-y0) * y3 + N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[z, -9.5e-49], N[((-y3) * N[(N[(t$95$4 * j + N[(t$95$5 * z), $MachinePrecision]), $MachinePrecision] - N[(N[(y4 * c + N[((-a) * y5), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -3.1e-218], N[(N[(N[((-y) * N[(y4 * b + N[((-i) * y5), $MachinePrecision]), $MachinePrecision] + N[(t$95$4 * y2), $MachinePrecision]), $MachinePrecision] + N[(z * t$95$2), $MachinePrecision]), $MachinePrecision] * k), $MachinePrecision], If[LessEqual[z, 1.05e-104], N[(N[(N[((-y5) * t$95$6 + N[(t$95$1 * c), $MachinePrecision]), $MachinePrecision] - N[(t$95$3 * b), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[z, 2.45e+163], N[(N[(N[((-a) * t$95$1 + N[(t$95$6 * y4), $MachinePrecision]), $MachinePrecision] + N[(i * t$95$3), $MachinePrecision]), $MachinePrecision] * y1), $MachinePrecision], If[LessEqual[z, 2.7e+198], N[((-i) * N[(N[(N[(y * x + N[((-t) * z), $MachinePrecision]), $MachinePrecision] * c + N[(N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision] - N[(t$95$3 * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[((-z) * N[(N[(t$95$5 * y3 + N[(N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision] - N[(t$95$2 * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]

Derivation

1. Split input into 7 regimes

2. if z < -2.00000000000000012e84

   1. Initial program 20.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 48.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in z around inf

      \[\leadsto \left(z \cdot \left(-1 \cdot \left(y0 \cdot y3\right) + i \cdot t\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 58.6%

      \[\leadsto \left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c \]

   if -2.00000000000000012e84 < z < -9.50000000000000006e-49

   1. Initial program 42.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 62.2%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   if -9.50000000000000006e-49 < z < -3.09999999999999997e-218

   1. Initial program 30.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in k around inf

      \[\leadsto \color{blue}{k \cdot \left(\left(-1 \cdot \left(y \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + y2 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) - -1 \cdot \left(z \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + y2 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) - -1 \cdot \left(z \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right) \cdot k} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + y2 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) - -1 \cdot \left(z \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right) \cdot k} \]

   5. Applied rewrites 58.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right) \cdot y2\right) - \left(-z\right) \cdot \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right)\right) \cdot k} \]

   if -3.09999999999999997e-218 < z < 1.04999999999999999e-104

   1. Initial program 30.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 54.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   if 1.04999999999999999e-104 < z < 2.45e163

   1. Initial program 30.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y1 around inf

      \[\leadsto \color{blue}{y1 \cdot \left(\left(-1 \cdot \left(a \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) + y4 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - -1 \cdot \left(i \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(a \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) + y4 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - -1 \cdot \left(i \cdot \left(j \cdot x - k \cdot z\right)\right)\right) \cdot y1} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(a \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) + y4 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - -1 \cdot \left(i \cdot \left(j \cdot x - k \cdot z\right)\right)\right) \cdot y1} \]

   5. Applied rewrites 56.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-a, \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right), \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right) \cdot y4\right) - \left(-i\right) \cdot \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right)\right) \cdot y1} \]

   if 2.45e163 < z < 2.6999999999999999e198

   1. Initial program 25.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 83.3%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   if 2.6999999999999999e198 < z

   1. Initial program 15.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 84.6%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]
3. Recombined 7 regimes into one program.

4. Final simplification 59.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2 \cdot 10^{+84}:\\ \;\;\;\;\left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c\\ \mathbf{elif}\;z \leq -9.5 \cdot 10^{-49}:\\ \;\;\;\;\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)\\ \mathbf{elif}\;z \leq -3.1 \cdot 10^{-218}:\\ \;\;\;\;\left(\mathsf{fma}\left(-y, \mathsf{fma}\left(y4, b, \left(-i\right) \cdot y5\right), \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right) \cdot y2\right) + z \cdot \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right)\right) \cdot k\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{-104}:\\ \;\;\;\;\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0\\ \mathbf{elif}\;z \leq 2.45 \cdot 10^{+163}:\\ \;\;\;\;\left(\mathsf{fma}\left(-a, \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right), \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right) \cdot y4\right) + i \cdot \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right)\right) \cdot y1\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{+198}:\\ \;\;\;\;\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 54.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right)\\ \mathbf{if}\;t\_1 \leq \infty:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, \left(-a\right) \cdot y1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1
         (+
          (-
           (+
            (+
             (-
              (* (- (* x y) (* z t)) (- (* a b) (* c i)))
              (* (- (* x j) (* z k)) (- (* y0 b) (* y1 i))))
             (* (- (* x y2) (* z y3)) (- (* y0 c) (* y1 a))))
            (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i))))
           (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a))))
          (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0))))))
   (if (<= t_1 INFINITY)
     t_1
     (*
      (- y3)
      (fma z (fma c y0 (* (- a) y1)) (* j (fma y1 y4 (* (- y0) y5))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - (((x * j) - (z * k)) * ((y0 * b) - (y1 * i)))) + (((x * y2) - (z * y3)) * ((y0 * c) - (y1 * a)))) + (((t * j) - (y * k)) * ((y4 * b) - (y5 * i)))) - (((t * y2) - (y * y3)) * ((y4 * c) - (y5 * a)))) + (((k * y2) - (j * y3)) * ((y4 * y1) - (y5 * y0)));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = -y3 * fma(z, fma(c, y0, (-a * y1)), (j * fma(y1, y4, (-y0 * y5))));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) - Float64(Float64(Float64(x * j) - Float64(z * k)) * Float64(Float64(y0 * b) - Float64(y1 * i)))) + Float64(Float64(Float64(x * y2) - Float64(z * y3)) * Float64(Float64(y0 * c) - Float64(y1 * a)))) + Float64(Float64(Float64(t * j) - Float64(y * k)) * Float64(Float64(y4 * b) - Float64(y5 * i)))) - Float64(Float64(Float64(t * y2) - Float64(y * y3)) * Float64(Float64(y4 * c) - Float64(y5 * a)))) + Float64(Float64(Float64(k * y2) - Float64(j * y3)) * Float64(Float64(y4 * y1) - Float64(y5 * y0))))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(Float64(-y3) * fma(z, fma(c, y0, Float64(Float64(-a) * y1)), Float64(j * fma(y1, y4, Float64(Float64(-y0) * y5)))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * N[(N[(a * b), $MachinePrecision] - N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * j), $MachinePrecision] - N[(z * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * b), $MachinePrecision] - N[(y1 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y0 * c), $MachinePrecision] - N[(y1 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * b), $MachinePrecision] - N[(y5 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * c), $MachinePrecision] - N[(y5 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y4 * y1), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[((-y3) * N[(z * N[(c * y0 + N[((-a) * y1), $MachinePrecision]), $MachinePrecision] + N[(j * N[(y1 * y4 + N[((-y0) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (+.f64 (+.f64 (-.f64 (*.f64 (-.f64 (*.f64 x y) (*.f64 z t)) (-.f64 (*.f64 a b) (*.f64 c i))) (*.f64 (-.f64 (*.f64 x j) (*.f64 z k)) (-.f64 (*.f64 y0 b) (*.f64 y1 i)))) (*.f64 (-.f64 (*.f64 x y2) (*.f64 z y3)) (-.f64 (*.f64 y0 c) (*.f64 y1 a)))) (*.f64 (-.f64 (*.f64 t j) (*.f64 y k)) (-.f64 (*.f64 y4 b) (*.f64 y5 i)))) (*.f64 (-.f64 (*.f64 t y2) (*.f64 y y3)) (-.f64 (*.f64 y4 c) (*.f64 y5 a)))) (*.f64 (-.f64 (*.f64 k y2) (*.f64 j y3)) (-.f64 (*.f64 y4 y1) (*.f64 y5 y0)))) < +inf.0

   1. Initial program 96.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   if +inf.0 < (+.f64 (-.f64 (+.f64 (+.f64 (-.f64 (*.f64 (-.f64 (*.f64 x y) (*.f64 z t)) (-.f64 (*.f64 a b) (*.f64 c i))) (*.f64 (-.f64 (*.f64 x j) (*.f64 z k)) (-.f64 (*.f64 y0 b) (*.f64 y1 i)))) (*.f64 (-.f64 (*.f64 x y2) (*.f64 z y3)) (-.f64 (*.f64 y0 c) (*.f64 y1 a)))) (*.f64 (-.f64 (*.f64 t j) (*.f64 y k)) (-.f64 (*.f64 y4 b) (*.f64 y5 i)))) (*.f64 (-.f64 (*.f64 t y2) (*.f64 y y3)) (-.f64 (*.f64 y4 c) (*.f64 y5 a)))) (*.f64 (-.f64 (*.f64 k y2) (*.f64 j y3)) (-.f64 (*.f64 y4 y1) (*.f64 y5 y0))))

   1. Initial program 0.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 34.9%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y around 0

      \[\leadsto \left(-y3\right) \cdot \left(j \cdot \left(-1 \cdot \left(y0 \cdot y5\right) + y1 \cdot y4\right) + \color{blue}{z \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 37.3%

      \[\leadsto \left(-y3\right) \cdot \mathsf{fma}\left(z, \color{blue}{\mathsf{fma}\left(c, y0, -a \cdot y1\right)}, j \cdot \mathsf{fma}\left(y1, y4, -y0 \cdot y5\right)\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 54.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \leq \infty:\\ \;\;\;\;\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, \left(-a\right) \cdot y1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 40.4% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right)\\ t_2 := \mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right)\\ t_3 := \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right)\\ t_4 := \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right)\\ t_5 := \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right)\\ t_6 := \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right)\\ \mathbf{if}\;z \leq -2 \cdot 10^{+84}:\\ \;\;\;\;\left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c\\ \mathbf{elif}\;z \leq -2.8 \cdot 10^{-51}:\\ \;\;\;\;\left(-y3\right) \cdot \left(\mathsf{fma}\left(t\_2, j, t\_5 \cdot z\right) - t\_1 \cdot y\right)\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-170}:\\ \;\;\;\;\left(\mathsf{fma}\left(t\_2, k, t\_5 \cdot x\right) - t\_1 \cdot t\right) \cdot y2\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{-104}:\\ \;\;\;\;\left(\mathsf{fma}\left(-y5, t\_3, t\_4 \cdot c\right) - t\_6 \cdot b\right) \cdot y0\\ \mathbf{elif}\;z \leq 2.45 \cdot 10^{+163}:\\ \;\;\;\;\left(\mathsf{fma}\left(-a, t\_4, t\_3 \cdot y4\right) + i \cdot t\_6\right) \cdot y1\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{+198}:\\ \;\;\;\;\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - t\_6 \cdot y1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-z\right) \cdot \left(\mathsf{fma}\left(t\_5, y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (fma y4 c (* (- a) y5)))
        (t_2 (fma y4 y1 (* (- y0) y5)))
        (t_3 (fma y2 k (* (- j) y3)))
        (t_4 (fma y2 x (* (- y3) z)))
        (t_5 (fma y0 c (* (- a) y1)))
        (t_6 (fma j x (* (- k) z))))
   (if (<= z -2e+84)
     (* (* z (fma (- y0) y3 (* i t))) c)
     (if (<= z -2.8e-51)
       (* (- y3) (- (fma t_2 j (* t_5 z)) (* t_1 y)))
       (if (<= z -4.2e-170)
         (* (- (fma t_2 k (* t_5 x)) (* t_1 t)) y2)
         (if (<= z 1.05e-104)
           (* (- (fma (- y5) t_3 (* t_4 c)) (* t_6 b)) y0)
           (if (<= z 2.45e+163)
             (* (+ (fma (- a) t_4 (* t_3 y4)) (* i t_6)) y1)
             (if (<= z 2.7e+198)
               (*
                (- i)
                (-
                 (fma (fma y x (* (- t) z)) c (* (fma j t (* (- k) y)) y5))
                 (* t_6 y1)))
               (*
                (- z)
                (-
                 (fma t_5 y3 (* (fma b a (* (- c) i)) t))
                 (* (fma y0 b (* (- i) y1)) k)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = fma(y4, c, (-a * y5));
	double t_2 = fma(y4, y1, (-y0 * y5));
	double t_3 = fma(y2, k, (-j * y3));
	double t_4 = fma(y2, x, (-y3 * z));
	double t_5 = fma(y0, c, (-a * y1));
	double t_6 = fma(j, x, (-k * z));
	double tmp;
	if (z <= -2e+84) {
		tmp = (z * fma(-y0, y3, (i * t))) * c;
	} else if (z <= -2.8e-51) {
		tmp = -y3 * (fma(t_2, j, (t_5 * z)) - (t_1 * y));
	} else if (z <= -4.2e-170) {
		tmp = (fma(t_2, k, (t_5 * x)) - (t_1 * t)) * y2;
	} else if (z <= 1.05e-104) {
		tmp = (fma(-y5, t_3, (t_4 * c)) - (t_6 * b)) * y0;
	} else if (z <= 2.45e+163) {
		tmp = (fma(-a, t_4, (t_3 * y4)) + (i * t_6)) * y1;
	} else if (z <= 2.7e+198) {
		tmp = -i * (fma(fma(y, x, (-t * z)), c, (fma(j, t, (-k * y)) * y5)) - (t_6 * y1));
	} else {
		tmp = -z * (fma(t_5, y3, (fma(b, a, (-c * i)) * t)) - (fma(y0, b, (-i * y1)) * k));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = fma(y4, c, Float64(Float64(-a) * y5))
	t_2 = fma(y4, y1, Float64(Float64(-y0) * y5))
	t_3 = fma(y2, k, Float64(Float64(-j) * y3))
	t_4 = fma(y2, x, Float64(Float64(-y3) * z))
	t_5 = fma(y0, c, Float64(Float64(-a) * y1))
	t_6 = fma(j, x, Float64(Float64(-k) * z))
	tmp = 0.0
	if (z <= -2e+84)
		tmp = Float64(Float64(z * fma(Float64(-y0), y3, Float64(i * t))) * c);
	elseif (z <= -2.8e-51)
		tmp = Float64(Float64(-y3) * Float64(fma(t_2, j, Float64(t_5 * z)) - Float64(t_1 * y)));
	elseif (z <= -4.2e-170)
		tmp = Float64(Float64(fma(t_2, k, Float64(t_5 * x)) - Float64(t_1 * t)) * y2);
	elseif (z <= 1.05e-104)
		tmp = Float64(Float64(fma(Float64(-y5), t_3, Float64(t_4 * c)) - Float64(t_6 * b)) * y0);
	elseif (z <= 2.45e+163)
		tmp = Float64(Float64(fma(Float64(-a), t_4, Float64(t_3 * y4)) + Float64(i * t_6)) * y1);
	elseif (z <= 2.7e+198)
		tmp = Float64(Float64(-i) * Float64(fma(fma(y, x, Float64(Float64(-t) * z)), c, Float64(fma(j, t, Float64(Float64(-k) * y)) * y5)) - Float64(t_6 * y1)));
	else
		tmp = Float64(Float64(-z) * Float64(fma(t_5, y3, Float64(fma(b, a, Float64(Float64(-c) * i)) * t)) - Float64(fma(y0, b, Float64(Float64(-i) * y1)) * k)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(y4 * c + N[((-a) * y5), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y4 * y1 + N[((-y0) * y5), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(y2 * k + N[((-j) * y3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(y2 * x + N[((-y3) * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(y0 * c + N[((-a) * y1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(j * x + N[((-k) * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2e+84], N[(N[(z * N[((-y0) * y3 + N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[z, -2.8e-51], N[((-y3) * N[(N[(t$95$2 * j + N[(t$95$5 * z), $MachinePrecision]), $MachinePrecision] - N[(t$95$1 * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -4.2e-170], N[(N[(N[(t$95$2 * k + N[(t$95$5 * x), $MachinePrecision]), $MachinePrecision] - N[(t$95$1 * t), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision], If[LessEqual[z, 1.05e-104], N[(N[(N[((-y5) * t$95$3 + N[(t$95$4 * c), $MachinePrecision]), $MachinePrecision] - N[(t$95$6 * b), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[z, 2.45e+163], N[(N[(N[((-a) * t$95$4 + N[(t$95$3 * y4), $MachinePrecision]), $MachinePrecision] + N[(i * t$95$6), $MachinePrecision]), $MachinePrecision] * y1), $MachinePrecision], If[LessEqual[z, 2.7e+198], N[((-i) * N[(N[(N[(y * x + N[((-t) * z), $MachinePrecision]), $MachinePrecision] * c + N[(N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision] - N[(t$95$6 * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[((-z) * N[(N[(t$95$5 * y3 + N[(N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision] - N[(N[(y0 * b + N[((-i) * y1), $MachinePrecision]), $MachinePrecision] * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]

Derivation

1. Split input into 7 regimes

2. if z < -2.00000000000000012e84

   1. Initial program 20.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 48.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in z around inf

      \[\leadsto \left(z \cdot \left(-1 \cdot \left(y0 \cdot y3\right) + i \cdot t\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 58.6%

      \[\leadsto \left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c \]

   if -2.00000000000000012e84 < z < -2.8e-51

   1. Initial program 42.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 62.2%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   if -2.8e-51 < z < -4.2000000000000001e-170

   1. Initial program 25.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 61.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   if -4.2000000000000001e-170 < z < 1.04999999999999999e-104

   1. Initial program 32.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 53.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   if 1.04999999999999999e-104 < z < 2.45e163

   1. Initial program 30.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y1 around inf

      \[\leadsto \color{blue}{y1 \cdot \left(\left(-1 \cdot \left(a \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) + y4 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - -1 \cdot \left(i \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(a \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) + y4 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - -1 \cdot \left(i \cdot \left(j \cdot x - k \cdot z\right)\right)\right) \cdot y1} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(a \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) + y4 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - -1 \cdot \left(i \cdot \left(j \cdot x - k \cdot z\right)\right)\right) \cdot y1} \]

   5. Applied rewrites 56.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-a, \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right), \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right) \cdot y4\right) - \left(-i\right) \cdot \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right)\right) \cdot y1} \]

   if 2.45e163 < z < 2.6999999999999999e198

   1. Initial program 25.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 83.3%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   if 2.6999999999999999e198 < z

   1. Initial program 15.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 84.6%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]
3. Recombined 7 regimes into one program.

4. Final simplification 59.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2 \cdot 10^{+84}:\\ \;\;\;\;\left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c\\ \mathbf{elif}\;z \leq -2.8 \cdot 10^{-51}:\\ \;\;\;\;\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-170}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2\\ \mathbf{elif}\;z \leq 1.05 \cdot 10^{-104}:\\ \;\;\;\;\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0\\ \mathbf{elif}\;z \leq 2.45 \cdot 10^{+163}:\\ \;\;\;\;\left(\mathsf{fma}\left(-a, \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right), \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right) \cdot y4\right) + i \cdot \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right)\right) \cdot y1\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{+198}:\\ \;\;\;\;\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 44.1% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(-a\right) \cdot y1\\ t_2 := \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, t\_1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \mathbf{if}\;x \leq -1.9 \cdot 10^{+111}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;x \leq -2.5 \cdot 10^{-191}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y1, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot b\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot c\right) \cdot y4\\ \mathbf{elif}\;x \leq 2.35 \cdot 10^{+72}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, t\_1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (- a) y1))
        (t_2
         (*
          (-
           (fma (fma y0 c t_1) y2 (* (fma b a (* (- c) i)) y))
           (* (fma y0 b (* (- i) y1)) j))
          x)))
   (if (<= x -1.9e+111)
     t_2
     (if (<= x -2.5e-191)
       (*
        (-
         (fma (fma y2 k (* (- j) y3)) y1 (* (fma j t (* (- k) y)) b))
         (* (fma y2 t (* (- y) y3)) c))
        y4)
       (if (<= x 2.35e+72)
         (* (- y3) (fma z (fma c y0 t_1) (* j (fma y1 y4 (* (- y0) y5)))))
         t_2)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = -a * y1;
	double t_2 = (fma(fma(y0, c, t_1), y2, (fma(b, a, (-c * i)) * y)) - (fma(y0, b, (-i * y1)) * j)) * x;
	double tmp;
	if (x <= -1.9e+111) {
		tmp = t_2;
	} else if (x <= -2.5e-191) {
		tmp = (fma(fma(y2, k, (-j * y3)), y1, (fma(j, t, (-k * y)) * b)) - (fma(y2, t, (-y * y3)) * c)) * y4;
	} else if (x <= 2.35e+72) {
		tmp = -y3 * fma(z, fma(c, y0, t_1), (j * fma(y1, y4, (-y0 * y5))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(-a) * y1)
	t_2 = Float64(Float64(fma(fma(y0, c, t_1), y2, Float64(fma(b, a, Float64(Float64(-c) * i)) * y)) - Float64(fma(y0, b, Float64(Float64(-i) * y1)) * j)) * x)
	tmp = 0.0
	if (x <= -1.9e+111)
		tmp = t_2;
	elseif (x <= -2.5e-191)
		tmp = Float64(Float64(fma(fma(y2, k, Float64(Float64(-j) * y3)), y1, Float64(fma(j, t, Float64(Float64(-k) * y)) * b)) - Float64(fma(y2, t, Float64(Float64(-y) * y3)) * c)) * y4);
	elseif (x <= 2.35e+72)
		tmp = Float64(Float64(-y3) * fma(z, fma(c, y0, t_1), Float64(j * fma(y1, y4, Float64(Float64(-y0) * y5)))));
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[((-a) * y1), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(y0 * c + t$95$1), $MachinePrecision] * y2 + N[(N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] - N[(N[(y0 * b + N[((-i) * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -1.9e+111], t$95$2, If[LessEqual[x, -2.5e-191], N[(N[(N[(N[(y2 * k + N[((-j) * y3), $MachinePrecision]), $MachinePrecision] * y1 + N[(N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision] - N[(N[(y2 * t + N[((-y) * y3), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision]), $MachinePrecision] * y4), $MachinePrecision], If[LessEqual[x, 2.35e+72], N[((-y3) * N[(z * N[(c * y0 + t$95$1), $MachinePrecision] + N[(j * N[(y1 * y4 + N[((-y0) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.89999999999999988e111 or 2.35000000000000017e72 < x

   1. Initial program 22.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 65.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   if -1.89999999999999988e111 < x < -2.5e-191

   1. Initial program 24.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y4 around inf

      \[\leadsto \color{blue}{y4 \cdot \left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot y4} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot y4} \]

   5. Applied rewrites 50.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y1, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot b\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot c\right) \cdot y4} \]

   if -2.5e-191 < x < 2.35000000000000017e72

   1. Initial program 35.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 42.0%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y around 0

      \[\leadsto \left(-y3\right) \cdot \left(j \cdot \left(-1 \cdot \left(y0 \cdot y5\right) + y1 \cdot y4\right) + \color{blue}{z \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 49.1%

      \[\leadsto \left(-y3\right) \cdot \mathsf{fma}\left(z, \color{blue}{\mathsf{fma}\left(c, y0, -a \cdot y1\right)}, j \cdot \mathsf{fma}\left(y1, y4, -y0 \cdot y5\right)\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 54.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.9 \cdot 10^{+111}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \mathbf{elif}\;x \leq -2.5 \cdot 10^{-191}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), y1, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot b\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot c\right) \cdot y4\\ \mathbf{elif}\;x \leq 2.35 \cdot 10^{+72}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, \left(-a\right) \cdot y1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 43.7% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(-a\right) \cdot y1\\ t_2 := \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, t\_1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \mathbf{if}\;x \leq -1.5 \cdot 10^{+60}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;x \leq -2.25:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{elif}\;x \leq 2.35 \cdot 10^{+72}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, t\_1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (- a) y1))
        (t_2
         (*
          (-
           (fma (fma y0 c t_1) y2 (* (fma b a (* (- c) i)) y))
           (* (fma y0 b (* (- i) y1)) j))
          x)))
   (if (<= x -1.5e+60)
     t_2
     (if (<= x -2.25)
       (*
        (-
         (fma (fma y x (* (- t) z)) a (* (fma j t (* (- k) y)) y4))
         (* (fma j x (* (- k) z)) y0))
        b)
       (if (<= x 2.35e+72)
         (* (- y3) (fma z (fma c y0 t_1) (* j (fma y1 y4 (* (- y0) y5)))))
         t_2)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = -a * y1;
	double t_2 = (fma(fma(y0, c, t_1), y2, (fma(b, a, (-c * i)) * y)) - (fma(y0, b, (-i * y1)) * j)) * x;
	double tmp;
	if (x <= -1.5e+60) {
		tmp = t_2;
	} else if (x <= -2.25) {
		tmp = (fma(fma(y, x, (-t * z)), a, (fma(j, t, (-k * y)) * y4)) - (fma(j, x, (-k * z)) * y0)) * b;
	} else if (x <= 2.35e+72) {
		tmp = -y3 * fma(z, fma(c, y0, t_1), (j * fma(y1, y4, (-y0 * y5))));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(-a) * y1)
	t_2 = Float64(Float64(fma(fma(y0, c, t_1), y2, Float64(fma(b, a, Float64(Float64(-c) * i)) * y)) - Float64(fma(y0, b, Float64(Float64(-i) * y1)) * j)) * x)
	tmp = 0.0
	if (x <= -1.5e+60)
		tmp = t_2;
	elseif (x <= -2.25)
		tmp = Float64(Float64(fma(fma(y, x, Float64(Float64(-t) * z)), a, Float64(fma(j, t, Float64(Float64(-k) * y)) * y4)) - Float64(fma(j, x, Float64(Float64(-k) * z)) * y0)) * b);
	elseif (x <= 2.35e+72)
		tmp = Float64(Float64(-y3) * fma(z, fma(c, y0, t_1), Float64(j * fma(y1, y4, Float64(Float64(-y0) * y5)))));
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[((-a) * y1), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(y0 * c + t$95$1), $MachinePrecision] * y2 + N[(N[(b * a + N[((-c) * i), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] - N[(N[(y0 * b + N[((-i) * y1), $MachinePrecision]), $MachinePrecision] * j), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -1.5e+60], t$95$2, If[LessEqual[x, -2.25], N[(N[(N[(N[(y * x + N[((-t) * z), $MachinePrecision]), $MachinePrecision] * a + N[(N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision] * y4), $MachinePrecision]), $MachinePrecision] - N[(N[(j * x + N[((-k) * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], If[LessEqual[x, 2.35e+72], N[((-y3) * N[(z * N[(c * y0 + t$95$1), $MachinePrecision] + N[(j * N[(y1 * y4 + N[((-y0) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.4999999999999999e60 or 2.35000000000000017e72 < x

   1. Initial program 23.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(y \cdot \left(a \cdot b - c \cdot i\right) + y2 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - j \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \cdot x} \]

   5. Applied rewrites 60.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x} \]

   if -1.4999999999999999e60 < x < -2.25

   1. Initial program 0.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 80.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   if -2.25 < x < 2.35000000000000017e72

   1. Initial program 33.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 44.0%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y around 0

      \[\leadsto \left(-y3\right) \cdot \left(j \cdot \left(-1 \cdot \left(y0 \cdot y5\right) + y1 \cdot y4\right) + \color{blue}{z \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 48.8%

      \[\leadsto \left(-y3\right) \cdot \mathsf{fma}\left(z, \color{blue}{\mathsf{fma}\left(c, y0, -a \cdot y1\right)}, j \cdot \mathsf{fma}\left(y1, y4, -y0 \cdot y5\right)\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 54.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.5 \cdot 10^{+60}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \mathbf{elif}\;x \leq -2.25:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{elif}\;x \leq 2.35 \cdot 10^{+72}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, \left(-a\right) \cdot y1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y2, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot y\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot j\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 38.2% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.6 \cdot 10^{+55}:\\ \;\;\;\;\left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2\\ \mathbf{elif}\;x \leq -2.25:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+91}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, \left(-a\right) \cdot y1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{+140}:\\ \;\;\;\;i \cdot \left(y \cdot \mathsf{fma}\left(-1, c \cdot x, k \cdot y5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-i\right) \cdot \left(x \cdot \mathsf{fma}\left(c, y, \left(-j\right) \cdot y1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= x -5.6e+55)
   (* (* y0 (* c (fma -1.0 (/ (* k y5) c) x))) y2)
   (if (<= x -2.25)
     (*
      (-
       (fma (fma y x (* (- t) z)) a (* (fma j t (* (- k) y)) y4))
       (* (fma j x (* (- k) z)) y0))
      b)
     (if (<= x 5e+91)
       (*
        (- y3)
        (fma z (fma c y0 (* (- a) y1)) (* j (fma y1 y4 (* (- y0) y5)))))
       (if (<= x 6.4e+140)
         (* i (* y (fma -1.0 (* c x) (* k y5))))
         (* (- i) (* x (fma c y (* (- j) y1)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (x <= -5.6e+55) {
		tmp = (y0 * (c * fma(-1.0, ((k * y5) / c), x))) * y2;
	} else if (x <= -2.25) {
		tmp = (fma(fma(y, x, (-t * z)), a, (fma(j, t, (-k * y)) * y4)) - (fma(j, x, (-k * z)) * y0)) * b;
	} else if (x <= 5e+91) {
		tmp = -y3 * fma(z, fma(c, y0, (-a * y1)), (j * fma(y1, y4, (-y0 * y5))));
	} else if (x <= 6.4e+140) {
		tmp = i * (y * fma(-1.0, (c * x), (k * y5)));
	} else {
		tmp = -i * (x * fma(c, y, (-j * y1)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (x <= -5.6e+55)
		tmp = Float64(Float64(y0 * Float64(c * fma(-1.0, Float64(Float64(k * y5) / c), x))) * y2);
	elseif (x <= -2.25)
		tmp = Float64(Float64(fma(fma(y, x, Float64(Float64(-t) * z)), a, Float64(fma(j, t, Float64(Float64(-k) * y)) * y4)) - Float64(fma(j, x, Float64(Float64(-k) * z)) * y0)) * b);
	elseif (x <= 5e+91)
		tmp = Float64(Float64(-y3) * fma(z, fma(c, y0, Float64(Float64(-a) * y1)), Float64(j * fma(y1, y4, Float64(Float64(-y0) * y5)))));
	elseif (x <= 6.4e+140)
		tmp = Float64(i * Float64(y * fma(-1.0, Float64(c * x), Float64(k * y5))));
	else
		tmp = Float64(Float64(-i) * Float64(x * fma(c, y, Float64(Float64(-j) * y1))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[x, -5.6e+55], N[(N[(y0 * N[(c * N[(-1.0 * N[(N[(k * y5), $MachinePrecision] / c), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision], If[LessEqual[x, -2.25], N[(N[(N[(N[(y * x + N[((-t) * z), $MachinePrecision]), $MachinePrecision] * a + N[(N[(j * t + N[((-k) * y), $MachinePrecision]), $MachinePrecision] * y4), $MachinePrecision]), $MachinePrecision] - N[(N[(j * x + N[((-k) * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], If[LessEqual[x, 5e+91], N[((-y3) * N[(z * N[(c * y0 + N[((-a) * y1), $MachinePrecision]), $MachinePrecision] + N[(j * N[(y1 * y4 + N[((-y0) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 6.4e+140], N[(i * N[(y * N[(-1.0 * N[(c * x), $MachinePrecision] + N[(k * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[((-i) * N[(x * N[(c * y + N[((-j) * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if x < -5.6000000000000002e55

   1. Initial program 24.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 49.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y0 around inf

      \[\leadsto \left(y0 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 40.9%

      \[\leadsto \left(y0 \cdot \mathsf{fma}\left(-1, k \cdot y5, c \cdot x\right)\right) \cdot y2 \]

   9. Taylor expanded in c around inf

      \[\leadsto \left(y0 \cdot \left(c \cdot \left(x + -1 \cdot \frac{k \cdot y5}{c}\right)\right)\right) \cdot y2 \]
   10. Step-by-step derivation

   11. Applied rewrites 44.6%

       \[\leadsto \left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2 \]

   if -5.6000000000000002e55 < x < -2.25

   1. Initial program 0.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 80.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   if -2.25 < x < 5.0000000000000002e91

   1. Initial program 33.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 44.0%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y around 0

      \[\leadsto \left(-y3\right) \cdot \left(j \cdot \left(-1 \cdot \left(y0 \cdot y5\right) + y1 \cdot y4\right) + \color{blue}{z \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 48.8%

      \[\leadsto \left(-y3\right) \cdot \mathsf{fma}\left(z, \color{blue}{\mathsf{fma}\left(c, y0, -a \cdot y1\right)}, j \cdot \mathsf{fma}\left(y1, y4, -y0 \cdot y5\right)\right) \]

   if 5.0000000000000002e91 < x < 6.40000000000000021e140

   1. Initial program 33.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 66.7%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto i \cdot \color{blue}{\left(y \cdot \left(-1 \cdot \left(c \cdot x\right) + k \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 91.8%

      \[\leadsto i \cdot \color{blue}{\left(y \cdot \mathsf{fma}\left(-1, c \cdot x, k \cdot y5\right)\right)} \]

   if 6.40000000000000021e140 < x

   1. Initial program 18.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 48.5%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(-i\right) \cdot \left(x \cdot \color{blue}{\left(c \cdot y - j \cdot y1\right)}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 61.1%

      \[\leadsto \left(-i\right) \cdot \left(x \cdot \color{blue}{\mathsf{fma}\left(c, y, \left(-j\right) \cdot y1\right)}\right) \]
3. Recombined 5 regimes into one program.

4. Final simplification 52.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5.6 \cdot 10^{+55}:\\ \;\;\;\;\left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2\\ \mathbf{elif}\;x \leq -2.25:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+91}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, \left(-a\right) \cdot y1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{+140}:\\ \;\;\;\;i \cdot \left(y \cdot \mathsf{fma}\left(-1, c \cdot x, k \cdot y5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-i\right) \cdot \left(x \cdot \mathsf{fma}\left(c, y, \left(-j\right) \cdot y1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 31.1% accurate, 3.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{if}\;a \leq -1.55 \cdot 10^{+67}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -2.7 \cdot 10^{-52}:\\ \;\;\;\;\left(\left(-k\right) \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b\\ \mathbf{elif}\;a \leq -4.4 \cdot 10^{-184}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-203}:\\ \;\;\;\;\left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-118}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y4 (fma k y1 (* (- c) t))) y2)))
   (if (<= a -1.55e+67)
     (* (* y1 z) (fma a y3 (* (- i) k)))
     (if (<= a -2.7e-52)
       (* (* (- k) (fma y y4 (* (- y0) z))) b)
       (if (<= a -4.4e-184)
         t_1
         (if (<= a 1.85e-203)
           (* (* y0 (* c (fma -1.0 (/ (* k y5) c) x))) y2)
           (if (<= a 3.25e-118)
             t_1
             (if (<= a 1.02e+128)
               (* (* j (fma t y4 (* (- x) y0))) b)
               (* (* a y3) (fma y1 z (* (- y) y5)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * fma(k, y1, (-c * t))) * y2;
	double tmp;
	if (a <= -1.55e+67) {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	} else if (a <= -2.7e-52) {
		tmp = (-k * fma(y, y4, (-y0 * z))) * b;
	} else if (a <= -4.4e-184) {
		tmp = t_1;
	} else if (a <= 1.85e-203) {
		tmp = (y0 * (c * fma(-1.0, ((k * y5) / c), x))) * y2;
	} else if (a <= 3.25e-118) {
		tmp = t_1;
	} else if (a <= 1.02e+128) {
		tmp = (j * fma(t, y4, (-x * y0))) * b;
	} else {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y4 * fma(k, y1, Float64(Float64(-c) * t))) * y2)
	tmp = 0.0
	if (a <= -1.55e+67)
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	elseif (a <= -2.7e-52)
		tmp = Float64(Float64(Float64(-k) * fma(y, y4, Float64(Float64(-y0) * z))) * b);
	elseif (a <= -4.4e-184)
		tmp = t_1;
	elseif (a <= 1.85e-203)
		tmp = Float64(Float64(y0 * Float64(c * fma(-1.0, Float64(Float64(k * y5) / c), x))) * y2);
	elseif (a <= 3.25e-118)
		tmp = t_1;
	elseif (a <= 1.02e+128)
		tmp = Float64(Float64(j * fma(t, y4, Float64(Float64(-x) * y0))) * b);
	else
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y4 * N[(k * y1 + N[((-c) * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision]}, If[LessEqual[a, -1.55e+67], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -2.7e-52], N[(N[((-k) * N[(y * y4 + N[((-y0) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], If[LessEqual[a, -4.4e-184], t$95$1, If[LessEqual[a, 1.85e-203], N[(N[(y0 * N[(c * N[(-1.0 * N[(N[(k * y5), $MachinePrecision] / c), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision], If[LessEqual[a, 3.25e-118], t$95$1, If[LessEqual[a, 1.02e+128], N[(N[(j * N[(t * y4 + N[((-x) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if a < -1.54999999999999998e67

   1. Initial program 24.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 35.1%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 46.4%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]

   if -1.54999999999999998e67 < a < -2.70000000000000009e-52

   1. Initial program 14.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 52.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in k around -inf

      \[\leadsto \left(-1 \cdot \left(k \cdot \left(y \cdot y4 - y0 \cdot z\right)\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 58.0%

      \[\leadsto \left(-k \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b \]

   if -2.70000000000000009e-52 < a < -4.39999999999999984e-184 or 1.85000000000000001e-203 < a < 3.24999999999999979e-118

   1. Initial program 35.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 40.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y4 around inf

      \[\leadsto \left(y4 \cdot \left(k \cdot y1 - c \cdot t\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 56.2%

      \[\leadsto \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2 \]

   if -4.39999999999999984e-184 < a < 1.85000000000000001e-203

   1. Initial program 34.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 41.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y0 around inf

      \[\leadsto \left(y0 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 44.7%

      \[\leadsto \left(y0 \cdot \mathsf{fma}\left(-1, k \cdot y5, c \cdot x\right)\right) \cdot y2 \]

   9. Taylor expanded in c around inf

      \[\leadsto \left(y0 \cdot \left(c \cdot \left(x + -1 \cdot \frac{k \cdot y5}{c}\right)\right)\right) \cdot y2 \]
   10. Step-by-step derivation

   11. Applied rewrites 44.7%

       \[\leadsto \left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2 \]

   if 3.24999999999999979e-118 < a < 1.02000000000000008e128

   1. Initial program 23.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 54.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(t \cdot y4 - x \cdot y0\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 59.3%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b \]

   if 1.02000000000000008e128 < a

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 53.8%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 54.3%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]
3. Recombined 6 regimes into one program.

4. Final simplification 52.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -1.55 \cdot 10^{+67}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -2.7 \cdot 10^{-52}:\\ \;\;\;\;\left(\left(-k\right) \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b\\ \mathbf{elif}\;a \leq -4.4 \cdot 10^{-184}:\\ \;\;\;\;\left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-203}:\\ \;\;\;\;\left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-118}:\\ \;\;\;\;\left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 30.7% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{if}\;a \leq -1.55 \cdot 10^{+67}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -2.7 \cdot 10^{-52}:\\ \;\;\;\;\left(\left(-k\right) \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b\\ \mathbf{elif}\;a \leq -1.75 \cdot 10^{-186}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-203}:\\ \;\;\;\;\left(y0 \cdot \mathsf{fma}\left(-1, k \cdot y5, c \cdot x\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-118}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y4 (fma k y1 (* (- c) t))) y2)))
   (if (<= a -1.55e+67)
     (* (* y1 z) (fma a y3 (* (- i) k)))
     (if (<= a -2.7e-52)
       (* (* (- k) (fma y y4 (* (- y0) z))) b)
       (if (<= a -1.75e-186)
         t_1
         (if (<= a 1.85e-203)
           (* (* y0 (fma -1.0 (* k y5) (* c x))) y2)
           (if (<= a 3.25e-118)
             t_1
             (if (<= a 1.02e+128)
               (* (* j (fma t y4 (* (- x) y0))) b)
               (* (* a y3) (fma y1 z (* (- y) y5)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * fma(k, y1, (-c * t))) * y2;
	double tmp;
	if (a <= -1.55e+67) {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	} else if (a <= -2.7e-52) {
		tmp = (-k * fma(y, y4, (-y0 * z))) * b;
	} else if (a <= -1.75e-186) {
		tmp = t_1;
	} else if (a <= 1.85e-203) {
		tmp = (y0 * fma(-1.0, (k * y5), (c * x))) * y2;
	} else if (a <= 3.25e-118) {
		tmp = t_1;
	} else if (a <= 1.02e+128) {
		tmp = (j * fma(t, y4, (-x * y0))) * b;
	} else {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y4 * fma(k, y1, Float64(Float64(-c) * t))) * y2)
	tmp = 0.0
	if (a <= -1.55e+67)
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	elseif (a <= -2.7e-52)
		tmp = Float64(Float64(Float64(-k) * fma(y, y4, Float64(Float64(-y0) * z))) * b);
	elseif (a <= -1.75e-186)
		tmp = t_1;
	elseif (a <= 1.85e-203)
		tmp = Float64(Float64(y0 * fma(-1.0, Float64(k * y5), Float64(c * x))) * y2);
	elseif (a <= 3.25e-118)
		tmp = t_1;
	elseif (a <= 1.02e+128)
		tmp = Float64(Float64(j * fma(t, y4, Float64(Float64(-x) * y0))) * b);
	else
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y4 * N[(k * y1 + N[((-c) * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision]}, If[LessEqual[a, -1.55e+67], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -2.7e-52], N[(N[((-k) * N[(y * y4 + N[((-y0) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], If[LessEqual[a, -1.75e-186], t$95$1, If[LessEqual[a, 1.85e-203], N[(N[(y0 * N[(-1.0 * N[(k * y5), $MachinePrecision] + N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision], If[LessEqual[a, 3.25e-118], t$95$1, If[LessEqual[a, 1.02e+128], N[(N[(j * N[(t * y4 + N[((-x) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if a < -1.54999999999999998e67

   1. Initial program 24.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 35.1%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 46.4%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]

   if -1.54999999999999998e67 < a < -2.70000000000000009e-52

   1. Initial program 14.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 52.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in k around -inf

      \[\leadsto \left(-1 \cdot \left(k \cdot \left(y \cdot y4 - y0 \cdot z\right)\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 58.0%

      \[\leadsto \left(-k \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b \]

   if -2.70000000000000009e-52 < a < -1.74999999999999995e-186 or 1.85000000000000001e-203 < a < 3.24999999999999979e-118

   1. Initial program 35.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 40.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y4 around inf

      \[\leadsto \left(y4 \cdot \left(k \cdot y1 - c \cdot t\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 56.2%

      \[\leadsto \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2 \]

   if -1.74999999999999995e-186 < a < 1.85000000000000001e-203

   1. Initial program 34.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 41.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y0 around inf

      \[\leadsto \left(y0 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 44.7%

      \[\leadsto \left(y0 \cdot \mathsf{fma}\left(-1, k \cdot y5, c \cdot x\right)\right) \cdot y2 \]

   if 3.24999999999999979e-118 < a < 1.02000000000000008e128

   1. Initial program 23.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 54.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(t \cdot y4 - x \cdot y0\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 59.3%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b \]

   if 1.02000000000000008e128 < a

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 53.8%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 54.3%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]
3. Recombined 6 regimes into one program.

4. Final simplification 52.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -1.55 \cdot 10^{+67}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -2.7 \cdot 10^{-52}:\\ \;\;\;\;\left(\left(-k\right) \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b\\ \mathbf{elif}\;a \leq -1.75 \cdot 10^{-186}:\\ \;\;\;\;\left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-203}:\\ \;\;\;\;\left(y0 \cdot \mathsf{fma}\left(-1, k \cdot y5, c \cdot x\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-118}:\\ \;\;\;\;\left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 30.8% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{if}\;a \leq -1.55 \cdot 10^{+67}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -2.7 \cdot 10^{-52}:\\ \;\;\;\;\left(\left(-k\right) \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b\\ \mathbf{elif}\;a \leq -1.75 \cdot 10^{-186}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-203}:\\ \;\;\;\;\left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-118}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y4 (fma k y1 (* (- c) t))) y2)))
   (if (<= a -1.55e+67)
     (* (* y1 z) (fma a y3 (* (- i) k)))
     (if (<= a -2.7e-52)
       (* (* (- k) (fma y y4 (* (- y0) z))) b)
       (if (<= a -1.75e-186)
         t_1
         (if (<= a 1.85e-203)
           (* (* y2 (fma (- k) y5 (* c x))) y0)
           (if (<= a 3.25e-118)
             t_1
             (if (<= a 1.02e+128)
               (* (* j (fma t y4 (* (- x) y0))) b)
               (* (* a y3) (fma y1 z (* (- y) y5)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * fma(k, y1, (-c * t))) * y2;
	double tmp;
	if (a <= -1.55e+67) {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	} else if (a <= -2.7e-52) {
		tmp = (-k * fma(y, y4, (-y0 * z))) * b;
	} else if (a <= -1.75e-186) {
		tmp = t_1;
	} else if (a <= 1.85e-203) {
		tmp = (y2 * fma(-k, y5, (c * x))) * y0;
	} else if (a <= 3.25e-118) {
		tmp = t_1;
	} else if (a <= 1.02e+128) {
		tmp = (j * fma(t, y4, (-x * y0))) * b;
	} else {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y4 * fma(k, y1, Float64(Float64(-c) * t))) * y2)
	tmp = 0.0
	if (a <= -1.55e+67)
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	elseif (a <= -2.7e-52)
		tmp = Float64(Float64(Float64(-k) * fma(y, y4, Float64(Float64(-y0) * z))) * b);
	elseif (a <= -1.75e-186)
		tmp = t_1;
	elseif (a <= 1.85e-203)
		tmp = Float64(Float64(y2 * fma(Float64(-k), y5, Float64(c * x))) * y0);
	elseif (a <= 3.25e-118)
		tmp = t_1;
	elseif (a <= 1.02e+128)
		tmp = Float64(Float64(j * fma(t, y4, Float64(Float64(-x) * y0))) * b);
	else
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y4 * N[(k * y1 + N[((-c) * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision]}, If[LessEqual[a, -1.55e+67], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -2.7e-52], N[(N[((-k) * N[(y * y4 + N[((-y0) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], If[LessEqual[a, -1.75e-186], t$95$1, If[LessEqual[a, 1.85e-203], N[(N[(y2 * N[((-k) * y5 + N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[a, 3.25e-118], t$95$1, If[LessEqual[a, 1.02e+128], N[(N[(j * N[(t * y4 + N[((-x) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if a < -1.54999999999999998e67

   1. Initial program 24.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 35.1%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 46.4%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]

   if -1.54999999999999998e67 < a < -2.70000000000000009e-52

   1. Initial program 14.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 52.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in k around -inf

      \[\leadsto \left(-1 \cdot \left(k \cdot \left(y \cdot y4 - y0 \cdot z\right)\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 58.0%

      \[\leadsto \left(-k \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b \]

   if -2.70000000000000009e-52 < a < -1.74999999999999995e-186 or 1.85000000000000001e-203 < a < 3.24999999999999979e-118

   1. Initial program 35.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 40.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y4 around inf

      \[\leadsto \left(y4 \cdot \left(k \cdot y1 - c \cdot t\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 56.2%

      \[\leadsto \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2 \]

   if -1.74999999999999995e-186 < a < 1.85000000000000001e-203

   1. Initial program 34.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 23.0%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in y2 around inf

      \[\leadsto \left(y2 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 44.7%

       \[\leadsto \left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0 \]

   if 3.24999999999999979e-118 < a < 1.02000000000000008e128

   1. Initial program 23.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 54.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(t \cdot y4 - x \cdot y0\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 59.3%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b \]

   if 1.02000000000000008e128 < a

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 53.8%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 54.3%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]
3. Recombined 6 regimes into one program.

4. Final simplification 52.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -1.55 \cdot 10^{+67}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -2.7 \cdot 10^{-52}:\\ \;\;\;\;\left(\left(-k\right) \cdot \mathsf{fma}\left(y, y4, \left(-y0\right) \cdot z\right)\right) \cdot b\\ \mathbf{elif}\;a \leq -1.75 \cdot 10^{-186}:\\ \;\;\;\;\left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-203}:\\ \;\;\;\;\left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-118}:\\ \;\;\;\;\left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 30.9% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{if}\;a \leq -4 \cdot 10^{+103}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -7200:\\ \;\;\;\;\left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0\\ \mathbf{elif}\;a \leq -1.75 \cdot 10^{-186}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-203}:\\ \;\;\;\;\left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-118}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y4 (fma k y1 (* (- c) t))) y2)))
   (if (<= a -4e+103)
     (* (* y1 z) (fma a y3 (* (- i) k)))
     (if (<= a -7200.0)
       (* (* (- z) (fma c y3 (* (- b) k))) y0)
       (if (<= a -1.75e-186)
         t_1
         (if (<= a 1.85e-203)
           (* (* y2 (fma (- k) y5 (* c x))) y0)
           (if (<= a 3.25e-118)
             t_1
             (if (<= a 1.02e+128)
               (* (* j (fma t y4 (* (- x) y0))) b)
               (* (* a y3) (fma y1 z (* (- y) y5)))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * fma(k, y1, (-c * t))) * y2;
	double tmp;
	if (a <= -4e+103) {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	} else if (a <= -7200.0) {
		tmp = (-z * fma(c, y3, (-b * k))) * y0;
	} else if (a <= -1.75e-186) {
		tmp = t_1;
	} else if (a <= 1.85e-203) {
		tmp = (y2 * fma(-k, y5, (c * x))) * y0;
	} else if (a <= 3.25e-118) {
		tmp = t_1;
	} else if (a <= 1.02e+128) {
		tmp = (j * fma(t, y4, (-x * y0))) * b;
	} else {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y4 * fma(k, y1, Float64(Float64(-c) * t))) * y2)
	tmp = 0.0
	if (a <= -4e+103)
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	elseif (a <= -7200.0)
		tmp = Float64(Float64(Float64(-z) * fma(c, y3, Float64(Float64(-b) * k))) * y0);
	elseif (a <= -1.75e-186)
		tmp = t_1;
	elseif (a <= 1.85e-203)
		tmp = Float64(Float64(y2 * fma(Float64(-k), y5, Float64(c * x))) * y0);
	elseif (a <= 3.25e-118)
		tmp = t_1;
	elseif (a <= 1.02e+128)
		tmp = Float64(Float64(j * fma(t, y4, Float64(Float64(-x) * y0))) * b);
	else
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y4 * N[(k * y1 + N[((-c) * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision]}, If[LessEqual[a, -4e+103], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -7200.0], N[(N[((-z) * N[(c * y3 + N[((-b) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[a, -1.75e-186], t$95$1, If[LessEqual[a, 1.85e-203], N[(N[(y2 * N[((-k) * y5 + N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[a, 3.25e-118], t$95$1, If[LessEqual[a, 1.02e+128], N[(N[(j * N[(t * y4 + N[((-x) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if a < -4e103

   1. Initial program 16.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 32.6%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 49.2%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]

   if -4e103 < a < -7200

   1. Initial program 32.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 48.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 48.9%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   if -7200 < a < -1.74999999999999995e-186 or 1.85000000000000001e-203 < a < 3.24999999999999979e-118

   1. Initial program 32.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 38.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y4 around inf

      \[\leadsto \left(y4 \cdot \left(k \cdot y1 - c \cdot t\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 54.2%

      \[\leadsto \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2 \]

   if -1.74999999999999995e-186 < a < 1.85000000000000001e-203

   1. Initial program 34.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 23.0%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in y2 around inf

      \[\leadsto \left(y2 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 44.7%

       \[\leadsto \left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0 \]

   if 3.24999999999999979e-118 < a < 1.02000000000000008e128

   1. Initial program 23.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 54.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(t \cdot y4 - x \cdot y0\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 59.3%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b \]

   if 1.02000000000000008e128 < a

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 53.8%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 54.3%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]
3. Recombined 6 regimes into one program.
4. Add Preprocessing

Alternative 11: 37.9% accurate, 3.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.6 \cdot 10^{+108}:\\ \;\;\;\;\left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+91}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, \left(-a\right) \cdot y1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{+140}:\\ \;\;\;\;i \cdot \left(y \cdot \mathsf{fma}\left(-1, c \cdot x, k \cdot y5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-i\right) \cdot \left(x \cdot \mathsf{fma}\left(c, y, \left(-j\right) \cdot y1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= x -4.6e+108)
   (* (* y0 (* c (fma -1.0 (/ (* k y5) c) x))) y2)
   (if (<= x 5e+91)
     (* (- y3) (fma z (fma c y0 (* (- a) y1)) (* j (fma y1 y4 (* (- y0) y5)))))
     (if (<= x 6.4e+140)
       (* i (* y (fma -1.0 (* c x) (* k y5))))
       (* (- i) (* x (fma c y (* (- j) y1))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (x <= -4.6e+108) {
		tmp = (y0 * (c * fma(-1.0, ((k * y5) / c), x))) * y2;
	} else if (x <= 5e+91) {
		tmp = -y3 * fma(z, fma(c, y0, (-a * y1)), (j * fma(y1, y4, (-y0 * y5))));
	} else if (x <= 6.4e+140) {
		tmp = i * (y * fma(-1.0, (c * x), (k * y5)));
	} else {
		tmp = -i * (x * fma(c, y, (-j * y1)));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (x <= -4.6e+108)
		tmp = Float64(Float64(y0 * Float64(c * fma(-1.0, Float64(Float64(k * y5) / c), x))) * y2);
	elseif (x <= 5e+91)
		tmp = Float64(Float64(-y3) * fma(z, fma(c, y0, Float64(Float64(-a) * y1)), Float64(j * fma(y1, y4, Float64(Float64(-y0) * y5)))));
	elseif (x <= 6.4e+140)
		tmp = Float64(i * Float64(y * fma(-1.0, Float64(c * x), Float64(k * y5))));
	else
		tmp = Float64(Float64(-i) * Float64(x * fma(c, y, Float64(Float64(-j) * y1))));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[x, -4.6e+108], N[(N[(y0 * N[(c * N[(-1.0 * N[(N[(k * y5), $MachinePrecision] / c), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision], If[LessEqual[x, 5e+91], N[((-y3) * N[(z * N[(c * y0 + N[((-a) * y1), $MachinePrecision]), $MachinePrecision] + N[(j * N[(y1 * y4 + N[((-y0) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 6.4e+140], N[(i * N[(y * N[(-1.0 * N[(c * x), $MachinePrecision] + N[(k * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[((-i) * N[(x * N[(c * y + N[((-j) * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if x < -4.5999999999999998e108

   1. Initial program 22.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 50.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y0 around inf

      \[\leadsto \left(y0 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 44.3%

      \[\leadsto \left(y0 \cdot \mathsf{fma}\left(-1, k \cdot y5, c \cdot x\right)\right) \cdot y2 \]

   9. Taylor expanded in c around inf

      \[\leadsto \left(y0 \cdot \left(c \cdot \left(x + -1 \cdot \frac{k \cdot y5}{c}\right)\right)\right) \cdot y2 \]
   10. Step-by-step derivation

   11. Applied rewrites 48.7%

       \[\leadsto \left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2 \]

   if -4.5999999999999998e108 < x < 5.0000000000000002e91

   1. Initial program 31.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 43.2%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y around 0

      \[\leadsto \left(-y3\right) \cdot \left(j \cdot \left(-1 \cdot \left(y0 \cdot y5\right) + y1 \cdot y4\right) + \color{blue}{z \cdot \left(-1 \cdot \left(a \cdot y1\right) + c \cdot y0\right)}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 46.9%

      \[\leadsto \left(-y3\right) \cdot \mathsf{fma}\left(z, \color{blue}{\mathsf{fma}\left(c, y0, -a \cdot y1\right)}, j \cdot \mathsf{fma}\left(y1, y4, -y0 \cdot y5\right)\right) \]

   if 5.0000000000000002e91 < x < 6.40000000000000021e140

   1. Initial program 33.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 66.7%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in y around -inf

      \[\leadsto i \cdot \color{blue}{\left(y \cdot \left(-1 \cdot \left(c \cdot x\right) + k \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 91.8%

      \[\leadsto i \cdot \color{blue}{\left(y \cdot \mathsf{fma}\left(-1, c \cdot x, k \cdot y5\right)\right)} \]

   if 6.40000000000000021e140 < x

   1. Initial program 18.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 48.5%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(-i\right) \cdot \left(x \cdot \color{blue}{\left(c \cdot y - j \cdot y1\right)}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 61.1%

      \[\leadsto \left(-i\right) \cdot \left(x \cdot \color{blue}{\mathsf{fma}\left(c, y, \left(-j\right) \cdot y1\right)}\right) \]
3. Recombined 4 regimes into one program.

4. Final simplification 51.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.6 \cdot 10^{+108}:\\ \;\;\;\;\left(y0 \cdot \left(c \cdot \mathsf{fma}\left(-1, \frac{k \cdot y5}{c}, x\right)\right)\right) \cdot y2\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+91}:\\ \;\;\;\;\left(-y3\right) \cdot \mathsf{fma}\left(z, \mathsf{fma}\left(c, y0, \left(-a\right) \cdot y1\right), j \cdot \mathsf{fma}\left(y1, y4, \left(-y0\right) \cdot y5\right)\right)\\ \mathbf{elif}\;x \leq 6.4 \cdot 10^{+140}:\\ \;\;\;\;i \cdot \left(y \cdot \mathsf{fma}\left(-1, c \cdot x, k \cdot y5\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-i\right) \cdot \left(x \cdot \mathsf{fma}\left(c, y, \left(-j\right) \cdot y1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 30.9% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2\\ \mathbf{if}\;a \leq -1 \cdot 10^{+65}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -1.75 \cdot 10^{-186}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.85 \cdot 10^{-203}:\\ \;\;\;\;\left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-118}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y4 (fma k y1 (* (- c) t))) y2)))
   (if (<= a -1e+65)
     (* (* y1 z) (fma a y3 (* (- i) k)))
     (if (<= a -1.75e-186)
       t_1
       (if (<= a 1.85e-203)
         (* (* y2 (fma (- k) y5 (* c x))) y0)
         (if (<= a 3.25e-118)
           t_1
           (if (<= a 1.02e+128)
             (* (* j (fma t y4 (* (- x) y0))) b)
             (* (* a y3) (fma y1 z (* (- y) y5))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * fma(k, y1, (-c * t))) * y2;
	double tmp;
	if (a <= -1e+65) {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	} else if (a <= -1.75e-186) {
		tmp = t_1;
	} else if (a <= 1.85e-203) {
		tmp = (y2 * fma(-k, y5, (c * x))) * y0;
	} else if (a <= 3.25e-118) {
		tmp = t_1;
	} else if (a <= 1.02e+128) {
		tmp = (j * fma(t, y4, (-x * y0))) * b;
	} else {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y4 * fma(k, y1, Float64(Float64(-c) * t))) * y2)
	tmp = 0.0
	if (a <= -1e+65)
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	elseif (a <= -1.75e-186)
		tmp = t_1;
	elseif (a <= 1.85e-203)
		tmp = Float64(Float64(y2 * fma(Float64(-k), y5, Float64(c * x))) * y0);
	elseif (a <= 3.25e-118)
		tmp = t_1;
	elseif (a <= 1.02e+128)
		tmp = Float64(Float64(j * fma(t, y4, Float64(Float64(-x) * y0))) * b);
	else
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y4 * N[(k * y1 + N[((-c) * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y2), $MachinePrecision]}, If[LessEqual[a, -1e+65], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -1.75e-186], t$95$1, If[LessEqual[a, 1.85e-203], N[(N[(y2 * N[((-k) * y5 + N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[a, 3.25e-118], t$95$1, If[LessEqual[a, 1.02e+128], N[(N[(j * N[(t * y4 + N[((-x) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

1. Split input into 5 regimes

2. if a < -9.9999999999999999e64

   1. Initial program 23.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 34.3%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 45.4%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]

   if -9.9999999999999999e64 < a < -1.74999999999999995e-186 or 1.85000000000000001e-203 < a < 3.24999999999999979e-118

   1. Initial program 29.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 37.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y4 around inf

      \[\leadsto \left(y4 \cdot \left(k \cdot y1 - c \cdot t\right)\right) \cdot y2 \]
   7. Step-by-step derivation

   8. Applied rewrites 49.9%

      \[\leadsto \left(y4 \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\right) \cdot y2 \]

   if -1.74999999999999995e-186 < a < 1.85000000000000001e-203

   1. Initial program 34.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 40.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 23.0%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in y2 around inf

      \[\leadsto \left(y2 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 44.7%

       \[\leadsto \left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0 \]

   if 3.24999999999999979e-118 < a < 1.02000000000000008e128

   1. Initial program 23.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 54.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(t \cdot y4 - x \cdot y0\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 59.3%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b \]

   if 1.02000000000000008e128 < a

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 53.8%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 54.3%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 13: 30.4% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -9.5 \cdot 10^{+64}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -1 \cdot 10^{-153}:\\ \;\;\;\;\left(y2 \cdot y4\right) \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\\ \mathbf{elif}\;a \leq 4.5 \cdot 10^{-206}:\\ \;\;\;\;\left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0\\ \mathbf{elif}\;a \leq 9 \cdot 10^{-117}:\\ \;\;\;\;\left(t \cdot \mathsf{fma}\left(i, z, \left(-y2\right) \cdot y4\right)\right) \cdot c\\ \mathbf{elif}\;a \leq 1.02 \cdot 10^{+128}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= a -9.5e+64)
   (* (* y1 z) (fma a y3 (* (- i) k)))
   (if (<= a -1e-153)
     (* (* y2 y4) (fma k y1 (* (- c) t)))
     (if (<= a 4.5e-206)
       (* (* y2 (fma (- k) y5 (* c x))) y0)
       (if (<= a 9e-117)
         (* (* t (fma i z (* (- y2) y4))) c)
         (if (<= a 1.02e+128)
           (* (* j (fma t y4 (* (- x) y0))) b)
           (* (* a y3) (fma y1 z (* (- y) y5)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (a <= -9.5e+64) {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	} else if (a <= -1e-153) {
		tmp = (y2 * y4) * fma(k, y1, (-c * t));
	} else if (a <= 4.5e-206) {
		tmp = (y2 * fma(-k, y5, (c * x))) * y0;
	} else if (a <= 9e-117) {
		tmp = (t * fma(i, z, (-y2 * y4))) * c;
	} else if (a <= 1.02e+128) {
		tmp = (j * fma(t, y4, (-x * y0))) * b;
	} else {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (a <= -9.5e+64)
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	elseif (a <= -1e-153)
		tmp = Float64(Float64(y2 * y4) * fma(k, y1, Float64(Float64(-c) * t)));
	elseif (a <= 4.5e-206)
		tmp = Float64(Float64(y2 * fma(Float64(-k), y5, Float64(c * x))) * y0);
	elseif (a <= 9e-117)
		tmp = Float64(Float64(t * fma(i, z, Float64(Float64(-y2) * y4))) * c);
	elseif (a <= 1.02e+128)
		tmp = Float64(Float64(j * fma(t, y4, Float64(Float64(-x) * y0))) * b);
	else
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[a, -9.5e+64], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -1e-153], N[(N[(y2 * y4), $MachinePrecision] * N[(k * y1 + N[((-c) * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.5e-206], N[(N[(y2 * N[((-k) * y5 + N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[a, 9e-117], N[(N[(t * N[(i * z + N[((-y2) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[a, 1.02e+128], N[(N[(j * N[(t * y4 + N[((-x) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * b), $MachinePrecision], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 6 regimes

2. if a < -9.50000000000000028e64

   1. Initial program 23.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 34.3%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 45.4%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]

   if -9.50000000000000028e64 < a < -1.00000000000000004e-153

   1. Initial program 21.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 34.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y4 around inf

      \[\leadsto y2 \cdot \color{blue}{\left(y4 \cdot \left(k \cdot y1 - c \cdot t\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 45.5%

      \[\leadsto \left(y2 \cdot y4\right) \cdot \color{blue}{\mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)} \]

   if -1.00000000000000004e-153 < a < 4.4999999999999998e-206

   1. Initial program 35.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 37.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 21.5%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in y2 around inf

      \[\leadsto \left(y2 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 45.0%

       \[\leadsto \left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0 \]

   if 4.4999999999999998e-206 < a < 8.99999999999999939e-117

   1. Initial program 37.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 45.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in t around inf

      \[\leadsto \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 42.6%

      \[\leadsto \left(t \cdot \mathsf{fma}\left(i, z, \left(-y2\right) \cdot y4\right)\right) \cdot c \]

   if 8.99999999999999939e-117 < a < 1.02000000000000008e128

   1. Initial program 24.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 53.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(t \cdot y4 - x \cdot y0\right)\right) \cdot b \]
   7. Step-by-step derivation

   8. Applied rewrites 60.5%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(t, y4, \left(-x\right) \cdot y0\right)\right) \cdot b \]

   if 1.02000000000000008e128 < a

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 53.8%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 54.3%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]
3. Recombined 6 regimes into one program.
4. Add Preprocessing

Alternative 14: 29.3% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -7.5 \cdot 10^{+196}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;b \leq -1.1 \cdot 10^{-216}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;b \leq 4.6 \cdot 10^{-212}:\\ \;\;\;\;y2 \cdot \left(y5 \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right)\\ \mathbf{elif}\;b \leq 4.3 \cdot 10^{-52}:\\ \;\;\;\;\left(i \cdot y\right) \cdot \mathsf{fma}\left(-c, x, k \cdot y5\right)\\ \mathbf{elif}\;b \leq 9.2 \cdot 10^{+227}:\\ \;\;\;\;\left(y \cdot y3\right) \cdot \mathsf{fma}\left(c, y4, \left(-a\right) \cdot y5\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\left(-b\right) \cdot j\right) \cdot x\right) \cdot y0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= b -7.5e+196)
   (* (* b x) (fma a y (* (- j) y0)))
   (if (<= b -1.1e-216)
     (* (* i k) (fma y y5 (* (- y1) z)))
     (if (<= b 4.6e-212)
       (* y2 (* y5 (fma a t (* (- k) y0))))
       (if (<= b 4.3e-52)
         (* (* i y) (fma (- c) x (* k y5)))
         (if (<= b 9.2e+227)
           (* (* y y3) (fma c y4 (* (- a) y5)))
           (* (* (* (- b) j) x) y0)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (b <= -7.5e+196) {
		tmp = (b * x) * fma(a, y, (-j * y0));
	} else if (b <= -1.1e-216) {
		tmp = (i * k) * fma(y, y5, (-y1 * z));
	} else if (b <= 4.6e-212) {
		tmp = y2 * (y5 * fma(a, t, (-k * y0)));
	} else if (b <= 4.3e-52) {
		tmp = (i * y) * fma(-c, x, (k * y5));
	} else if (b <= 9.2e+227) {
		tmp = (y * y3) * fma(c, y4, (-a * y5));
	} else {
		tmp = ((-b * j) * x) * y0;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (b <= -7.5e+196)
		tmp = Float64(Float64(b * x) * fma(a, y, Float64(Float64(-j) * y0)));
	elseif (b <= -1.1e-216)
		tmp = Float64(Float64(i * k) * fma(y, y5, Float64(Float64(-y1) * z)));
	elseif (b <= 4.6e-212)
		tmp = Float64(y2 * Float64(y5 * fma(a, t, Float64(Float64(-k) * y0))));
	elseif (b <= 4.3e-52)
		tmp = Float64(Float64(i * y) * fma(Float64(-c), x, Float64(k * y5)));
	elseif (b <= 9.2e+227)
		tmp = Float64(Float64(y * y3) * fma(c, y4, Float64(Float64(-a) * y5)));
	else
		tmp = Float64(Float64(Float64(Float64(-b) * j) * x) * y0);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[b, -7.5e+196], N[(N[(b * x), $MachinePrecision] * N[(a * y + N[((-j) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -1.1e-216], N[(N[(i * k), $MachinePrecision] * N[(y * y5 + N[((-y1) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 4.6e-212], N[(y2 * N[(y5 * N[(a * t + N[((-k) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 4.3e-52], N[(N[(i * y), $MachinePrecision] * N[((-c) * x + N[(k * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 9.2e+227], N[(N[(y * y3), $MachinePrecision] * N[(c * y4 + N[((-a) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[((-b) * j), $MachinePrecision] * x), $MachinePrecision] * y0), $MachinePrecision]]]]]]

Derivation

1. Split input into 6 regimes

2. if b < -7.5000000000000005e196

   1. Initial program 16.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(a \cdot \left(x \cdot y - t \cdot z\right) + y4 \cdot \left(j \cdot t - k \cdot y\right)\right) - y0 \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot b} \]

   5. Applied rewrites 60.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), a, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y4\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y0\right) \cdot b} \]

   6. Taylor expanded in x around inf

      \[\leadsto b \cdot \color{blue}{\left(x \cdot \left(a \cdot y - j \cdot y0\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.5%

      \[\leadsto \left(b \cdot x\right) \cdot \color{blue}{\mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)} \]

   if -7.5000000000000005e196 < b < -1.09999999999999995e-216

   1. Initial program 30.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 47.6%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 46.5%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   if -1.09999999999999995e-216 < b < 4.6000000000000002e-212

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 45.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y5 around -inf

      \[\leadsto -1 \cdot \color{blue}{\left(y2 \cdot \left(y5 \cdot \left(k \cdot y0 - a \cdot t\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 49.5%

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \mathsf{fma}\left(k, y0, \left(-a\right) \cdot t\right) \]
   9. Step-by-step derivation

   10. Applied rewrites 47.1%

       \[\leadsto y2 \cdot \left(y5 \cdot \color{blue}{\mathsf{fma}\left(a, t, -k \cdot y0\right)}\right) \]

   if 4.6000000000000002e-212 < b < 4.3000000000000003e-52

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 44.1%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 17.9%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around -inf

      \[\leadsto i \cdot \color{blue}{\left(y \cdot \left(-1 \cdot \left(c \cdot x\right) + k \cdot y5\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 40.6%

       \[\leadsto \left(i \cdot y\right) \cdot \color{blue}{\mathsf{fma}\left(-c, x, k \cdot y5\right)} \]

   if 4.3000000000000003e-52 < b < 9.1999999999999992e227

   1. Initial program 30.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 48.8%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y around inf

      \[\leadsto y \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(a \cdot y5\right) + c \cdot y4\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 39.6%

      \[\leadsto \left(y \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(c, y4, -a \cdot y5\right)} \]

   if 9.1999999999999992e227 < b

   1. Initial program 23.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 39.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 48.0%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in x around inf

      \[\leadsto \left(-1 \cdot \left(b \cdot \left(j \cdot x\right)\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 48.4%

       \[\leadsto \left(-\left(b \cdot j\right) \cdot x\right) \cdot y0 \]
3. Recombined 6 regimes into one program.

4. Final simplification 44.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \leq -7.5 \cdot 10^{+196}:\\ \;\;\;\;\left(b \cdot x\right) \cdot \mathsf{fma}\left(a, y, \left(-j\right) \cdot y0\right)\\ \mathbf{elif}\;b \leq -1.1 \cdot 10^{-216}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;b \leq 4.6 \cdot 10^{-212}:\\ \;\;\;\;y2 \cdot \left(y5 \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right)\\ \mathbf{elif}\;b \leq 4.3 \cdot 10^{-52}:\\ \;\;\;\;\left(i \cdot y\right) \cdot \mathsf{fma}\left(-c, x, k \cdot y5\right)\\ \mathbf{elif}\;b \leq 9.2 \cdot 10^{+227}:\\ \;\;\;\;\left(y \cdot y3\right) \cdot \mathsf{fma}\left(c, y4, \left(-a\right) \cdot y5\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\left(-b\right) \cdot j\right) \cdot x\right) \cdot y0\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 30.0% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0\\ \mathbf{if}\;b \leq -1.6 \cdot 10^{+197}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq -1.1 \cdot 10^{-216}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;b \leq -1.85 \cdot 10^{-277}:\\ \;\;\;\;y2 \cdot \left(y5 \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right)\\ \mathbf{elif}\;b \leq 1.26 \cdot 10^{+87}:\\ \;\;\;\;\left(c \cdot \mathsf{fma}\left(x, y2, \left(-y3\right) \cdot z\right)\right) \cdot y0\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* j (fma y3 y5 (* (- b) x))) y0)))
   (if (<= b -1.6e+197)
     t_1
     (if (<= b -1.1e-216)
       (* (* i k) (fma y y5 (* (- y1) z)))
       (if (<= b -1.85e-277)
         (* y2 (* y5 (fma a t (* (- k) y0))))
         (if (<= b 1.26e+87) (* (* c (fma x y2 (* (- y3) z))) y0) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (j * fma(y3, y5, (-b * x))) * y0;
	double tmp;
	if (b <= -1.6e+197) {
		tmp = t_1;
	} else if (b <= -1.1e-216) {
		tmp = (i * k) * fma(y, y5, (-y1 * z));
	} else if (b <= -1.85e-277) {
		tmp = y2 * (y5 * fma(a, t, (-k * y0)));
	} else if (b <= 1.26e+87) {
		tmp = (c * fma(x, y2, (-y3 * z))) * y0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(j * fma(y3, y5, Float64(Float64(-b) * x))) * y0)
	tmp = 0.0
	if (b <= -1.6e+197)
		tmp = t_1;
	elseif (b <= -1.1e-216)
		tmp = Float64(Float64(i * k) * fma(y, y5, Float64(Float64(-y1) * z)));
	elseif (b <= -1.85e-277)
		tmp = Float64(y2 * Float64(y5 * fma(a, t, Float64(Float64(-k) * y0))));
	elseif (b <= 1.26e+87)
		tmp = Float64(Float64(c * fma(x, y2, Float64(Float64(-y3) * z))) * y0);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(j * N[(y3 * y5 + N[((-b) * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision]}, If[LessEqual[b, -1.6e+197], t$95$1, If[LessEqual[b, -1.1e-216], N[(N[(i * k), $MachinePrecision] * N[(y * y5 + N[((-y1) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, -1.85e-277], N[(y2 * N[(y5 * N[(a * t + N[((-k) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.26e+87], N[(N[(c * N[(x * y2 + N[((-y3) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if b < -1.5999999999999999e197 or 1.26000000000000005e87 < b

   1. Initial program 23.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 30.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 44.4%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   if -1.5999999999999999e197 < b < -1.09999999999999995e-216

   1. Initial program 30.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 47.1%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 46.0%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   if -1.09999999999999995e-216 < b < -1.84999999999999992e-277

   1. Initial program 37.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 63.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y5 around -inf

      \[\leadsto -1 \cdot \color{blue}{\left(y2 \cdot \left(y5 \cdot \left(k \cdot y0 - a \cdot t\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 69.1%

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \mathsf{fma}\left(k, y0, \left(-a\right) \cdot t\right) \]
   9. Step-by-step derivation

   10. Applied rewrites 62.9%

       \[\leadsto y2 \cdot \left(y5 \cdot \color{blue}{\mathsf{fma}\left(a, t, -k \cdot y0\right)}\right) \]

   if -1.84999999999999992e-277 < b < 1.26000000000000005e87

   1. Initial program 29.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 47.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 19.0%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in c around inf

      \[\leadsto \left(c \cdot \left(-1 \cdot \left(y3 \cdot z\right) + x \cdot y2\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 38.5%

       \[\leadsto \left(c \cdot \mathsf{fma}\left(x, y2, -y3 \cdot z\right)\right) \cdot y0 \]
3. Recombined 4 regimes into one program.

4. Final simplification 43.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \leq -1.6 \cdot 10^{+197}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0\\ \mathbf{elif}\;b \leq -1.1 \cdot 10^{-216}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;b \leq -1.85 \cdot 10^{-277}:\\ \;\;\;\;y2 \cdot \left(y5 \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right)\\ \mathbf{elif}\;b \leq 1.26 \cdot 10^{+87}:\\ \;\;\;\;\left(c \cdot \mathsf{fma}\left(x, y2, \left(-y3\right) \cdot z\right)\right) \cdot y0\\ \mathbf{else}:\\ \;\;\;\;\left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0\\ \end{array} \]
5. Add Preprocessing

Alternative 16: 31.4% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+80}:\\ \;\;\;\;\left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-253}:\\ \;\;\;\;\left(i \cdot y\right) \cdot \mathsf{fma}\left(-c, x, k \cdot y5\right)\\ \mathbf{elif}\;z \leq 3.1 \cdot 10^{-131}:\\ \;\;\;\;\left(x \cdot y0\right) \cdot \mathsf{fma}\left(c, y2, \left(-b\right) \cdot j\right)\\ \mathbf{elif}\;z \leq 1.15 \cdot 10^{+18}:\\ \;\;\;\;y2 \cdot \left(y5 \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= z -2.1e+80)
   (* (* z (fma (- y0) y3 (* i t))) c)
   (if (<= z -3.8e-253)
     (* (* i y) (fma (- c) x (* k y5)))
     (if (<= z 3.1e-131)
       (* (* x y0) (fma c y2 (* (- b) j)))
       (if (<= z 1.15e+18)
         (* y2 (* y5 (fma a t (* (- k) y0))))
         (* (* y1 z) (fma a y3 (* (- i) k))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (z <= -2.1e+80) {
		tmp = (z * fma(-y0, y3, (i * t))) * c;
	} else if (z <= -3.8e-253) {
		tmp = (i * y) * fma(-c, x, (k * y5));
	} else if (z <= 3.1e-131) {
		tmp = (x * y0) * fma(c, y2, (-b * j));
	} else if (z <= 1.15e+18) {
		tmp = y2 * (y5 * fma(a, t, (-k * y0)));
	} else {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (z <= -2.1e+80)
		tmp = Float64(Float64(z * fma(Float64(-y0), y3, Float64(i * t))) * c);
	elseif (z <= -3.8e-253)
		tmp = Float64(Float64(i * y) * fma(Float64(-c), x, Float64(k * y5)));
	elseif (z <= 3.1e-131)
		tmp = Float64(Float64(x * y0) * fma(c, y2, Float64(Float64(-b) * j)));
	elseif (z <= 1.15e+18)
		tmp = Float64(y2 * Float64(y5 * fma(a, t, Float64(Float64(-k) * y0))));
	else
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[z, -2.1e+80], N[(N[(z * N[((-y0) * y3 + N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[z, -3.8e-253], N[(N[(i * y), $MachinePrecision] * N[((-c) * x + N[(k * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.1e-131], N[(N[(x * y0), $MachinePrecision] * N[(c * y2 + N[((-b) * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.15e+18], N[(y2 * N[(y5 * N[(a * t + N[((-k) * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if z < -2.10000000000000001e80

   1. Initial program 20.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 49.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in z around inf

      \[\leadsto \left(z \cdot \left(-1 \cdot \left(y0 \cdot y3\right) + i \cdot t\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 57.2%

      \[\leadsto \left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c \]

   if -2.10000000000000001e80 < z < -3.80000000000000012e-253

   1. Initial program 37.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 43.3%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 24.7%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around -inf

      \[\leadsto i \cdot \color{blue}{\left(y \cdot \left(-1 \cdot \left(c \cdot x\right) + k \cdot y5\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 31.2%

       \[\leadsto \left(i \cdot y\right) \cdot \color{blue}{\mathsf{fma}\left(-c, x, k \cdot y5\right)} \]

   if -3.80000000000000012e-253 < z < 3.10000000000000021e-131

   1. Initial program 30.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 54.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in x around inf

      \[\leadsto x \cdot \color{blue}{\left(y0 \cdot \left(c \cdot y2 - b \cdot j\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 48.0%

      \[\leadsto \left(x \cdot y0\right) \cdot \color{blue}{\mathsf{fma}\left(c, y2, \left(-b\right) \cdot j\right)} \]

   if 3.10000000000000021e-131 < z < 1.15e18

   1. Initial program 25.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 35.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y5 around -inf

      \[\leadsto -1 \cdot \color{blue}{\left(y2 \cdot \left(y5 \cdot \left(k \cdot y0 - a \cdot t\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 32.4%

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \mathsf{fma}\left(k, y0, \left(-a\right) \cdot t\right) \]
   9. Step-by-step derivation

   10. Applied rewrites 35.5%

       \[\leadsto y2 \cdot \left(y5 \cdot \color{blue}{\mathsf{fma}\left(a, t, -k \cdot y0\right)}\right) \]

   if 1.15e18 < z

   1. Initial program 25.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 51.5%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 46.6%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]
3. Recombined 5 regimes into one program.

4. Final simplification 43.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+80}:\\ \;\;\;\;\left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-253}:\\ \;\;\;\;\left(i \cdot y\right) \cdot \mathsf{fma}\left(-c, x, k \cdot y5\right)\\ \mathbf{elif}\;z \leq 3.1 \cdot 10^{-131}:\\ \;\;\;\;\left(x \cdot y0\right) \cdot \mathsf{fma}\left(c, y2, \left(-b\right) \cdot j\right)\\ \mathbf{elif}\;z \leq 1.15 \cdot 10^{+18}:\\ \;\;\;\;y2 \cdot \left(y5 \cdot \mathsf{fma}\left(a, t, \left(-k\right) \cdot y0\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 17: 30.4% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -9.5 \cdot 10^{+64}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -4 \cdot 10^{-145}:\\ \;\;\;\;\left(y2 \cdot y4\right) \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\\ \mathbf{elif}\;a \leq 1.8 \cdot 10^{-231}:\\ \;\;\;\;\left(x \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\right) \cdot c\\ \mathbf{elif}\;a \leq 8.5 \cdot 10^{+141}:\\ \;\;\;\;\left(t \cdot \mathsf{fma}\left(i, z, \left(-y2\right) \cdot y4\right)\right) \cdot c\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= a -9.5e+64)
   (* (* y1 z) (fma a y3 (* (- i) k)))
   (if (<= a -4e-145)
     (* (* y2 y4) (fma k y1 (* (- c) t)))
     (if (<= a 1.8e-231)
       (* (* x (fma y0 y2 (* (- i) y))) c)
       (if (<= a 8.5e+141)
         (* (* t (fma i z (* (- y2) y4))) c)
         (* (* a y3) (fma y1 z (* (- y) y5))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (a <= -9.5e+64) {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	} else if (a <= -4e-145) {
		tmp = (y2 * y4) * fma(k, y1, (-c * t));
	} else if (a <= 1.8e-231) {
		tmp = (x * fma(y0, y2, (-i * y))) * c;
	} else if (a <= 8.5e+141) {
		tmp = (t * fma(i, z, (-y2 * y4))) * c;
	} else {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (a <= -9.5e+64)
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	elseif (a <= -4e-145)
		tmp = Float64(Float64(y2 * y4) * fma(k, y1, Float64(Float64(-c) * t)));
	elseif (a <= 1.8e-231)
		tmp = Float64(Float64(x * fma(y0, y2, Float64(Float64(-i) * y))) * c);
	elseif (a <= 8.5e+141)
		tmp = Float64(Float64(t * fma(i, z, Float64(Float64(-y2) * y4))) * c);
	else
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[a, -9.5e+64], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -4e-145], N[(N[(y2 * y4), $MachinePrecision] * N[(k * y1 + N[((-c) * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.8e-231], N[(N[(x * N[(y0 * y2 + N[((-i) * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[a, 8.5e+141], N[(N[(t * N[(i * z + N[((-y2) * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if a < -9.50000000000000028e64

   1. Initial program 23.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 34.3%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 45.4%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]

   if -9.50000000000000028e64 < a < -3.99999999999999966e-145

   1. Initial program 22.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 36.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y4 around inf

      \[\leadsto y2 \cdot \color{blue}{\left(y4 \cdot \left(k \cdot y1 - c \cdot t\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 45.2%

      \[\leadsto \left(y2 \cdot y4\right) \cdot \color{blue}{\mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)} \]

   if -3.99999999999999966e-145 < a < 1.79999999999999987e-231

   1. Initial program 33.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 40.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 41.6%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(y0, y2, -i \cdot y\right)\right) \cdot c \]

   if 1.79999999999999987e-231 < a < 8.4999999999999996e141

   1. Initial program 31.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 46.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in t around inf

      \[\leadsto \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 36.5%

      \[\leadsto \left(t \cdot \mathsf{fma}\left(i, z, \left(-y2\right) \cdot y4\right)\right) \cdot c \]

   if 8.4999999999999996e141 < a

   1. Initial program 27.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 55.4%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 55.9%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]
3. Recombined 5 regimes into one program.

4. Final simplification 43.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -9.5 \cdot 10^{+64}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{elif}\;a \leq -4 \cdot 10^{-145}:\\ \;\;\;\;\left(y2 \cdot y4\right) \cdot \mathsf{fma}\left(k, y1, \left(-c\right) \cdot t\right)\\ \mathbf{elif}\;a \leq 1.8 \cdot 10^{-231}:\\ \;\;\;\;\left(x \cdot \mathsf{fma}\left(y0, y2, \left(-i\right) \cdot y\right)\right) \cdot c\\ \mathbf{elif}\;a \leq 8.5 \cdot 10^{+141}:\\ \;\;\;\;\left(t \cdot \mathsf{fma}\left(i, z, \left(-y2\right) \cdot y4\right)\right) \cdot c\\ \mathbf{else}:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 18: 30.3% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \mathbf{if}\;z \leq -7.8 \cdot 10^{+242}:\\ \;\;\;\;\left(b \cdot \left(k \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;z \leq -9 \cdot 10^{+23}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq -3.8 \cdot 10^{-253}:\\ \;\;\;\;\left(i \cdot y\right) \cdot \mathsf{fma}\left(-c, x, k \cdot y5\right)\\ \mathbf{elif}\;z \leq 5.6 \cdot 10^{+18}:\\ \;\;\;\;\left(x \cdot y0\right) \cdot \mathsf{fma}\left(c, y2, \left(-b\right) \cdot j\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* y1 z) (fma a y3 (* (- i) k)))))
   (if (<= z -7.8e+242)
     (* (* b (* k z)) y0)
     (if (<= z -9e+23)
       t_1
       (if (<= z -3.8e-253)
         (* (* i y) (fma (- c) x (* k y5)))
         (if (<= z 5.6e+18) (* (* x y0) (fma c y2 (* (- b) j))) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y1 * z) * fma(a, y3, (-i * k));
	double tmp;
	if (z <= -7.8e+242) {
		tmp = (b * (k * z)) * y0;
	} else if (z <= -9e+23) {
		tmp = t_1;
	} else if (z <= -3.8e-253) {
		tmp = (i * y) * fma(-c, x, (k * y5));
	} else if (z <= 5.6e+18) {
		tmp = (x * y0) * fma(c, y2, (-b * j));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)))
	tmp = 0.0
	if (z <= -7.8e+242)
		tmp = Float64(Float64(b * Float64(k * z)) * y0);
	elseif (z <= -9e+23)
		tmp = t_1;
	elseif (z <= -3.8e-253)
		tmp = Float64(Float64(i * y) * fma(Float64(-c), x, Float64(k * y5)));
	elseif (z <= 5.6e+18)
		tmp = Float64(Float64(x * y0) * fma(c, y2, Float64(Float64(-b) * j)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.8e+242], N[(N[(b * N[(k * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[z, -9e+23], t$95$1, If[LessEqual[z, -3.8e-253], N[(N[(i * y), $MachinePrecision] * N[((-c) * x + N[(k * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 5.6e+18], N[(N[(x * y0), $MachinePrecision] * N[(c * y2 + N[((-b) * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -7.8000000000000003e242

   1. Initial program 14.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 71.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 71.9%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   9. Taylor expanded in b around inf

      \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 50.9%

       \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]

   if -7.8000000000000003e242 < z < -8.99999999999999958e23 or 5.6e18 < z

   1. Initial program 25.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 54.6%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 49.0%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]

   if -8.99999999999999958e23 < z < -3.80000000000000012e-253

   1. Initial program 35.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 43.5%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 26.6%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around -inf

      \[\leadsto i \cdot \color{blue}{\left(y \cdot \left(-1 \cdot \left(c \cdot x\right) + k \cdot y5\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 32.0%

       \[\leadsto \left(i \cdot y\right) \cdot \color{blue}{\mathsf{fma}\left(-c, x, k \cdot y5\right)} \]

   if -3.80000000000000012e-253 < z < 5.6e18

   1. Initial program 29.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 43.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in x around inf

      \[\leadsto x \cdot \color{blue}{\left(y0 \cdot \left(c \cdot y2 - b \cdot j\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 37.6%

      \[\leadsto \left(x \cdot y0\right) \cdot \color{blue}{\mathsf{fma}\left(c, y2, \left(-b\right) \cdot j\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 19: 22.0% accurate, 4.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \mathbf{if}\;x \leq -1.8 \cdot 10^{-19}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq -1.7 \cdot 10^{-185}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \left(\left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{-213}:\\ \;\;\;\;-k \cdot \left(\left(y0 \cdot y2\right) \cdot y5\right)\\ \mathbf{elif}\;x \leq 6.9 \cdot 10^{+59}:\\ \;\;\;\;\left(b \cdot \left(k \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{+207}:\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* (* x y0) y2) c)))
   (if (<= x -1.8e-19)
     t_1
     (if (<= x -1.7e-185)
       (* (* i k) (* (- y1) z))
       (if (<= x 4.2e-213)
         (- (* k (* (* y0 y2) y5)))
         (if (<= x 6.9e+59)
           (* (* b (* k z)) y0)
           (if (<= x 4.2e+207) (* (* (* k i) y5) y) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((x * y0) * y2) * c;
	double tmp;
	if (x <= -1.8e-19) {
		tmp = t_1;
	} else if (x <= -1.7e-185) {
		tmp = (i * k) * (-y1 * z);
	} else if (x <= 4.2e-213) {
		tmp = -(k * ((y0 * y2) * y5));
	} else if (x <= 6.9e+59) {
		tmp = (b * (k * z)) * y0;
	} else if (x <= 4.2e+207) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((x * y0) * y2) * c
    if (x <= (-1.8d-19)) then
        tmp = t_1
    else if (x <= (-1.7d-185)) then
        tmp = (i * k) * (-y1 * z)
    else if (x <= 4.2d-213) then
        tmp = -(k * ((y0 * y2) * y5))
    else if (x <= 6.9d+59) then
        tmp = (b * (k * z)) * y0
    else if (x <= 4.2d+207) then
        tmp = ((k * i) * y5) * y
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((x * y0) * y2) * c;
	double tmp;
	if (x <= -1.8e-19) {
		tmp = t_1;
	} else if (x <= -1.7e-185) {
		tmp = (i * k) * (-y1 * z);
	} else if (x <= 4.2e-213) {
		tmp = -(k * ((y0 * y2) * y5));
	} else if (x <= 6.9e+59) {
		tmp = (b * (k * z)) * y0;
	} else if (x <= 4.2e+207) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = ((x * y0) * y2) * c
	tmp = 0
	if x <= -1.8e-19:
		tmp = t_1
	elif x <= -1.7e-185:
		tmp = (i * k) * (-y1 * z)
	elif x <= 4.2e-213:
		tmp = -(k * ((y0 * y2) * y5))
	elif x <= 6.9e+59:
		tmp = (b * (k * z)) * y0
	elif x <= 4.2e+207:
		tmp = ((k * i) * y5) * y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(Float64(x * y0) * y2) * c)
	tmp = 0.0
	if (x <= -1.8e-19)
		tmp = t_1;
	elseif (x <= -1.7e-185)
		tmp = Float64(Float64(i * k) * Float64(Float64(-y1) * z));
	elseif (x <= 4.2e-213)
		tmp = Float64(-Float64(k * Float64(Float64(y0 * y2) * y5)));
	elseif (x <= 6.9e+59)
		tmp = Float64(Float64(b * Float64(k * z)) * y0);
	elseif (x <= 4.2e+207)
		tmp = Float64(Float64(Float64(k * i) * y5) * y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = ((x * y0) * y2) * c;
	tmp = 0.0;
	if (x <= -1.8e-19)
		tmp = t_1;
	elseif (x <= -1.7e-185)
		tmp = (i * k) * (-y1 * z);
	elseif (x <= 4.2e-213)
		tmp = -(k * ((y0 * y2) * y5));
	elseif (x <= 6.9e+59)
		tmp = (b * (k * z)) * y0;
	elseif (x <= 4.2e+207)
		tmp = ((k * i) * y5) * y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(x * y0), $MachinePrecision] * y2), $MachinePrecision] * c), $MachinePrecision]}, If[LessEqual[x, -1.8e-19], t$95$1, If[LessEqual[x, -1.7e-185], N[(N[(i * k), $MachinePrecision] * N[((-y1) * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.2e-213], (-N[(k * N[(N[(y0 * y2), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision]), If[LessEqual[x, 6.9e+59], N[(N[(b * N[(k * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[x, 4.2e+207], N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 5 regimes

2. if x < -1.8000000000000001e-19 or 4.1999999999999999e207 < x

   1. Initial program 20.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 41.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 36.8%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(y0, y2, -i \cdot y\right)\right) \cdot c \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(x \cdot \left(y0 \cdot y2\right)\right) \cdot c \]
   10. Step-by-step derivation

   11. Applied rewrites 33.4%

       \[\leadsto \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c \]

   if -1.8000000000000001e-19 < x < -1.6999999999999999e-185

   1. Initial program 25.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 48.8%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 32.9%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(i \cdot k\right) \cdot \left(-1 \cdot \left(y1 \cdot \color{blue}{z}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 36.6%

       \[\leadsto \left(i \cdot k\right) \cdot \left(\left(-y1\right) \cdot z\right) \]

   if -1.6999999999999999e-185 < x < 4.1999999999999997e-213

   1. Initial program 40.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 33.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y5 around -inf

      \[\leadsto -1 \cdot \color{blue}{\left(y2 \cdot \left(y5 \cdot \left(k \cdot y0 - a \cdot t\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 33.3%

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \mathsf{fma}\left(k, y0, \left(-a\right) \cdot t\right) \]

   9. Taylor expanded in t around 0

      \[\leadsto -k \cdot \left(y0 \cdot \left(y2 \cdot y5\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 33.5%

       \[\leadsto -k \cdot \left(\left(y0 \cdot y2\right) \cdot y5\right) \]

   if 4.1999999999999997e-213 < x < 6.8999999999999998e59

   1. Initial program 32.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 36.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 35.8%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   9. Taylor expanded in b around inf

      \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 27.5%

       \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]

   if 6.8999999999999998e59 < x < 4.1999999999999999e207

   1. Initial program 26.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 60.3%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.9%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 35.4%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 48.0%

       \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 20: 22.2% accurate, 4.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \mathbf{if}\;x \leq -1.8 \cdot 10^{-19}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq -5.2 \cdot 10^{-233}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \left(\left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;x \leq 2.7 \cdot 10^{-205}:\\ \;\;\;\;\left(\left(j \cdot y3\right) \cdot y5\right) \cdot y0\\ \mathbf{elif}\;x \leq 6.9 \cdot 10^{+59}:\\ \;\;\;\;\left(b \cdot \left(k \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{+207}:\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* (* x y0) y2) c)))
   (if (<= x -1.8e-19)
     t_1
     (if (<= x -5.2e-233)
       (* (* i k) (* (- y1) z))
       (if (<= x 2.7e-205)
         (* (* (* j y3) y5) y0)
         (if (<= x 6.9e+59)
           (* (* b (* k z)) y0)
           (if (<= x 4.2e+207) (* (* (* k i) y5) y) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((x * y0) * y2) * c;
	double tmp;
	if (x <= -1.8e-19) {
		tmp = t_1;
	} else if (x <= -5.2e-233) {
		tmp = (i * k) * (-y1 * z);
	} else if (x <= 2.7e-205) {
		tmp = ((j * y3) * y5) * y0;
	} else if (x <= 6.9e+59) {
		tmp = (b * (k * z)) * y0;
	} else if (x <= 4.2e+207) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((x * y0) * y2) * c
    if (x <= (-1.8d-19)) then
        tmp = t_1
    else if (x <= (-5.2d-233)) then
        tmp = (i * k) * (-y1 * z)
    else if (x <= 2.7d-205) then
        tmp = ((j * y3) * y5) * y0
    else if (x <= 6.9d+59) then
        tmp = (b * (k * z)) * y0
    else if (x <= 4.2d+207) then
        tmp = ((k * i) * y5) * y
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((x * y0) * y2) * c;
	double tmp;
	if (x <= -1.8e-19) {
		tmp = t_1;
	} else if (x <= -5.2e-233) {
		tmp = (i * k) * (-y1 * z);
	} else if (x <= 2.7e-205) {
		tmp = ((j * y3) * y5) * y0;
	} else if (x <= 6.9e+59) {
		tmp = (b * (k * z)) * y0;
	} else if (x <= 4.2e+207) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = ((x * y0) * y2) * c
	tmp = 0
	if x <= -1.8e-19:
		tmp = t_1
	elif x <= -5.2e-233:
		tmp = (i * k) * (-y1 * z)
	elif x <= 2.7e-205:
		tmp = ((j * y3) * y5) * y0
	elif x <= 6.9e+59:
		tmp = (b * (k * z)) * y0
	elif x <= 4.2e+207:
		tmp = ((k * i) * y5) * y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(Float64(x * y0) * y2) * c)
	tmp = 0.0
	if (x <= -1.8e-19)
		tmp = t_1;
	elseif (x <= -5.2e-233)
		tmp = Float64(Float64(i * k) * Float64(Float64(-y1) * z));
	elseif (x <= 2.7e-205)
		tmp = Float64(Float64(Float64(j * y3) * y5) * y0);
	elseif (x <= 6.9e+59)
		tmp = Float64(Float64(b * Float64(k * z)) * y0);
	elseif (x <= 4.2e+207)
		tmp = Float64(Float64(Float64(k * i) * y5) * y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = ((x * y0) * y2) * c;
	tmp = 0.0;
	if (x <= -1.8e-19)
		tmp = t_1;
	elseif (x <= -5.2e-233)
		tmp = (i * k) * (-y1 * z);
	elseif (x <= 2.7e-205)
		tmp = ((j * y3) * y5) * y0;
	elseif (x <= 6.9e+59)
		tmp = (b * (k * z)) * y0;
	elseif (x <= 4.2e+207)
		tmp = ((k * i) * y5) * y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(x * y0), $MachinePrecision] * y2), $MachinePrecision] * c), $MachinePrecision]}, If[LessEqual[x, -1.8e-19], t$95$1, If[LessEqual[x, -5.2e-233], N[(N[(i * k), $MachinePrecision] * N[((-y1) * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.7e-205], N[(N[(N[(j * y3), $MachinePrecision] * y5), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[x, 6.9e+59], N[(N[(b * N[(k * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[x, 4.2e+207], N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 5 regimes

2. if x < -1.8000000000000001e-19 or 4.1999999999999999e207 < x

   1. Initial program 20.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 41.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 36.8%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(y0, y2, -i \cdot y\right)\right) \cdot c \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(x \cdot \left(y0 \cdot y2\right)\right) \cdot c \]
   10. Step-by-step derivation

   11. Applied rewrites 33.4%

       \[\leadsto \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c \]

   if -1.8000000000000001e-19 < x < -5.1999999999999996e-233

   1. Initial program 37.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 47.2%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 28.8%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(i \cdot k\right) \cdot \left(-1 \cdot \left(y1 \cdot \color{blue}{z}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 29.1%

       \[\leadsto \left(i \cdot k\right) \cdot \left(\left(-y1\right) \cdot z\right) \]

   if -5.1999999999999996e-233 < x < 2.7000000000000001e-205

   1. Initial program 31.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 48.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 32.9%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in x around 0

      \[\leadsto \left(j \cdot \left(y3 \cdot y5\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 35.3%

       \[\leadsto \left(\left(j \cdot y3\right) \cdot y5\right) \cdot y0 \]

   if 2.7000000000000001e-205 < x < 6.8999999999999998e59

   1. Initial program 32.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 36.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 35.7%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   9. Taylor expanded in b around inf

      \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 28.9%

       \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]

   if 6.8999999999999998e59 < x < 4.1999999999999999e207

   1. Initial program 26.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 60.3%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.9%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 35.4%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 48.0%

       \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 21: 22.4% accurate, 4.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \mathbf{if}\;x \leq -3.85 \cdot 10^{-26}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq -5.2 \cdot 10^{-233}:\\ \;\;\;\;\left(-i\right) \cdot \left(k \cdot \left(y1 \cdot z\right)\right)\\ \mathbf{elif}\;x \leq 2.7 \cdot 10^{-205}:\\ \;\;\;\;\left(\left(j \cdot y3\right) \cdot y5\right) \cdot y0\\ \mathbf{elif}\;x \leq 6.9 \cdot 10^{+59}:\\ \;\;\;\;\left(b \cdot \left(k \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{+207}:\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* (* x y0) y2) c)))
   (if (<= x -3.85e-26)
     t_1
     (if (<= x -5.2e-233)
       (* (- i) (* k (* y1 z)))
       (if (<= x 2.7e-205)
         (* (* (* j y3) y5) y0)
         (if (<= x 6.9e+59)
           (* (* b (* k z)) y0)
           (if (<= x 4.2e+207) (* (* (* k i) y5) y) t_1)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((x * y0) * y2) * c;
	double tmp;
	if (x <= -3.85e-26) {
		tmp = t_1;
	} else if (x <= -5.2e-233) {
		tmp = -i * (k * (y1 * z));
	} else if (x <= 2.7e-205) {
		tmp = ((j * y3) * y5) * y0;
	} else if (x <= 6.9e+59) {
		tmp = (b * (k * z)) * y0;
	} else if (x <= 4.2e+207) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((x * y0) * y2) * c
    if (x <= (-3.85d-26)) then
        tmp = t_1
    else if (x <= (-5.2d-233)) then
        tmp = -i * (k * (y1 * z))
    else if (x <= 2.7d-205) then
        tmp = ((j * y3) * y5) * y0
    else if (x <= 6.9d+59) then
        tmp = (b * (k * z)) * y0
    else if (x <= 4.2d+207) then
        tmp = ((k * i) * y5) * y
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((x * y0) * y2) * c;
	double tmp;
	if (x <= -3.85e-26) {
		tmp = t_1;
	} else if (x <= -5.2e-233) {
		tmp = -i * (k * (y1 * z));
	} else if (x <= 2.7e-205) {
		tmp = ((j * y3) * y5) * y0;
	} else if (x <= 6.9e+59) {
		tmp = (b * (k * z)) * y0;
	} else if (x <= 4.2e+207) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = ((x * y0) * y2) * c
	tmp = 0
	if x <= -3.85e-26:
		tmp = t_1
	elif x <= -5.2e-233:
		tmp = -i * (k * (y1 * z))
	elif x <= 2.7e-205:
		tmp = ((j * y3) * y5) * y0
	elif x <= 6.9e+59:
		tmp = (b * (k * z)) * y0
	elif x <= 4.2e+207:
		tmp = ((k * i) * y5) * y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(Float64(x * y0) * y2) * c)
	tmp = 0.0
	if (x <= -3.85e-26)
		tmp = t_1;
	elseif (x <= -5.2e-233)
		tmp = Float64(Float64(-i) * Float64(k * Float64(y1 * z)));
	elseif (x <= 2.7e-205)
		tmp = Float64(Float64(Float64(j * y3) * y5) * y0);
	elseif (x <= 6.9e+59)
		tmp = Float64(Float64(b * Float64(k * z)) * y0);
	elseif (x <= 4.2e+207)
		tmp = Float64(Float64(Float64(k * i) * y5) * y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = ((x * y0) * y2) * c;
	tmp = 0.0;
	if (x <= -3.85e-26)
		tmp = t_1;
	elseif (x <= -5.2e-233)
		tmp = -i * (k * (y1 * z));
	elseif (x <= 2.7e-205)
		tmp = ((j * y3) * y5) * y0;
	elseif (x <= 6.9e+59)
		tmp = (b * (k * z)) * y0;
	elseif (x <= 4.2e+207)
		tmp = ((k * i) * y5) * y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(x * y0), $MachinePrecision] * y2), $MachinePrecision] * c), $MachinePrecision]}, If[LessEqual[x, -3.85e-26], t$95$1, If[LessEqual[x, -5.2e-233], N[((-i) * N[(k * N[(y1 * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.7e-205], N[(N[(N[(j * y3), $MachinePrecision] * y5), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[x, 6.9e+59], N[(N[(b * N[(k * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[x, 4.2e+207], N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 5 regimes

2. if x < -3.85e-26 or 4.1999999999999999e207 < x

   1. Initial program 20.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 41.4%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 36.4%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(y0, y2, -i \cdot y\right)\right) \cdot c \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(x \cdot \left(y0 \cdot y2\right)\right) \cdot c \]
   10. Step-by-step derivation

   11. Applied rewrites 33.0%

       \[\leadsto \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c \]

   if -3.85e-26 < x < -5.1999999999999996e-233

   1. Initial program 38.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 46.0%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 29.4%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around 0

      \[\leadsto -1 \cdot \left(i \cdot \color{blue}{\left(k \cdot \left(y1 \cdot z\right)\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 27.6%

       \[\leadsto -i \cdot \left(k \cdot \left(y1 \cdot z\right)\right) \]

   if -5.1999999999999996e-233 < x < 2.7000000000000001e-205

   1. Initial program 31.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 48.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 32.9%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in x around 0

      \[\leadsto \left(j \cdot \left(y3 \cdot y5\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 35.3%

       \[\leadsto \left(\left(j \cdot y3\right) \cdot y5\right) \cdot y0 \]

   if 2.7000000000000001e-205 < x < 6.8999999999999998e59

   1. Initial program 32.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 36.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 35.7%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   9. Taylor expanded in b around inf

      \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 28.9%

       \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]

   if 6.8999999999999998e59 < x < 4.1999999999999999e207

   1. Initial program 26.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 60.3%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 44.9%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 35.4%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 48.0%

       \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]
3. Recombined 5 regimes into one program.

4. Final simplification 33.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.85 \cdot 10^{-26}:\\ \;\;\;\;\left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \mathbf{elif}\;x \leq -5.2 \cdot 10^{-233}:\\ \;\;\;\;\left(-i\right) \cdot \left(k \cdot \left(y1 \cdot z\right)\right)\\ \mathbf{elif}\;x \leq 2.7 \cdot 10^{-205}:\\ \;\;\;\;\left(\left(j \cdot y3\right) \cdot y5\right) \cdot y0\\ \mathbf{elif}\;x \leq 6.9 \cdot 10^{+59}:\\ \;\;\;\;\left(b \cdot \left(k \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{+207}:\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \end{array} \]
5. Add Preprocessing

Alternative 22: 25.6% accurate, 4.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -k \cdot \left(\left(y0 \cdot y2\right) \cdot y5\right)\\ \mathbf{if}\;y2 \leq -7.8 \cdot 10^{+213}:\\ \;\;\;\;\left(c \cdot \left(\left(-y3\right) \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;y2 \leq -2.4 \cdot 10^{+143}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq 1.1 \cdot 10^{+31}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;y2 \leq 1.6 \cdot 10^{+271}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-y2\right) \cdot y5\right) \cdot \left(\left(-a\right) \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (- (* k (* (* y0 y2) y5)))))
   (if (<= y2 -7.8e+213)
     (* (* c (* (- y3) z)) y0)
     (if (<= y2 -2.4e+143)
       t_1
       (if (<= y2 1.1e+31)
         (* (* i k) (fma y y5 (* (- y1) z)))
         (if (<= y2 1.6e+271) t_1 (* (* (- y2) y5) (* (- a) t))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = -(k * ((y0 * y2) * y5));
	double tmp;
	if (y2 <= -7.8e+213) {
		tmp = (c * (-y3 * z)) * y0;
	} else if (y2 <= -2.4e+143) {
		tmp = t_1;
	} else if (y2 <= 1.1e+31) {
		tmp = (i * k) * fma(y, y5, (-y1 * z));
	} else if (y2 <= 1.6e+271) {
		tmp = t_1;
	} else {
		tmp = (-y2 * y5) * (-a * t);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(-Float64(k * Float64(Float64(y0 * y2) * y5)))
	tmp = 0.0
	if (y2 <= -7.8e+213)
		tmp = Float64(Float64(c * Float64(Float64(-y3) * z)) * y0);
	elseif (y2 <= -2.4e+143)
		tmp = t_1;
	elseif (y2 <= 1.1e+31)
		tmp = Float64(Float64(i * k) * fma(y, y5, Float64(Float64(-y1) * z)));
	elseif (y2 <= 1.6e+271)
		tmp = t_1;
	else
		tmp = Float64(Float64(Float64(-y2) * y5) * Float64(Float64(-a) * t));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = (-N[(k * N[(N[(y0 * y2), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision])}, If[LessEqual[y2, -7.8e+213], N[(N[(c * N[((-y3) * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[y2, -2.4e+143], t$95$1, If[LessEqual[y2, 1.1e+31], N[(N[(i * k), $MachinePrecision] * N[(y * y5 + N[((-y1) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 1.6e+271], t$95$1, N[(N[((-y2) * y5), $MachinePrecision] * N[((-a) * t), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if y2 < -7.8000000000000003e213

   1. Initial program 10.5%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 52.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 42.2%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   9. Taylor expanded in b around 0

      \[\leadsto \left(-1 \cdot \left(c \cdot \left(y3 \cdot z\right)\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 48.1%

       \[\leadsto \left(\left(-c\right) \cdot \left(y3 \cdot z\right)\right) \cdot y0 \]

   if -7.8000000000000003e213 < y2 < -2.3999999999999998e143 or 1.10000000000000005e31 < y2 < 1.6000000000000001e271

   1. Initial program 30.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 56.7%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y5 around -inf

      \[\leadsto -1 \cdot \color{blue}{\left(y2 \cdot \left(y5 \cdot \left(k \cdot y0 - a \cdot t\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 46.5%

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \mathsf{fma}\left(k, y0, \left(-a\right) \cdot t\right) \]

   9. Taylor expanded in t around 0

      \[\leadsto -k \cdot \left(y0 \cdot \left(y2 \cdot y5\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 45.9%

       \[\leadsto -k \cdot \left(\left(y0 \cdot y2\right) \cdot y5\right) \]

   if -2.3999999999999998e143 < y2 < 1.10000000000000005e31

   1. Initial program 31.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 41.5%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 31.2%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   if 1.6000000000000001e271 < y2

   1. Initial program 0.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 75.0%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y5 around -inf

      \[\leadsto -1 \cdot \color{blue}{\left(y2 \cdot \left(y5 \cdot \left(k \cdot y0 - a \cdot t\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 62.5%

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \mathsf{fma}\left(k, y0, \left(-a\right) \cdot t\right) \]

   9. Taylor expanded in t around inf

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \left(-1 \cdot \left(a \cdot t\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 75.1%

       \[\leadsto -\left(y2 \cdot y5\right) \cdot \left(\left(-a\right) \cdot t\right) \]
3. Recombined 4 regimes into one program.

4. Final simplification 38.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -7.8 \cdot 10^{+213}:\\ \;\;\;\;\left(c \cdot \left(\left(-y3\right) \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;y2 \leq -2.4 \cdot 10^{+143}:\\ \;\;\;\;-k \cdot \left(\left(y0 \cdot y2\right) \cdot y5\right)\\ \mathbf{elif}\;y2 \leq 1.1 \cdot 10^{+31}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;y2 \leq 1.6 \cdot 10^{+271}:\\ \;\;\;\;-k \cdot \left(\left(y0 \cdot y2\right) \cdot y5\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-y2\right) \cdot y5\right) \cdot \left(\left(-a\right) \cdot t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 23: 20.9% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{if}\;z \leq -4.3 \cdot 10^{+60}:\\ \;\;\;\;\left(c \cdot \left(\left(-y3\right) \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;z \leq -1.38 \cdot 10^{-235}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.4 \cdot 10^{-164}:\\ \;\;\;\;\left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \mathbf{elif}\;z \leq 6.4 \cdot 10^{+156}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \left(\left(-y1\right) \cdot z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* (* k i) y5) y)))
   (if (<= z -4.3e+60)
     (* (* c (* (- y3) z)) y0)
     (if (<= z -1.38e-235)
       t_1
       (if (<= z 3.4e-164)
         (* (* (* x y0) y2) c)
         (if (<= z 6.4e+156) t_1 (* (* i k) (* (- y1) z))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((k * i) * y5) * y;
	double tmp;
	if (z <= -4.3e+60) {
		tmp = (c * (-y3 * z)) * y0;
	} else if (z <= -1.38e-235) {
		tmp = t_1;
	} else if (z <= 3.4e-164) {
		tmp = ((x * y0) * y2) * c;
	} else if (z <= 6.4e+156) {
		tmp = t_1;
	} else {
		tmp = (i * k) * (-y1 * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((k * i) * y5) * y
    if (z <= (-4.3d+60)) then
        tmp = (c * (-y3 * z)) * y0
    else if (z <= (-1.38d-235)) then
        tmp = t_1
    else if (z <= 3.4d-164) then
        tmp = ((x * y0) * y2) * c
    else if (z <= 6.4d+156) then
        tmp = t_1
    else
        tmp = (i * k) * (-y1 * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = ((k * i) * y5) * y;
	double tmp;
	if (z <= -4.3e+60) {
		tmp = (c * (-y3 * z)) * y0;
	} else if (z <= -1.38e-235) {
		tmp = t_1;
	} else if (z <= 3.4e-164) {
		tmp = ((x * y0) * y2) * c;
	} else if (z <= 6.4e+156) {
		tmp = t_1;
	} else {
		tmp = (i * k) * (-y1 * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = ((k * i) * y5) * y
	tmp = 0
	if z <= -4.3e+60:
		tmp = (c * (-y3 * z)) * y0
	elif z <= -1.38e-235:
		tmp = t_1
	elif z <= 3.4e-164:
		tmp = ((x * y0) * y2) * c
	elif z <= 6.4e+156:
		tmp = t_1
	else:
		tmp = (i * k) * (-y1 * z)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(Float64(k * i) * y5) * y)
	tmp = 0.0
	if (z <= -4.3e+60)
		tmp = Float64(Float64(c * Float64(Float64(-y3) * z)) * y0);
	elseif (z <= -1.38e-235)
		tmp = t_1;
	elseif (z <= 3.4e-164)
		tmp = Float64(Float64(Float64(x * y0) * y2) * c);
	elseif (z <= 6.4e+156)
		tmp = t_1;
	else
		tmp = Float64(Float64(i * k) * Float64(Float64(-y1) * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = ((k * i) * y5) * y;
	tmp = 0.0;
	if (z <= -4.3e+60)
		tmp = (c * (-y3 * z)) * y0;
	elseif (z <= -1.38e-235)
		tmp = t_1;
	elseif (z <= 3.4e-164)
		tmp = ((x * y0) * y2) * c;
	elseif (z <= 6.4e+156)
		tmp = t_1;
	else
		tmp = (i * k) * (-y1 * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[z, -4.3e+60], N[(N[(c * N[((-y3) * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], If[LessEqual[z, -1.38e-235], t$95$1, If[LessEqual[z, 3.4e-164], N[(N[(N[(x * y0), $MachinePrecision] * y2), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[z, 6.4e+156], t$95$1, N[(N[(i * k), $MachinePrecision] * N[((-y1) * z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -4.29999999999999971e60

   1. Initial program 23.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 36.3%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 45.4%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   9. Taylor expanded in b around 0

      \[\leadsto \left(-1 \cdot \left(c \cdot \left(y3 \cdot z\right)\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 38.5%

       \[\leadsto \left(\left(-c\right) \cdot \left(y3 \cdot z\right)\right) \cdot y0 \]

   if -4.29999999999999971e60 < z < -1.37999999999999995e-235 or 3.4e-164 < z < 6.40000000000000005e156

   1. Initial program 30.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 35.7%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 28.8%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 20.9%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 27.4%

       \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]

   if -1.37999999999999995e-235 < z < 3.4e-164

   1. Initial program 33.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 46.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 43.1%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(y0, y2, -i \cdot y\right)\right) \cdot c \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(x \cdot \left(y0 \cdot y2\right)\right) \cdot c \]
   10. Step-by-step derivation

   11. Applied rewrites 43.0%

       \[\leadsto \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c \]

   if 6.40000000000000005e156 < z

   1. Initial program 19.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 56.1%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 40.7%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(i \cdot k\right) \cdot \left(-1 \cdot \left(y1 \cdot \color{blue}{z}\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 40.8%

       \[\leadsto \left(i \cdot k\right) \cdot \left(\left(-y1\right) \cdot z\right) \]
3. Recombined 4 regimes into one program.

4. Final simplification 33.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.3 \cdot 10^{+60}:\\ \;\;\;\;\left(c \cdot \left(\left(-y3\right) \cdot z\right)\right) \cdot y0\\ \mathbf{elif}\;z \leq -1.38 \cdot 10^{-235}:\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{elif}\;z \leq 3.4 \cdot 10^{-164}:\\ \;\;\;\;\left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \mathbf{elif}\;z \leq 6.4 \cdot 10^{+156}:\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \left(\left(-y1\right) \cdot z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 24: 32.2% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{+81}:\\ \;\;\;\;\left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c\\ \mathbf{elif}\;z \leq -135000:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \mathbf{elif}\;z \leq 7.5 \cdot 10^{+21}:\\ \;\;\;\;\left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0\\ \mathbf{else}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= z -2.8e+81)
   (* (* z (fma (- y0) y3 (* i t))) c)
   (if (<= z -135000.0)
     (* (* a y3) (fma y1 z (* (- y) y5)))
     (if (<= z 7.5e+21)
       (* (* y2 (fma (- k) y5 (* c x))) y0)
       (* (* y1 z) (fma a y3 (* (- i) k)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (z <= -2.8e+81) {
		tmp = (z * fma(-y0, y3, (i * t))) * c;
	} else if (z <= -135000.0) {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	} else if (z <= 7.5e+21) {
		tmp = (y2 * fma(-k, y5, (c * x))) * y0;
	} else {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (z <= -2.8e+81)
		tmp = Float64(Float64(z * fma(Float64(-y0), y3, Float64(i * t))) * c);
	elseif (z <= -135000.0)
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	elseif (z <= 7.5e+21)
		tmp = Float64(Float64(y2 * fma(Float64(-k), y5, Float64(c * x))) * y0);
	else
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[z, -2.8e+81], N[(N[(z * N[((-y0) * y3 + N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[z, -135000.0], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.5e+21], N[(N[(y2 * N[((-k) * y5 + N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if z < -2.79999999999999995e81

   1. Initial program 20.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 49.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in z around inf

      \[\leadsto \left(z \cdot \left(-1 \cdot \left(y0 \cdot y3\right) + i \cdot t\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 57.2%

      \[\leadsto \left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c \]

   if -2.79999999999999995e81 < z < -135000

   1. Initial program 60.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 70.4%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 60.7%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]

   if -135000 < z < 7.5e21

   1. Initial program 31.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 41.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in j around inf

      \[\leadsto \left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 29.7%

      \[\leadsto \left(j \cdot \mathsf{fma}\left(y3, y5, \left(-b\right) \cdot x\right)\right) \cdot y0 \]

   9. Taylor expanded in y2 around inf

      \[\leadsto \left(y2 \cdot \left(-1 \cdot \left(k \cdot y5\right) + c \cdot x\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 38.1%

       \[\leadsto \left(y2 \cdot \mathsf{fma}\left(-k, y5, c \cdot x\right)\right) \cdot y0 \]

   if 7.5e21 < z

   1. Initial program 22.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 53.3%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 48.3%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 25: 31.7% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.8 \cdot 10^{+81}:\\ \;\;\;\;\left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c\\ \mathbf{elif}\;z \leq -850000000:\\ \;\;\;\;\left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \mathbf{elif}\;z \leq 7.4 \cdot 10^{+21}:\\ \;\;\;\;\left(x \cdot \mathsf{fma}\left(c, y2, \left(-b\right) \cdot j\right)\right) \cdot y0\\ \mathbf{else}:\\ \;\;\;\;\left(y1 \cdot z\right) \cdot \mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= z -2.8e+81)
   (* (* z (fma (- y0) y3 (* i t))) c)
   (if (<= z -850000000.0)
     (* (* a y3) (fma y1 z (* (- y) y5)))
     (if (<= z 7.4e+21)
       (* (* x (fma c y2 (* (- b) j))) y0)
       (* (* y1 z) (fma a y3 (* (- i) k)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (z <= -2.8e+81) {
		tmp = (z * fma(-y0, y3, (i * t))) * c;
	} else if (z <= -850000000.0) {
		tmp = (a * y3) * fma(y1, z, (-y * y5));
	} else if (z <= 7.4e+21) {
		tmp = (x * fma(c, y2, (-b * j))) * y0;
	} else {
		tmp = (y1 * z) * fma(a, y3, (-i * k));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (z <= -2.8e+81)
		tmp = Float64(Float64(z * fma(Float64(-y0), y3, Float64(i * t))) * c);
	elseif (z <= -850000000.0)
		tmp = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)));
	elseif (z <= 7.4e+21)
		tmp = Float64(Float64(x * fma(c, y2, Float64(Float64(-b) * j))) * y0);
	else
		tmp = Float64(Float64(y1 * z) * fma(a, y3, Float64(Float64(-i) * k)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[z, -2.8e+81], N[(N[(z * N[((-y0) * y3 + N[(i * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[z, -850000000.0], N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 7.4e+21], N[(N[(x * N[(c * y2 + N[((-b) * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision], N[(N[(y1 * z), $MachinePrecision] * N[(a * y3 + N[((-i) * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if z < -2.79999999999999995e81

   1. Initial program 20.9%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 49.2%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in z around inf

      \[\leadsto \left(z \cdot \left(-1 \cdot \left(y0 \cdot y3\right) + i \cdot t\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 57.2%

      \[\leadsto \left(z \cdot \mathsf{fma}\left(-y0, y3, i \cdot t\right)\right) \cdot c \]

   if -2.79999999999999995e81 < z < -8.5e8

   1. Initial program 66.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 67.1%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 67.0%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]

   if -8.5e8 < z < 7.4e21

   1. Initial program 31.2%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 40.8%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 14.6%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   9. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(c \cdot y2 - b \cdot j\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 34.0%

       \[\leadsto \left(x \cdot \mathsf{fma}\left(c, y2, \left(-b\right) \cdot j\right)\right) \cdot y0 \]

   if 7.4e21 < z

   1. Initial program 22.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in z around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(z \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(z\right)\right) \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-z\right)} \cdot \left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-z\right) \cdot \color{blue}{\left(\left(t \cdot \left(a \cdot b - c \cdot i\right) + y3 \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - k \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   5. Applied rewrites 53.3%

      \[\leadsto \color{blue}{\left(-z\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right), y3, \mathsf{fma}\left(b, a, \left(-c\right) \cdot i\right) \cdot t\right) - \mathsf{fma}\left(y0, b, \left(-i\right) \cdot y1\right) \cdot k\right)} \]

   6. Taylor expanded in y1 around -inf

      \[\leadsto y1 \cdot \color{blue}{\left(z \cdot \left(a \cdot y3 - i \cdot k\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 48.3%

      \[\leadsto \left(y1 \cdot z\right) \cdot \color{blue}{\mathsf{fma}\left(a, y3, \left(-i\right) \cdot k\right)} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 26: 29.7% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a \cdot y3\right) \cdot \mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)\\ \mathbf{if}\;y3 \leq -140000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y3 \leq 7.2 \cdot 10^{+14}:\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{elif}\;y3 \leq 3.3 \cdot 10^{+144}:\\ \;\;\;\;-k \cdot \left(\left(y0 \cdot y2\right) \cdot y5\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* a y3) (fma y1 z (* (- y) y5)))))
   (if (<= y3 -140000000000.0)
     t_1
     (if (<= y3 7.2e+14)
       (* (* i k) (fma y y5 (* (- y1) z)))
       (if (<= y3 3.3e+144) (- (* k (* (* y0 y2) y5))) t_1)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (a * y3) * fma(y1, z, (-y * y5));
	double tmp;
	if (y3 <= -140000000000.0) {
		tmp = t_1;
	} else if (y3 <= 7.2e+14) {
		tmp = (i * k) * fma(y, y5, (-y1 * z));
	} else if (y3 <= 3.3e+144) {
		tmp = -(k * ((y0 * y2) * y5));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(a * y3) * fma(y1, z, Float64(Float64(-y) * y5)))
	tmp = 0.0
	if (y3 <= -140000000000.0)
		tmp = t_1;
	elseif (y3 <= 7.2e+14)
		tmp = Float64(Float64(i * k) * fma(y, y5, Float64(Float64(-y1) * z)));
	elseif (y3 <= 3.3e+144)
		tmp = Float64(-Float64(k * Float64(Float64(y0 * y2) * y5)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(a * y3), $MachinePrecision] * N[(y1 * z + N[((-y) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y3, -140000000000.0], t$95$1, If[LessEqual[y3, 7.2e+14], N[(N[(i * k), $MachinePrecision] * N[(y * y5 + N[((-y1) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y3, 3.3e+144], (-N[(k * N[(N[(y0 * y2), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision]), t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y3 < -1.4e11 or 3.3e144 < y3

   1. Initial program 24.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 58.9%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in a around -inf

      \[\leadsto a \cdot \color{blue}{\left(y3 \cdot \left(y1 \cdot z - y \cdot y5\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 45.2%

      \[\leadsto \left(a \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(y1, z, \left(-y\right) \cdot y5\right)} \]

   if -1.4e11 < y3 < 7.2e14

   1. Initial program 33.7%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 40.8%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 33.3%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   if 7.2e14 < y3 < 3.3e144

   1. Initial program 19.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 32.9%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y5 around -inf

      \[\leadsto -1 \cdot \color{blue}{\left(y2 \cdot \left(y5 \cdot \left(k \cdot y0 - a \cdot t\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 42.8%

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \mathsf{fma}\left(k, y0, \left(-a\right) \cdot t\right) \]

   9. Taylor expanded in t around 0

      \[\leadsto -k \cdot \left(y0 \cdot \left(y2 \cdot y5\right)\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 36.6%

       \[\leadsto -k \cdot \left(\left(y0 \cdot y2\right) \cdot y5\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 27: 21.7% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(b \cdot \left(k \cdot z\right)\right) \cdot y0\\ t_2 := \left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{if}\;z \leq -360000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq -1.38 \cdot 10^{-235}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;z \leq 3.4 \cdot 10^{-164}:\\ \;\;\;\;\left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{+155}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* b (* k z)) y0)) (t_2 (* (* (* k i) y5) y)))
   (if (<= z -360000000000.0)
     t_1
     (if (<= z -1.38e-235)
       t_2
       (if (<= z 3.4e-164)
         (* (* (* x y0) y2) c)
         (if (<= z 2.2e+155) t_2 t_1))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (b * (k * z)) * y0;
	double t_2 = ((k * i) * y5) * y;
	double tmp;
	if (z <= -360000000000.0) {
		tmp = t_1;
	} else if (z <= -1.38e-235) {
		tmp = t_2;
	} else if (z <= 3.4e-164) {
		tmp = ((x * y0) * y2) * c;
	} else if (z <= 2.2e+155) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (b * (k * z)) * y0
    t_2 = ((k * i) * y5) * y
    if (z <= (-360000000000.0d0)) then
        tmp = t_1
    else if (z <= (-1.38d-235)) then
        tmp = t_2
    else if (z <= 3.4d-164) then
        tmp = ((x * y0) * y2) * c
    else if (z <= 2.2d+155) then
        tmp = t_2
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (b * (k * z)) * y0;
	double t_2 = ((k * i) * y5) * y;
	double tmp;
	if (z <= -360000000000.0) {
		tmp = t_1;
	} else if (z <= -1.38e-235) {
		tmp = t_2;
	} else if (z <= 3.4e-164) {
		tmp = ((x * y0) * y2) * c;
	} else if (z <= 2.2e+155) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (b * (k * z)) * y0
	t_2 = ((k * i) * y5) * y
	tmp = 0
	if z <= -360000000000.0:
		tmp = t_1
	elif z <= -1.38e-235:
		tmp = t_2
	elif z <= 3.4e-164:
		tmp = ((x * y0) * y2) * c
	elif z <= 2.2e+155:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(b * Float64(k * z)) * y0)
	t_2 = Float64(Float64(Float64(k * i) * y5) * y)
	tmp = 0.0
	if (z <= -360000000000.0)
		tmp = t_1;
	elseif (z <= -1.38e-235)
		tmp = t_2;
	elseif (z <= 3.4e-164)
		tmp = Float64(Float64(Float64(x * y0) * y2) * c);
	elseif (z <= 2.2e+155)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = (b * (k * z)) * y0;
	t_2 = ((k * i) * y5) * y;
	tmp = 0.0;
	if (z <= -360000000000.0)
		tmp = t_1;
	elseif (z <= -1.38e-235)
		tmp = t_2;
	elseif (z <= 3.4e-164)
		tmp = ((x * y0) * y2) * c;
	elseif (z <= 2.2e+155)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(b * N[(k * z), $MachinePrecision]), $MachinePrecision] * y0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[z, -360000000000.0], t$95$1, If[LessEqual[z, -1.38e-235], t$95$2, If[LessEqual[z, 3.4e-164], N[(N[(N[(x * y0), $MachinePrecision] * y2), $MachinePrecision] * c), $MachinePrecision], If[LessEqual[z, 2.2e+155], t$95$2, t$95$1]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -3.6e11 or 2.2000000000000002e155 < z

   1. Initial program 25.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y0 around inf

      \[\leadsto \color{blue}{y0 \cdot \left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(y5 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - b \cdot \left(j \cdot x - k \cdot z\right)\right) \cdot y0} \]

   5. Applied rewrites 39.5%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-y5, \mathsf{fma}\left(y2, k, \left(-j\right) \cdot y3\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot c\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot b\right) \cdot y0} \]

   6. Taylor expanded in z around -inf

      \[\leadsto \left(-1 \cdot \left(z \cdot \left(c \cdot y3 - b \cdot k\right)\right)\right) \cdot y0 \]
   7. Step-by-step derivation

   8. Applied rewrites 46.7%

      \[\leadsto \left(\left(-z\right) \cdot \mathsf{fma}\left(c, y3, \left(-b\right) \cdot k\right)\right) \cdot y0 \]

   9. Taylor expanded in b around inf

      \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]
   10. Step-by-step derivation

   11. Applied rewrites 29.8%

       \[\leadsto \left(b \cdot \left(k \cdot z\right)\right) \cdot y0 \]

   if -3.6e11 < z < -1.37999999999999995e-235 or 3.4e-164 < z < 2.2000000000000002e155

   1. Initial program 29.0%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 35.8%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 30.2%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 21.9%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 28.7%

       \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]

   if -1.37999999999999995e-235 < z < 3.4e-164

   1. Initial program 33.3%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 46.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 43.1%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(y0, y2, -i \cdot y\right)\right) \cdot c \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(x \cdot \left(y0 \cdot y2\right)\right) \cdot c \]
   10. Step-by-step derivation

   11. Applied rewrites 43.0%

       \[\leadsto \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 28: 30.7% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;i \leq -3.9 \cdot 10^{-119} \lor \neg \left(i \leq 6.2 \cdot 10^{+94}\right):\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y \cdot y3\right) \cdot \mathsf{fma}\left(c, y4, \left(-a\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (or (<= i -3.9e-119) (not (<= i 6.2e+94)))
   (* (* i k) (fma y y5 (* (- y1) z)))
   (* (* y y3) (fma c y4 (* (- a) y5)))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if ((i <= -3.9e-119) || !(i <= 6.2e+94)) {
		tmp = (i * k) * fma(y, y5, (-y1 * z));
	} else {
		tmp = (y * y3) * fma(c, y4, (-a * y5));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if ((i <= -3.9e-119) || !(i <= 6.2e+94))
		tmp = Float64(Float64(i * k) * fma(y, y5, Float64(Float64(-y1) * z)));
	else
		tmp = Float64(Float64(y * y3) * fma(c, y4, Float64(Float64(-a) * y5)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[Or[LessEqual[i, -3.9e-119], N[Not[LessEqual[i, 6.2e+94]], $MachinePrecision]], N[(N[(i * k), $MachinePrecision] * N[(y * y5 + N[((-y1) * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y * y3), $MachinePrecision] * N[(c * y4 + N[((-a) * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if i < -3.8999999999999999e-119 or 6.19999999999999983e94 < i

   1. Initial program 27.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 48.8%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 41.2%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   if -3.8999999999999999e-119 < i < 6.19999999999999983e94

   1. Initial program 29.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y3 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(y3 \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y3\right)\right) \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-y3\right)} \cdot \left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-y3\right) \cdot \color{blue}{\left(\left(j \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + z \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - y \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]

   5. Applied rewrites 39.3%

      \[\leadsto \color{blue}{\left(-y3\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), j, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot z\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot y\right)} \]

   6. Taylor expanded in y around inf

      \[\leadsto y \cdot \color{blue}{\left(y3 \cdot \left(-1 \cdot \left(a \cdot y5\right) + c \cdot y4\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 33.7%

      \[\leadsto \left(y \cdot y3\right) \cdot \color{blue}{\mathsf{fma}\left(c, y4, -a \cdot y5\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 37.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;i \leq -3.9 \cdot 10^{-119} \lor \neg \left(i \leq 6.2 \cdot 10^{+94}\right):\\ \;\;\;\;\left(i \cdot k\right) \cdot \mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y \cdot y3\right) \cdot \mathsf{fma}\left(c, y4, \left(-a\right) \cdot y5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 29: 22.2% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;k \leq -2.6 \cdot 10^{+186} \lor \neg \left(k \leq 8.2 \cdot 10^{+27}\right):\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (or (<= k -2.6e+186) (not (<= k 8.2e+27)))
   (* (* (* k i) y5) y)
   (* (* (* x y0) y2) c)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if ((k <= -2.6e+186) || !(k <= 8.2e+27)) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = ((x * y0) * y2) * c;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: tmp
    if ((k <= (-2.6d+186)) .or. (.not. (k <= 8.2d+27))) then
        tmp = ((k * i) * y5) * y
    else
        tmp = ((x * y0) * y2) * c
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if ((k <= -2.6e+186) || !(k <= 8.2e+27)) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = ((x * y0) * y2) * c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if (k <= -2.6e+186) or not (k <= 8.2e+27):
		tmp = ((k * i) * y5) * y
	else:
		tmp = ((x * y0) * y2) * c
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if ((k <= -2.6e+186) || !(k <= 8.2e+27))
		tmp = Float64(Float64(Float64(k * i) * y5) * y);
	else
		tmp = Float64(Float64(Float64(x * y0) * y2) * c);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0;
	if ((k <= -2.6e+186) || ~((k <= 8.2e+27)))
		tmp = ((k * i) * y5) * y;
	else
		tmp = ((x * y0) * y2) * c;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[Or[LessEqual[k, -2.6e+186], N[Not[LessEqual[k, 8.2e+27]], $MachinePrecision]], N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision], N[(N[(N[(x * y0), $MachinePrecision] * y2), $MachinePrecision] * c), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if k < -2.6000000000000001e186 or 8.2000000000000005e27 < k

   1. Initial program 24.1%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 42.9%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 50.0%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 37.9%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 44.3%

       \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]

   if -2.6000000000000001e186 < k < 8.2000000000000005e27

   1. Initial program 30.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf

      \[\leadsto \color{blue}{c \cdot \left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(-1 \cdot \left(i \cdot \left(x \cdot y - t \cdot z\right)\right) + y0 \cdot \left(x \cdot y2 - y3 \cdot z\right)\right) - y4 \cdot \left(t \cdot y2 - y \cdot y3\right)\right) \cdot c} \]

   5. Applied rewrites 41.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-i, \mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), \mathsf{fma}\left(y2, x, \left(-y3\right) \cdot z\right) \cdot y0\right) - \mathsf{fma}\left(y2, t, \left(-y\right) \cdot y3\right) \cdot y4\right) \cdot c} \]

   6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot \left(-1 \cdot \left(i \cdot y\right) + y0 \cdot y2\right)\right) \cdot c \]
   7. Step-by-step derivation

   8. Applied rewrites 26.2%

      \[\leadsto \left(x \cdot \mathsf{fma}\left(y0, y2, -i \cdot y\right)\right) \cdot c \]

   9. Taylor expanded in y around 0

      \[\leadsto \left(x \cdot \left(y0 \cdot y2\right)\right) \cdot c \]
   10. Step-by-step derivation

   11. Applied rewrites 18.6%

       \[\leadsto \left(\left(x \cdot y0\right) \cdot y2\right) \cdot c \]
3. Recombined 2 regimes into one program.

4. Final simplification 27.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;k \leq -2.6 \cdot 10^{+186} \lor \neg \left(k \leq 8.2 \cdot 10^{+27}\right):\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(\left(x \cdot y0\right) \cdot y2\right) \cdot c\\ \end{array} \]
5. Add Preprocessing

Alternative 30: 21.8% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;k \leq -6.8 \cdot 10^{-32} \lor \neg \left(k \leq 5.2 \cdot 10^{-105}\right):\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(\left(t \cdot y2\right) \cdot y5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (or (<= k -6.8e-32) (not (<= k 5.2e-105)))
   (* (* (* k i) y5) y)
   (* a (* (* t y2) y5))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if ((k <= -6.8e-32) || !(k <= 5.2e-105)) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = a * ((t * y2) * y5);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: tmp
    if ((k <= (-6.8d-32)) .or. (.not. (k <= 5.2d-105))) then
        tmp = ((k * i) * y5) * y
    else
        tmp = a * ((t * y2) * y5)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if ((k <= -6.8e-32) || !(k <= 5.2e-105)) {
		tmp = ((k * i) * y5) * y;
	} else {
		tmp = a * ((t * y2) * y5);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if (k <= -6.8e-32) or not (k <= 5.2e-105):
		tmp = ((k * i) * y5) * y
	else:
		tmp = a * ((t * y2) * y5)
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if ((k <= -6.8e-32) || !(k <= 5.2e-105))
		tmp = Float64(Float64(Float64(k * i) * y5) * y);
	else
		tmp = Float64(a * Float64(Float64(t * y2) * y5));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0;
	if ((k <= -6.8e-32) || ~((k <= 5.2e-105)))
		tmp = ((k * i) * y5) * y;
	else
		tmp = a * ((t * y2) * y5);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[Or[LessEqual[k, -6.8e-32], N[Not[LessEqual[k, 5.2e-105]], $MachinePrecision]], N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision], N[(a * N[(N[(t * y2), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if k < -6.79999999999999956e-32 or 5.1999999999999997e-105 < k

   1. Initial program 24.8%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 40.3%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 35.0%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 24.4%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 29.2%

       \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]

   if -6.79999999999999956e-32 < k < 5.1999999999999997e-105

   1. Initial program 34.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in y2 around inf

      \[\leadsto \color{blue}{y2 \cdot \left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(k \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + x \cdot \left(c \cdot y0 - a \cdot y1\right)\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) \cdot y2} \]

   5. Applied rewrites 33.1%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(y4, y1, \left(-y0\right) \cdot y5\right), k, \mathsf{fma}\left(y0, c, \left(-a\right) \cdot y1\right) \cdot x\right) - \mathsf{fma}\left(y4, c, \left(-a\right) \cdot y5\right) \cdot t\right) \cdot y2} \]

   6. Taylor expanded in y5 around -inf

      \[\leadsto -1 \cdot \color{blue}{\left(y2 \cdot \left(y5 \cdot \left(k \cdot y0 - a \cdot t\right)\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 25.5%

      \[\leadsto -\left(y2 \cdot y5\right) \cdot \mathsf{fma}\left(k, y0, \left(-a\right) \cdot t\right) \]

   9. Taylor expanded in t around inf

      \[\leadsto a \cdot \left(t \cdot \color{blue}{\left(y2 \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 21.5%

       \[\leadsto a \cdot \left(\left(t \cdot y2\right) \cdot \color{blue}{y5}\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 26.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;k \leq -6.8 \cdot 10^{-32} \lor \neg \left(k \leq 5.2 \cdot 10^{-105}\right):\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(\left(t \cdot y2\right) \cdot y5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 31: 18.2% accurate, 9.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.08 \cdot 10^{-67}:\\ \;\;\;\;\left(\left(y \cdot y5\right) \cdot i\right) \cdot k\\ \mathbf{else}:\\ \;\;\;\;\left(\left(k \cdot i\right) \cdot y5\right) \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y -1.08e-67) (* (* (* y y5) i) k) (* (* (* k i) y5) y)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y <= -1.08e-67) {
		tmp = ((y * y5) * i) * k;
	} else {
		tmp = ((k * i) * y5) * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: tmp
    if (y <= (-1.08d-67)) then
        tmp = ((y * y5) * i) * k
    else
        tmp = ((k * i) * y5) * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double tmp;
	if (y <= -1.08e-67) {
		tmp = ((y * y5) * i) * k;
	} else {
		tmp = ((k * i) * y5) * y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y <= -1.08e-67:
		tmp = ((y * y5) * i) * k
	else:
		tmp = ((k * i) * y5) * y
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0
	if (y <= -1.08e-67)
		tmp = Float64(Float64(Float64(y * y5) * i) * k);
	else
		tmp = Float64(Float64(Float64(k * i) * y5) * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = 0.0;
	if (y <= -1.08e-67)
		tmp = ((y * y5) * i) * k;
	else
		tmp = ((k * i) * y5) * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := If[LessEqual[y, -1.08e-67], N[(N[(N[(y * y5), $MachinePrecision] * i), $MachinePrecision] * k), $MachinePrecision], N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.0800000000000001e-67

   1. Initial program 23.6%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 42.1%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 35.1%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 27.9%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 32.1%

       \[\leadsto \left(\left(y \cdot y5\right) \cdot i\right) \cdot k \]

   if -1.0800000000000001e-67 < y

   1. Initial program 31.4%

      \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   2. Add Preprocessing

   3. Taylor expanded in i around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

      2. distribute-lft-neg-in N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

      5. lower--.f64 N/A

         \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   5. Applied rewrites 38.1%

      \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

   6. Taylor expanded in k around -inf

      \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 22.3%

      \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

   9. Taylor expanded in y around inf

      \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
   10. Step-by-step derivation

   11. Applied rewrites 11.7%

       \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
   12. Step-by-step derivation

   13. Applied rewrites 17.4%

       \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 32: 17.0% accurate, 12.6× speedup

Math

\[\begin{array}{l} \\ \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (* (* (* k i) y5) y))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return ((k * i) * y5) * y;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = ((k * i) * y5) * y
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return ((k * i) * y5) * y;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return ((k * i) * y5) * y

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(Float64(Float64(k * i) * y5) * y)
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = ((k * i) * y5) * y;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(N[(N[(k * i), $MachinePrecision] * y5), $MachinePrecision] * y), $MachinePrecision]

Derivation

1. Initial program 28.5%

   \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
2. Add Preprocessing

3. Taylor expanded in i around -inf

   \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

   2. distribute-lft-neg-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   4. lower-neg.f64 N/A

      \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

   5. lower--.f64 N/A

      \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

5. Applied rewrites 39.6%

   \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

6. Taylor expanded in k around -inf

   \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
7. Step-by-step derivation

8. Applied rewrites 27.0%

   \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

9. Taylor expanded in y around inf

   \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
10. Step-by-step derivation

11. Applied rewrites 17.6%

    \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
12. Step-by-step derivation

13. Applied rewrites 20.5%

    \[\leadsto \left(\left(k \cdot i\right) \cdot y5\right) \cdot y \]
14. Add Preprocessing

Alternative 33: 17.2% accurate, 12.6× speedup

Math

\[\begin{array}{l} \\ \left(\left(i \cdot y\right) \cdot k\right) \cdot y5 \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (* (* (* i y) k) y5))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return ((i * y) * k) * y5;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = ((i * y) * k) * y5
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return ((i * y) * k) * y5;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return ((i * y) * k) * y5

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(Float64(Float64(i * y) * k) * y5)
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = ((i * y) * k) * y5;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(N[(N[(i * y), $MachinePrecision] * k), $MachinePrecision] * y5), $MachinePrecision]

Derivation

1. Initial program 28.5%

   \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
2. Add Preprocessing

3. Taylor expanded in i around -inf

   \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

   2. distribute-lft-neg-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   4. lower-neg.f64 N/A

      \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

   5. lower--.f64 N/A

      \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

5. Applied rewrites 39.6%

   \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

6. Taylor expanded in k around -inf

   \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
7. Step-by-step derivation

8. Applied rewrites 27.0%

   \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

9. Taylor expanded in y around inf

   \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
10. Step-by-step derivation

11. Applied rewrites 17.6%

    \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
12. Step-by-step derivation

13. Applied rewrites 19.8%

    \[\leadsto \left(\left(i \cdot y\right) \cdot k\right) \cdot y5 \]
14. Add Preprocessing

Alternative 34: 17.1% accurate, 12.6× speedup

Math

\[\begin{array}{l} \\ i \cdot \left(\left(k \cdot y5\right) \cdot y\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (* i (* (* k y5) y)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return i * ((k * y5) * y);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = i * ((k * y5) * y)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return i * ((k * y5) * y);
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return i * ((k * y5) * y)

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(i * Float64(Float64(k * y5) * y))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = i * ((k * y5) * y);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(i * N[(N[(k * y5), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 28.5%

   \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
2. Add Preprocessing

3. Taylor expanded in i around -inf

   \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

   2. distribute-lft-neg-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   4. lower-neg.f64 N/A

      \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

   5. lower--.f64 N/A

      \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

5. Applied rewrites 39.6%

   \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

6. Taylor expanded in k around -inf

   \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
7. Step-by-step derivation

8. Applied rewrites 27.0%

   \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

9. Taylor expanded in y around inf

   \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
10. Step-by-step derivation

11. Applied rewrites 17.6%

    \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
12. Step-by-step derivation

13. Applied rewrites 19.1%

    \[\leadsto i \cdot \left(\left(k \cdot y5\right) \cdot y\right) \]
14. Add Preprocessing

Alternative 35: 17.3% accurate, 12.6× speedup

Math

\[\begin{array}{l} \\ i \cdot \left(\left(k \cdot y\right) \cdot y5\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (* i (* (* k y) y5)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return i * ((k * y) * y5);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    code = i * ((k * y) * y5)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	return i * ((k * y) * y5);
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	return i * ((k * y) * y5)

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	return Float64(i * Float64(Float64(k * y) * y5))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	tmp = i * ((k * y) * y5);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(i * N[(N[(k * y), $MachinePrecision] * y5), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 28.5%

   \[\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(x \cdot j - z \cdot k\right) \cdot \left(y0 \cdot b - y1 \cdot i\right)\right) + \left(x \cdot y2 - z \cdot y3\right) \cdot \left(y0 \cdot c - y1 \cdot a\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot \left(y4 \cdot b - y5 \cdot i\right)\right) - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
2. Add Preprocessing

3. Taylor expanded in i around -inf

   \[\leadsto \color{blue}{-1 \cdot \left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(i \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)\right)} \]

   2. distribute-lft-neg-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(i\right)\right) \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

   4. lower-neg.f64 N/A

      \[\leadsto \color{blue}{\left(-i\right)} \cdot \left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right) \]

   5. lower--.f64 N/A

      \[\leadsto \left(-i\right) \cdot \color{blue}{\left(\left(c \cdot \left(x \cdot y - t \cdot z\right) + y5 \cdot \left(j \cdot t - k \cdot y\right)\right) - y1 \cdot \left(j \cdot x - k \cdot z\right)\right)} \]

5. Applied rewrites 39.6%

   \[\leadsto \color{blue}{\left(-i\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(y, x, \left(-t\right) \cdot z\right), c, \mathsf{fma}\left(j, t, \left(-k\right) \cdot y\right) \cdot y5\right) - \mathsf{fma}\left(j, x, \left(-k\right) \cdot z\right) \cdot y1\right)} \]

6. Taylor expanded in k around -inf

   \[\leadsto i \cdot \color{blue}{\left(k \cdot \left(y \cdot y5 - y1 \cdot z\right)\right)} \]
7. Step-by-step derivation

8. Applied rewrites 27.0%

   \[\leadsto \left(i \cdot k\right) \cdot \color{blue}{\mathsf{fma}\left(y, y5, \left(-y1\right) \cdot z\right)} \]

9. Taylor expanded in y around inf

   \[\leadsto i \cdot \left(k \cdot \color{blue}{\left(y \cdot y5\right)}\right) \]
10. Step-by-step derivation

11. Applied rewrites 17.6%

    \[\leadsto i \cdot \left(\left(k \cdot y\right) \cdot \color{blue}{y5}\right) \]
12. Add Preprocessing

Developer Target 1: 27.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y4 \cdot c - y5 \cdot a\\ t_2 := x \cdot y2 - z \cdot y3\\ t_3 := y2 \cdot t - y3 \cdot y\\ t_4 := k \cdot y2 - j \cdot y3\\ t_5 := y4 \cdot b - y5 \cdot i\\ t_6 := \left(j \cdot t - k \cdot y\right) \cdot t\_5\\ t_7 := b \cdot a - i \cdot c\\ t_8 := t\_7 \cdot \left(y \cdot x - t \cdot z\right)\\ t_9 := j \cdot x - k \cdot z\\ t_10 := \left(b \cdot y0 - i \cdot y1\right) \cdot t\_9\\ t_11 := t\_9 \cdot \left(y0 \cdot b - i \cdot y1\right)\\ t_12 := y4 \cdot y1 - y5 \cdot y0\\ t_13 := t\_4 \cdot t\_12\\ t_14 := \left(y2 \cdot k - y3 \cdot j\right) \cdot t\_12\\ t_15 := \left(\left(\left(\left(k \cdot y\right) \cdot \left(y5 \cdot i\right) - \left(y \cdot b\right) \cdot \left(y4 \cdot k\right)\right) - \left(y5 \cdot t\right) \cdot \left(i \cdot j\right)\right) - \left(t\_3 \cdot t\_1 - t\_14\right)\right) + \left(t\_8 - \left(t\_11 - \left(y2 \cdot x - y3 \cdot z\right) \cdot \left(c \cdot y0 - y1 \cdot a\right)\right)\right)\\ t_16 := \left(\left(t\_6 - \left(y3 \cdot y\right) \cdot \left(y5 \cdot a - y4 \cdot c\right)\right) + \left(\left(y5 \cdot a\right) \cdot \left(t \cdot y2\right) + t\_13\right)\right) + \left(t\_2 \cdot \left(c \cdot y0 - a \cdot y1\right) - \left(t\_10 - \left(y \cdot x - z \cdot t\right) \cdot t\_7\right)\right)\\ t_17 := t \cdot y2 - y \cdot y3\\ \mathbf{if}\;y4 < -7.206256231996481 \cdot 10^{+60}:\\ \;\;\;\;\left(t\_8 - \left(t\_11 - t\_6\right)\right) - \left(\frac{t\_3}{\frac{1}{t\_1}} - t\_14\right)\\ \mathbf{elif}\;y4 < -3.364603505246317 \cdot 10^{-66}:\\ \;\;\;\;\left(\left(\left(\left(t \cdot c\right) \cdot \left(i \cdot z\right) - \left(a \cdot t\right) \cdot \left(b \cdot z\right)\right) - \left(y \cdot c\right) \cdot \left(i \cdot x\right)\right) - t\_10\right) + \left(\left(y0 \cdot c - a \cdot y1\right) \cdot t\_2 - \left(t\_17 \cdot \left(y4 \cdot c - a \cdot y5\right) - \left(y1 \cdot y4 - y5 \cdot y0\right) \cdot t\_4\right)\right)\\ \mathbf{elif}\;y4 < -1.2000065055686116 \cdot 10^{-105}:\\ \;\;\;\;t\_16\\ \mathbf{elif}\;y4 < 6.718963124057495 \cdot 10^{-279}:\\ \;\;\;\;t\_15\\ \mathbf{elif}\;y4 < 4.77962681403792 \cdot 10^{-222}:\\ \;\;\;\;t\_16\\ \mathbf{elif}\;y4 < 2.2852241541266835 \cdot 10^{-175}:\\ \;\;\;\;t\_15\\ \mathbf{else}:\\ \;\;\;\;\left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) - \left(k \cdot \left(i \cdot \left(z \cdot y1\right)\right) - \left(j \cdot \left(i \cdot \left(x \cdot y1\right)\right) + y0 \cdot \left(k \cdot \left(z \cdot b\right)\right)\right)\right)\right) + \left(z \cdot \left(y3 \cdot \left(a \cdot y1\right)\right) - \left(y2 \cdot \left(x \cdot \left(a \cdot y1\right)\right) + y0 \cdot \left(z \cdot \left(c \cdot y3\right)\right)\right)\right)\right) + \left(t \cdot j - y \cdot k\right) \cdot t\_5\right) - t\_17 \cdot t\_1\right) + t\_13\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (- (* y4 c) (* y5 a)))
        (t_2 (- (* x y2) (* z y3)))
        (t_3 (- (* y2 t) (* y3 y)))
        (t_4 (- (* k y2) (* j y3)))
        (t_5 (- (* y4 b) (* y5 i)))
        (t_6 (* (- (* j t) (* k y)) t_5))
        (t_7 (- (* b a) (* i c)))
        (t_8 (* t_7 (- (* y x) (* t z))))
        (t_9 (- (* j x) (* k z)))
        (t_10 (* (- (* b y0) (* i y1)) t_9))
        (t_11 (* t_9 (- (* y0 b) (* i y1))))
        (t_12 (- (* y4 y1) (* y5 y0)))
        (t_13 (* t_4 t_12))
        (t_14 (* (- (* y2 k) (* y3 j)) t_12))
        (t_15
         (+
          (-
           (-
            (- (* (* k y) (* y5 i)) (* (* y b) (* y4 k)))
            (* (* y5 t) (* i j)))
           (- (* t_3 t_1) t_14))
          (- t_8 (- t_11 (* (- (* y2 x) (* y3 z)) (- (* c y0) (* y1 a)))))))
        (t_16
         (+
          (+
           (- t_6 (* (* y3 y) (- (* y5 a) (* y4 c))))
           (+ (* (* y5 a) (* t y2)) t_13))
          (-
           (* t_2 (- (* c y0) (* a y1)))
           (- t_10 (* (- (* y x) (* z t)) t_7)))))
        (t_17 (- (* t y2) (* y y3))))
   (if (< y4 -7.206256231996481e+60)
     (- (- t_8 (- t_11 t_6)) (- (/ t_3 (/ 1.0 t_1)) t_14))
     (if (< y4 -3.364603505246317e-66)
       (+
        (-
         (- (- (* (* t c) (* i z)) (* (* a t) (* b z))) (* (* y c) (* i x)))
         t_10)
        (-
         (* (- (* y0 c) (* a y1)) t_2)
         (- (* t_17 (- (* y4 c) (* a y5))) (* (- (* y1 y4) (* y5 y0)) t_4))))
       (if (< y4 -1.2000065055686116e-105)
         t_16
         (if (< y4 6.718963124057495e-279)
           t_15
           (if (< y4 4.77962681403792e-222)
             t_16
             (if (< y4 2.2852241541266835e-175)
               t_15
               (+
                (-
                 (+
                  (+
                   (-
                    (* (- (* x y) (* z t)) (- (* a b) (* c i)))
                    (-
                     (* k (* i (* z y1)))
                     (+ (* j (* i (* x y1))) (* y0 (* k (* z b))))))
                   (-
                    (* z (* y3 (* a y1)))
                    (+ (* y2 (* x (* a y1))) (* y0 (* z (* c y3))))))
                  (* (- (* t j) (* y k)) t_5))
                 (* t_17 t_1))
                t_13)))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * c) - (y5 * a);
	double t_2 = (x * y2) - (z * y3);
	double t_3 = (y2 * t) - (y3 * y);
	double t_4 = (k * y2) - (j * y3);
	double t_5 = (y4 * b) - (y5 * i);
	double t_6 = ((j * t) - (k * y)) * t_5;
	double t_7 = (b * a) - (i * c);
	double t_8 = t_7 * ((y * x) - (t * z));
	double t_9 = (j * x) - (k * z);
	double t_10 = ((b * y0) - (i * y1)) * t_9;
	double t_11 = t_9 * ((y0 * b) - (i * y1));
	double t_12 = (y4 * y1) - (y5 * y0);
	double t_13 = t_4 * t_12;
	double t_14 = ((y2 * k) - (y3 * j)) * t_12;
	double t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))));
	double t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)));
	double t_17 = (t * y2) - (y * y3);
	double tmp;
	if (y4 < -7.206256231996481e+60) {
		tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0 / t_1)) - t_14);
	} else if (y4 < -3.364603505246317e-66) {
		tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)));
	} else if (y4 < -1.2000065055686116e-105) {
		tmp = t_16;
	} else if (y4 < 6.718963124057495e-279) {
		tmp = t_15;
	} else if (y4 < 4.77962681403792e-222) {
		tmp = t_16;
	} else if (y4 < 2.2852241541266835e-175) {
		tmp = t_15;
	} else {
		tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: i
    real(8), intent (in) :: j
    real(8), intent (in) :: k
    real(8), intent (in) :: y0
    real(8), intent (in) :: y1
    real(8), intent (in) :: y2
    real(8), intent (in) :: y3
    real(8), intent (in) :: y4
    real(8), intent (in) :: y5
    real(8) :: t_1
    real(8) :: t_10
    real(8) :: t_11
    real(8) :: t_12
    real(8) :: t_13
    real(8) :: t_14
    real(8) :: t_15
    real(8) :: t_16
    real(8) :: t_17
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: t_7
    real(8) :: t_8
    real(8) :: t_9
    real(8) :: tmp
    t_1 = (y4 * c) - (y5 * a)
    t_2 = (x * y2) - (z * y3)
    t_3 = (y2 * t) - (y3 * y)
    t_4 = (k * y2) - (j * y3)
    t_5 = (y4 * b) - (y5 * i)
    t_6 = ((j * t) - (k * y)) * t_5
    t_7 = (b * a) - (i * c)
    t_8 = t_7 * ((y * x) - (t * z))
    t_9 = (j * x) - (k * z)
    t_10 = ((b * y0) - (i * y1)) * t_9
    t_11 = t_9 * ((y0 * b) - (i * y1))
    t_12 = (y4 * y1) - (y5 * y0)
    t_13 = t_4 * t_12
    t_14 = ((y2 * k) - (y3 * j)) * t_12
    t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))))
    t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)))
    t_17 = (t * y2) - (y * y3)
    if (y4 < (-7.206256231996481d+60)) then
        tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0d0 / t_1)) - t_14)
    else if (y4 < (-3.364603505246317d-66)) then
        tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)))
    else if (y4 < (-1.2000065055686116d-105)) then
        tmp = t_16
    else if (y4 < 6.718963124057495d-279) then
        tmp = t_15
    else if (y4 < 4.77962681403792d-222) then
        tmp = t_16
    else if (y4 < 2.2852241541266835d-175) then
        tmp = t_15
    else
        tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k, double y0, double y1, double y2, double y3, double y4, double y5) {
	double t_1 = (y4 * c) - (y5 * a);
	double t_2 = (x * y2) - (z * y3);
	double t_3 = (y2 * t) - (y3 * y);
	double t_4 = (k * y2) - (j * y3);
	double t_5 = (y4 * b) - (y5 * i);
	double t_6 = ((j * t) - (k * y)) * t_5;
	double t_7 = (b * a) - (i * c);
	double t_8 = t_7 * ((y * x) - (t * z));
	double t_9 = (j * x) - (k * z);
	double t_10 = ((b * y0) - (i * y1)) * t_9;
	double t_11 = t_9 * ((y0 * b) - (i * y1));
	double t_12 = (y4 * y1) - (y5 * y0);
	double t_13 = t_4 * t_12;
	double t_14 = ((y2 * k) - (y3 * j)) * t_12;
	double t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))));
	double t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)));
	double t_17 = (t * y2) - (y * y3);
	double tmp;
	if (y4 < -7.206256231996481e+60) {
		tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0 / t_1)) - t_14);
	} else if (y4 < -3.364603505246317e-66) {
		tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)));
	} else if (y4 < -1.2000065055686116e-105) {
		tmp = t_16;
	} else if (y4 < 6.718963124057495e-279) {
		tmp = t_15;
	} else if (y4 < 4.77962681403792e-222) {
		tmp = t_16;
	} else if (y4 < 2.2852241541266835e-175) {
		tmp = t_15;
	} else {
		tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (y4 * c) - (y5 * a)
	t_2 = (x * y2) - (z * y3)
	t_3 = (y2 * t) - (y3 * y)
	t_4 = (k * y2) - (j * y3)
	t_5 = (y4 * b) - (y5 * i)
	t_6 = ((j * t) - (k * y)) * t_5
	t_7 = (b * a) - (i * c)
	t_8 = t_7 * ((y * x) - (t * z))
	t_9 = (j * x) - (k * z)
	t_10 = ((b * y0) - (i * y1)) * t_9
	t_11 = t_9 * ((y0 * b) - (i * y1))
	t_12 = (y4 * y1) - (y5 * y0)
	t_13 = t_4 * t_12
	t_14 = ((y2 * k) - (y3 * j)) * t_12
	t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))))
	t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)))
	t_17 = (t * y2) - (y * y3)
	tmp = 0
	if y4 < -7.206256231996481e+60:
		tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0 / t_1)) - t_14)
	elif y4 < -3.364603505246317e-66:
		tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)))
	elif y4 < -1.2000065055686116e-105:
		tmp = t_16
	elif y4 < 6.718963124057495e-279:
		tmp = t_15
	elif y4 < 4.77962681403792e-222:
		tmp = t_16
	elif y4 < 2.2852241541266835e-175:
		tmp = t_15
	else:
		tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(Float64(y4 * c) - Float64(y5 * a))
	t_2 = Float64(Float64(x * y2) - Float64(z * y3))
	t_3 = Float64(Float64(y2 * t) - Float64(y3 * y))
	t_4 = Float64(Float64(k * y2) - Float64(j * y3))
	t_5 = Float64(Float64(y4 * b) - Float64(y5 * i))
	t_6 = Float64(Float64(Float64(j * t) - Float64(k * y)) * t_5)
	t_7 = Float64(Float64(b * a) - Float64(i * c))
	t_8 = Float64(t_7 * Float64(Float64(y * x) - Float64(t * z)))
	t_9 = Float64(Float64(j * x) - Float64(k * z))
	t_10 = Float64(Float64(Float64(b * y0) - Float64(i * y1)) * t_9)
	t_11 = Float64(t_9 * Float64(Float64(y0 * b) - Float64(i * y1)))
	t_12 = Float64(Float64(y4 * y1) - Float64(y5 * y0))
	t_13 = Float64(t_4 * t_12)
	t_14 = Float64(Float64(Float64(y2 * k) - Float64(y3 * j)) * t_12)
	t_15 = Float64(Float64(Float64(Float64(Float64(Float64(k * y) * Float64(y5 * i)) - Float64(Float64(y * b) * Float64(y4 * k))) - Float64(Float64(y5 * t) * Float64(i * j))) - Float64(Float64(t_3 * t_1) - t_14)) + Float64(t_8 - Float64(t_11 - Float64(Float64(Float64(y2 * x) - Float64(y3 * z)) * Float64(Float64(c * y0) - Float64(y1 * a))))))
	t_16 = Float64(Float64(Float64(t_6 - Float64(Float64(y3 * y) * Float64(Float64(y5 * a) - Float64(y4 * c)))) + Float64(Float64(Float64(y5 * a) * Float64(t * y2)) + t_13)) + Float64(Float64(t_2 * Float64(Float64(c * y0) - Float64(a * y1))) - Float64(t_10 - Float64(Float64(Float64(y * x) - Float64(z * t)) * t_7))))
	t_17 = Float64(Float64(t * y2) - Float64(y * y3))
	tmp = 0.0
	if (y4 < -7.206256231996481e+60)
		tmp = Float64(Float64(t_8 - Float64(t_11 - t_6)) - Float64(Float64(t_3 / Float64(1.0 / t_1)) - t_14));
	elseif (y4 < -3.364603505246317e-66)
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(t * c) * Float64(i * z)) - Float64(Float64(a * t) * Float64(b * z))) - Float64(Float64(y * c) * Float64(i * x))) - t_10) + Float64(Float64(Float64(Float64(y0 * c) - Float64(a * y1)) * t_2) - Float64(Float64(t_17 * Float64(Float64(y4 * c) - Float64(a * y5))) - Float64(Float64(Float64(y1 * y4) - Float64(y5 * y0)) * t_4))));
	elseif (y4 < -1.2000065055686116e-105)
		tmp = t_16;
	elseif (y4 < 6.718963124057495e-279)
		tmp = t_15;
	elseif (y4 < 4.77962681403792e-222)
		tmp = t_16;
	elseif (y4 < 2.2852241541266835e-175)
		tmp = t_15;
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) - Float64(Float64(k * Float64(i * Float64(z * y1))) - Float64(Float64(j * Float64(i * Float64(x * y1))) + Float64(y0 * Float64(k * Float64(z * b)))))) + Float64(Float64(z * Float64(y3 * Float64(a * y1))) - Float64(Float64(y2 * Float64(x * Float64(a * y1))) + Float64(y0 * Float64(z * Float64(c * y3)))))) + Float64(Float64(Float64(t * j) - Float64(y * k)) * t_5)) - Float64(t_17 * t_1)) + t_13);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = (y4 * c) - (y5 * a);
	t_2 = (x * y2) - (z * y3);
	t_3 = (y2 * t) - (y3 * y);
	t_4 = (k * y2) - (j * y3);
	t_5 = (y4 * b) - (y5 * i);
	t_6 = ((j * t) - (k * y)) * t_5;
	t_7 = (b * a) - (i * c);
	t_8 = t_7 * ((y * x) - (t * z));
	t_9 = (j * x) - (k * z);
	t_10 = ((b * y0) - (i * y1)) * t_9;
	t_11 = t_9 * ((y0 * b) - (i * y1));
	t_12 = (y4 * y1) - (y5 * y0);
	t_13 = t_4 * t_12;
	t_14 = ((y2 * k) - (y3 * j)) * t_12;
	t_15 = (((((k * y) * (y5 * i)) - ((y * b) * (y4 * k))) - ((y5 * t) * (i * j))) - ((t_3 * t_1) - t_14)) + (t_8 - (t_11 - (((y2 * x) - (y3 * z)) * ((c * y0) - (y1 * a)))));
	t_16 = ((t_6 - ((y3 * y) * ((y5 * a) - (y4 * c)))) + (((y5 * a) * (t * y2)) + t_13)) + ((t_2 * ((c * y0) - (a * y1))) - (t_10 - (((y * x) - (z * t)) * t_7)));
	t_17 = (t * y2) - (y * y3);
	tmp = 0.0;
	if (y4 < -7.206256231996481e+60)
		tmp = (t_8 - (t_11 - t_6)) - ((t_3 / (1.0 / t_1)) - t_14);
	elseif (y4 < -3.364603505246317e-66)
		tmp = (((((t * c) * (i * z)) - ((a * t) * (b * z))) - ((y * c) * (i * x))) - t_10) + ((((y0 * c) - (a * y1)) * t_2) - ((t_17 * ((y4 * c) - (a * y5))) - (((y1 * y4) - (y5 * y0)) * t_4)));
	elseif (y4 < -1.2000065055686116e-105)
		tmp = t_16;
	elseif (y4 < 6.718963124057495e-279)
		tmp = t_15;
	elseif (y4 < 4.77962681403792e-222)
		tmp = t_16;
	elseif (y4 < 2.2852241541266835e-175)
		tmp = t_15;
	else
		tmp = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) - ((k * (i * (z * y1))) - ((j * (i * (x * y1))) + (y0 * (k * (z * b)))))) + ((z * (y3 * (a * y1))) - ((y2 * (x * (a * y1))) + (y0 * (z * (c * y3)))))) + (((t * j) - (y * k)) * t_5)) - (t_17 * t_1)) + t_13;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := Block[{t$95$1 = N[(N[(y4 * c), $MachinePrecision] - N[(y5 * a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(y2 * t), $MachinePrecision] - N[(y3 * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(y4 * b), $MachinePrecision] - N[(y5 * i), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(N[(N[(j * t), $MachinePrecision] - N[(k * y), $MachinePrecision]), $MachinePrecision] * t$95$5), $MachinePrecision]}, Block[{t$95$7 = N[(N[(b * a), $MachinePrecision] - N[(i * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$8 = N[(t$95$7 * N[(N[(y * x), $MachinePrecision] - N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$9 = N[(N[(j * x), $MachinePrecision] - N[(k * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$10 = N[(N[(N[(b * y0), $MachinePrecision] - N[(i * y1), $MachinePrecision]), $MachinePrecision] * t$95$9), $MachinePrecision]}, Block[{t$95$11 = N[(t$95$9 * N[(N[(y0 * b), $MachinePrecision] - N[(i * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$12 = N[(N[(y4 * y1), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$13 = N[(t$95$4 * t$95$12), $MachinePrecision]}, Block[{t$95$14 = N[(N[(N[(y2 * k), $MachinePrecision] - N[(y3 * j), $MachinePrecision]), $MachinePrecision] * t$95$12), $MachinePrecision]}, Block[{t$95$15 = N[(N[(N[(N[(N[(N[(k * y), $MachinePrecision] * N[(y5 * i), $MachinePrecision]), $MachinePrecision] - N[(N[(y * b), $MachinePrecision] * N[(y4 * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(y5 * t), $MachinePrecision] * N[(i * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(t$95$3 * t$95$1), $MachinePrecision] - t$95$14), $MachinePrecision]), $MachinePrecision] + N[(t$95$8 - N[(t$95$11 - N[(N[(N[(y2 * x), $MachinePrecision] - N[(y3 * z), $MachinePrecision]), $MachinePrecision] * N[(N[(c * y0), $MachinePrecision] - N[(y1 * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$16 = N[(N[(N[(t$95$6 - N[(N[(y3 * y), $MachinePrecision] * N[(N[(y5 * a), $MachinePrecision] - N[(y4 * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(y5 * a), $MachinePrecision] * N[(t * y2), $MachinePrecision]), $MachinePrecision] + t$95$13), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$2 * N[(N[(c * y0), $MachinePrecision] - N[(a * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(t$95$10 - N[(N[(N[(y * x), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * t$95$7), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$17 = N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision]}, If[Less[y4, -7.206256231996481e+60], N[(N[(t$95$8 - N[(t$95$11 - t$95$6), $MachinePrecision]), $MachinePrecision] - N[(N[(t$95$3 / N[(1.0 / t$95$1), $MachinePrecision]), $MachinePrecision] - t$95$14), $MachinePrecision]), $MachinePrecision], If[Less[y4, -3.364603505246317e-66], N[(N[(N[(N[(N[(N[(t * c), $MachinePrecision] * N[(i * z), $MachinePrecision]), $MachinePrecision] - N[(N[(a * t), $MachinePrecision] * N[(b * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(y * c), $MachinePrecision] * N[(i * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$10), $MachinePrecision] + N[(N[(N[(N[(y0 * c), $MachinePrecision] - N[(a * y1), $MachinePrecision]), $MachinePrecision] * t$95$2), $MachinePrecision] - N[(N[(t$95$17 * N[(N[(y4 * c), $MachinePrecision] - N[(a * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(y1 * y4), $MachinePrecision] - N[(y5 * y0), $MachinePrecision]), $MachinePrecision] * t$95$4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Less[y4, -1.2000065055686116e-105], t$95$16, If[Less[y4, 6.718963124057495e-279], t$95$15, If[Less[y4, 4.77962681403792e-222], t$95$16, If[Less[y4, 2.2852241541266835e-175], t$95$15, N[(N[(N[(N[(N[(N[(N[(N[(x * y), $MachinePrecision] - N[(z * t), $MachinePrecision]), $MachinePrecision] * N[(N[(a * b), $MachinePrecision] - N[(c * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(k * N[(i * N[(z * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(j * N[(i * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y0 * N[(k * N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(z * N[(y3 * N[(a * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(y2 * N[(x * N[(a * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y0 * N[(z * N[(c * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision] * t$95$5), $MachinePrecision]), $MachinePrecision] - N[(t$95$17 * t$95$1), $MachinePrecision]), $MachinePrecision] + t$95$13), $MachinePrecision]]]]]]]]]]]]]]]]]]]]]]]]

Reproduce

herbie shell --seed 2024337 
(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
  :name "Linear.Matrix:det44 from linear-1.19.1.3"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< y4 -7206256231996481000000000000000000000000000000000000000000000) (- (- (* (- (* b a) (* i c)) (- (* y x) (* t z))) (- (* (- (* j x) (* k z)) (- (* y0 b) (* i y1))) (* (- (* j t) (* k y)) (- (* y4 b) (* y5 i))))) (- (/ (- (* y2 t) (* y3 y)) (/ 1 (- (* y4 c) (* y5 a)))) (* (- (* y2 k) (* y3 j)) (- (* y4 y1) (* y5 y0))))) (if (< y4 -3364603505246317/1000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (- (- (- (* (* t c) (* i z)) (* (* a t) (* b z))) (* (* y c) (* i x))) (* (- (* b y0) (* i y1)) (- (* j x) (* k z)))) (- (* (- (* y0 c) (* a y1)) (- (* x y2) (* z y3))) (- (* (- (* t y2) (* y y3)) (- (* y4 c) (* a y5))) (* (- (* y1 y4) (* y5 y0)) (- (* k y2) (* j y3)))))) (if (< y4 -3000016263921529/2500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (+ (- (* (- (* j t) (* k y)) (- (* y4 b) (* y5 i))) (* (* y3 y) (- (* y5 a) (* y4 c)))) (+ (* (* y5 a) (* t y2)) (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0))))) (- (* (- (* x y2) (* z y3)) (- (* c y0) (* a y1))) (- (* (- (* b y0) (* i y1)) (- (* j x) (* k z))) (* (- (* y x) (* z t)) (- (* b a) (* i c)))))) (if (< y4 1343792624811499/200000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (- (- (- (* (* k y) (* y5 i)) (* (* y b) (* y4 k))) (* (* y5 t) (* i j))) (- (* (- (* y2 t) (* y3 y)) (- (* y4 c) (* y5 a))) (* (- (* y2 k) (* y3 j)) (- (* y4 y1) (* y5 y0))))) (- (* (- (* b a) (* i c)) (- (* y x) (* t z))) (- (* (- (* j x) (* k z)) (- (* y0 b) (* i y1))) (* (- (* y2 x) (* y3 z)) (- (* c y0) (* y1 a)))))) (if (< y4 29872667587737/6250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (+ (- (* (- (* j t) (* k y)) (- (* y4 b) (* y5 i))) (* (* y3 y) (- (* y5 a) (* y4 c)))) (+ (* (* y5 a) (* t y2)) (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0))))) (- (* (- (* x y2) (* z y3)) (- (* c y0) (* a y1))) (- (* (- (* b y0) (* i y1)) (- (* j x) (* k z))) (* (- (* y x) (* z t)) (- (* b a) (* i c)))))) (if (< y4 4570448308253367/20000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (+ (- (- (- (* (* k y) (* y5 i)) (* (* y b) (* y4 k))) (* (* y5 t) (* i j))) (- (* (- (* y2 t) (* y3 y)) (- (* y4 c) (* y5 a))) (* (- (* y2 k) (* y3 j)) (- (* y4 y1) (* y5 y0))))) (- (* (- (* b a) (* i c)) (- (* y x) (* t z))) (- (* (- (* j x) (* k z)) (- (* y0 b) (* i y1))) (* (- (* y2 x) (* y3 z)) (- (* c y0) (* y1 a)))))) (+ (- (+ (+ (- (* (- (* x y) (* z t)) (- (* a b) (* c i))) (- (* k (* i (* z y1))) (+ (* j (* i (* x y1))) (* y0 (* k (* z b)))))) (- (* z (* y3 (* a y1))) (+ (* y2 (* x (* a y1))) (* y0 (* z (* c y3)))))) (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i)))) (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a)))) (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0)))))))))))

  (+ (- (+ (+ (- (* (- (* x y) (* z t)) (- (* a b) (* c i))) (* (- (* x j) (* z k)) (- (* y0 b) (* y1 i)))) (* (- (* x y2) (* z y3)) (- (* y0 c) (* y1 a)))) (* (- (* t j) (* y k)) (- (* y4 b) (* y5 i)))) (* (- (* t y2) (* y y3)) (- (* y4 c) (* y5 a)))) (* (- (* k y2) (* j y3)) (- (* y4 y1) (* y5 y0)))))