Linear.Matrix:det44 from linear-1.19.1.3

Percentage Accurate: 30.4% → 42.4%

Time: 51.8s

Alternatives: 38

Speedup: 4.5×

• 88.6% 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: 30.4% 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: 42.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y1 \cdot y4 - y0 \cdot y5\\ t_2 := b \cdot y4 - i \cdot y5\\ t_3 := j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot t\_2\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ t_4 := a \cdot y5 - c \cdot y4\\ t_5 := k \cdot y2 - j \cdot y3\\ t_6 := t\_5 \cdot t\_1\\ \mathbf{if}\;y4 \leq -1.5 \cdot 10^{+144}:\\ \;\;\;\;y4 \cdot \left(y \cdot \left(c \cdot y3 - b \cdot k\right) + y1 \cdot t\_5\right)\\ \mathbf{elif}\;y4 \leq -9.5 \cdot 10^{-10}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;y4 \leq -3.1 \cdot 10^{-83}:\\ \;\;\;\;t\_6 + y2 \cdot \left(x \cdot \left(c \cdot y0 - a \cdot y1\right) + t \cdot t\_4\right)\\ \mathbf{elif}\;y4 \leq 1.6 \cdot 10^{-190}:\\ \;\;\;\;t\_6 + t \cdot \left(\left(z \cdot \left(c \cdot i - a \cdot b\right) + j \cdot t\_2\right) + y2 \cdot t\_4\right)\\ \mathbf{elif}\;y4 \leq 1.6 \cdot 10^{-99}:\\ \;\;\;\;k \cdot \left(\left(y \cdot \left(i \cdot y5 - b \cdot y4\right) + y2 \cdot t\_1\right) + z \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\\ \mathbf{elif}\;y4 \leq 1.15 \cdot 10^{+91}:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (- (* y1 y4) (* y0 y5)))
        (t_2 (- (* b y4) (* i y5)))
        (t_3
         (*
          j
          (+
           (+ (* y3 (- (* y0 y5) (* y1 y4))) (* t t_2))
           (* x (- (* i y1) (* b y0))))))
        (t_4 (- (* a y5) (* c y4)))
        (t_5 (- (* k y2) (* j y3)))
        (t_6 (* t_5 t_1)))
   (if (<= y4 -1.5e+144)
     (* y4 (+ (* y (- (* c y3) (* b k))) (* y1 t_5)))
     (if (<= y4 -9.5e-10)
       t_3
       (if (<= y4 -3.1e-83)
         (+ t_6 (* y2 (+ (* x (- (* c y0) (* a y1))) (* t t_4))))
         (if (<= y4 1.6e-190)
           (+ t_6 (* t (+ (+ (* z (- (* c i) (* a b))) (* j t_2)) (* y2 t_4))))
           (if (<= y4 1.6e-99)
             (*
              k
              (+
               (+ (* y (- (* i y5) (* b y4))) (* y2 t_1))
               (* z (- (* b y0) (* i y1)))))
             (if (<= y4 1.15e+91)
               t_3
               (*
                y4
                (-
                 (* c (- (* y y3) (* t y2)))
                 (+
                  (* b (- (* y k) (* t j)))
                  (* y1 (- (* j y3) (* k y2))))))))))))))

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 * y4) - (y0 * y5);
	double t_2 = (b * y4) - (i * y5);
	double t_3 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_2)) + (x * ((i * y1) - (b * y0))));
	double t_4 = (a * y5) - (c * y4);
	double t_5 = (k * y2) - (j * y3);
	double t_6 = t_5 * t_1;
	double tmp;
	if (y4 <= -1.5e+144) {
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * t_5));
	} else if (y4 <= -9.5e-10) {
		tmp = t_3;
	} else if (y4 <= -3.1e-83) {
		tmp = t_6 + (y2 * ((x * ((c * y0) - (a * y1))) + (t * t_4)));
	} else if (y4 <= 1.6e-190) {
		tmp = t_6 + (t * (((z * ((c * i) - (a * b))) + (j * t_2)) + (y2 * t_4)));
	} else if (y4 <= 1.6e-99) {
		tmp = k * (((y * ((i * y5) - (b * y4))) + (y2 * t_1)) + (z * ((b * y0) - (i * y1))));
	} else if (y4 <= 1.15e+91) {
		tmp = t_3;
	} else {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))));
	}
	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) :: t_3
    real(8) :: t_4
    real(8) :: t_5
    real(8) :: t_6
    real(8) :: tmp
    t_1 = (y1 * y4) - (y0 * y5)
    t_2 = (b * y4) - (i * y5)
    t_3 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_2)) + (x * ((i * y1) - (b * y0))))
    t_4 = (a * y5) - (c * y4)
    t_5 = (k * y2) - (j * y3)
    t_6 = t_5 * t_1
    if (y4 <= (-1.5d+144)) then
        tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * t_5))
    else if (y4 <= (-9.5d-10)) then
        tmp = t_3
    else if (y4 <= (-3.1d-83)) then
        tmp = t_6 + (y2 * ((x * ((c * y0) - (a * y1))) + (t * t_4)))
    else if (y4 <= 1.6d-190) then
        tmp = t_6 + (t * (((z * ((c * i) - (a * b))) + (j * t_2)) + (y2 * t_4)))
    else if (y4 <= 1.6d-99) then
        tmp = k * (((y * ((i * y5) - (b * y4))) + (y2 * t_1)) + (z * ((b * y0) - (i * y1))))
    else if (y4 <= 1.15d+91) then
        tmp = t_3
    else
        tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))))
    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 = (y1 * y4) - (y0 * y5);
	double t_2 = (b * y4) - (i * y5);
	double t_3 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_2)) + (x * ((i * y1) - (b * y0))));
	double t_4 = (a * y5) - (c * y4);
	double t_5 = (k * y2) - (j * y3);
	double t_6 = t_5 * t_1;
	double tmp;
	if (y4 <= -1.5e+144) {
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * t_5));
	} else if (y4 <= -9.5e-10) {
		tmp = t_3;
	} else if (y4 <= -3.1e-83) {
		tmp = t_6 + (y2 * ((x * ((c * y0) - (a * y1))) + (t * t_4)));
	} else if (y4 <= 1.6e-190) {
		tmp = t_6 + (t * (((z * ((c * i) - (a * b))) + (j * t_2)) + (y2 * t_4)));
	} else if (y4 <= 1.6e-99) {
		tmp = k * (((y * ((i * y5) - (b * y4))) + (y2 * t_1)) + (z * ((b * y0) - (i * y1))));
	} else if (y4 <= 1.15e+91) {
		tmp = t_3;
	} else {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (y1 * y4) - (y0 * y5)
	t_2 = (b * y4) - (i * y5)
	t_3 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_2)) + (x * ((i * y1) - (b * y0))))
	t_4 = (a * y5) - (c * y4)
	t_5 = (k * y2) - (j * y3)
	t_6 = t_5 * t_1
	tmp = 0
	if y4 <= -1.5e+144:
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * t_5))
	elif y4 <= -9.5e-10:
		tmp = t_3
	elif y4 <= -3.1e-83:
		tmp = t_6 + (y2 * ((x * ((c * y0) - (a * y1))) + (t * t_4)))
	elif y4 <= 1.6e-190:
		tmp = t_6 + (t * (((z * ((c * i) - (a * b))) + (j * t_2)) + (y2 * t_4)))
	elif y4 <= 1.6e-99:
		tmp = k * (((y * ((i * y5) - (b * y4))) + (y2 * t_1)) + (z * ((b * y0) - (i * y1))))
	elif y4 <= 1.15e+91:
		tmp = t_3
	else:
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))))
	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 * y4) - Float64(y0 * y5))
	t_2 = Float64(Float64(b * y4) - Float64(i * y5))
	t_3 = Float64(j * Float64(Float64(Float64(y3 * Float64(Float64(y0 * y5) - Float64(y1 * y4))) + Float64(t * t_2)) + Float64(x * Float64(Float64(i * y1) - Float64(b * y0)))))
	t_4 = Float64(Float64(a * y5) - Float64(c * y4))
	t_5 = Float64(Float64(k * y2) - Float64(j * y3))
	t_6 = Float64(t_5 * t_1)
	tmp = 0.0
	if (y4 <= -1.5e+144)
		tmp = Float64(y4 * Float64(Float64(y * Float64(Float64(c * y3) - Float64(b * k))) + Float64(y1 * t_5)));
	elseif (y4 <= -9.5e-10)
		tmp = t_3;
	elseif (y4 <= -3.1e-83)
		tmp = Float64(t_6 + Float64(y2 * Float64(Float64(x * Float64(Float64(c * y0) - Float64(a * y1))) + Float64(t * t_4))));
	elseif (y4 <= 1.6e-190)
		tmp = Float64(t_6 + Float64(t * Float64(Float64(Float64(z * Float64(Float64(c * i) - Float64(a * b))) + Float64(j * t_2)) + Float64(y2 * t_4))));
	elseif (y4 <= 1.6e-99)
		tmp = Float64(k * Float64(Float64(Float64(y * Float64(Float64(i * y5) - Float64(b * y4))) + Float64(y2 * t_1)) + Float64(z * Float64(Float64(b * y0) - Float64(i * y1)))));
	elseif (y4 <= 1.15e+91)
		tmp = t_3;
	else
		tmp = Float64(y4 * Float64(Float64(c * Float64(Float64(y * y3) - Float64(t * y2))) - Float64(Float64(b * Float64(Float64(y * k) - Float64(t * j))) + Float64(y1 * Float64(Float64(j * y3) - Float64(k * y2))))));
	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 = (y1 * y4) - (y0 * y5);
	t_2 = (b * y4) - (i * y5);
	t_3 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_2)) + (x * ((i * y1) - (b * y0))));
	t_4 = (a * y5) - (c * y4);
	t_5 = (k * y2) - (j * y3);
	t_6 = t_5 * t_1;
	tmp = 0.0;
	if (y4 <= -1.5e+144)
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * t_5));
	elseif (y4 <= -9.5e-10)
		tmp = t_3;
	elseif (y4 <= -3.1e-83)
		tmp = t_6 + (y2 * ((x * ((c * y0) - (a * y1))) + (t * t_4)));
	elseif (y4 <= 1.6e-190)
		tmp = t_6 + (t * (((z * ((c * i) - (a * b))) + (j * t_2)) + (y2 * t_4)));
	elseif (y4 <= 1.6e-99)
		tmp = k * (((y * ((i * y5) - (b * y4))) + (y2 * t_1)) + (z * ((b * y0) - (i * y1))));
	elseif (y4 <= 1.15e+91)
		tmp = t_3;
	else
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))));
	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[(y1 * y4), $MachinePrecision] - N[(y0 * y5), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(b * y4), $MachinePrecision] - N[(i * y5), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(j * N[(N[(N[(y3 * N[(N[(y0 * y5), $MachinePrecision] - N[(y1 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(x * N[(N[(i * y1), $MachinePrecision] - N[(b * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(a * y5), $MachinePrecision] - N[(c * y4), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[(t$95$5 * t$95$1), $MachinePrecision]}, If[LessEqual[y4, -1.5e+144], N[(y4 * N[(N[(y * N[(N[(c * y3), $MachinePrecision] - N[(b * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y1 * t$95$5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, -9.5e-10], t$95$3, If[LessEqual[y4, -3.1e-83], N[(t$95$6 + N[(y2 * N[(N[(x * N[(N[(c * y0), $MachinePrecision] - N[(a * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t * t$95$4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 1.6e-190], N[(t$95$6 + N[(t * N[(N[(N[(z * N[(N[(c * i), $MachinePrecision] - N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(j * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(y2 * t$95$4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 1.6e-99], N[(k * N[(N[(N[(y * N[(N[(i * y5), $MachinePrecision] - N[(b * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y2 * t$95$1), $MachinePrecision]), $MachinePrecision] + N[(z * N[(N[(b * y0), $MachinePrecision] - N[(i * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 1.15e+91], t$95$3, N[(y4 * N[(N[(c * N[(N[(y * y3), $MachinePrecision] - N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(b * N[(N[(y * k), $MachinePrecision] - N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y1 * N[(N[(j * y3), $MachinePrecision] - N[(k * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if y4 < -1.49999999999999995e144

   1. Initial program 26.5%

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

   3. Taylor expanded in y around inf 44.3%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y4 around inf 65.0%

      \[\leadsto \color{blue}{y4 \cdot \left(y \cdot \left(-1 \cdot \left(b \cdot k\right) - -1 \cdot \left(c \cdot y3\right)\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)} \]

   if -1.49999999999999995e144 < y4 < -9.50000000000000028e-10 or 1.6e-99 < y4 < 1.14999999999999996e91

   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 j around inf 66.8%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   if -9.50000000000000028e-10 < y4 < -3.09999999999999992e-83

   1. Initial program 54.4%

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

   3. Taylor expanded in y2 around inf 73.6%

      \[\leadsto \color{blue}{y2 \cdot \left(x \cdot \left(c \cdot y0 - a \cdot y1\right) - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]
   4. Step-by-step derivation

      1. *-commutative 73.6%

         \[\leadsto y2 \cdot \left(\color{blue}{\left(c \cdot y0 - a \cdot y1\right) \cdot x} - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   5. Simplified 73.6%

      \[\leadsto \color{blue}{y2 \cdot \left(\left(c \cdot y0 - a \cdot y1\right) \cdot x - t \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   if -3.09999999999999992e-83 < y4 < 1.6e-190

   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 t around inf 47.2%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   if 1.6e-190 < y4 < 1.6e-99

   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 k around inf 65.3%

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

   if 1.14999999999999996e91 < y4

   1. Initial program 38.9%

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

   3. Taylor expanded in y4 around inf 71.8%

      \[\leadsto \color{blue}{y4 \cdot \left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
3. Recombined 6 regimes into one program.

4. Final simplification 61.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -1.5 \cdot 10^{+144}:\\ \;\;\;\;y4 \cdot \left(y \cdot \left(c \cdot y3 - b \cdot k\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq -9.5 \cdot 10^{-10}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \mathbf{elif}\;y4 \leq -3.1 \cdot 10^{-83}:\\ \;\;\;\;\left(k \cdot y2 - j \cdot y3\right) \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + y2 \cdot \left(x \cdot \left(c \cdot y0 - a \cdot y1\right) + t \cdot \left(a \cdot y5 - c \cdot y4\right)\right)\\ \mathbf{elif}\;y4 \leq 1.6 \cdot 10^{-190}:\\ \;\;\;\;\left(k \cdot y2 - j \cdot y3\right) \cdot \left(y1 \cdot y4 - y0 \cdot y5\right) + t \cdot \left(\left(z \cdot \left(c \cdot i - a \cdot b\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + y2 \cdot \left(a \cdot y5 - c \cdot y4\right)\right)\\ \mathbf{elif}\;y4 \leq 1.6 \cdot 10^{-99}:\\ \;\;\;\;k \cdot \left(\left(y \cdot \left(i \cdot y5 - b \cdot y4\right) + y2 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + z \cdot \left(b \cdot y0 - i \cdot y1\right)\right)\\ \mathbf{elif}\;y4 \leq 1.15 \cdot 10^{+91}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 53.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot y4 - i \cdot y5\\ t_2 := \left(\left(\left(\left(\left(x \cdot y - z \cdot t\right) \cdot \left(a \cdot b - c \cdot i\right) + \left(b \cdot y0 - i \cdot y1\right) \cdot \left(z \cdot k - x \cdot j\right)\right) + \left(c \cdot y0 - a \cdot y1\right) \cdot \left(x \cdot y2 - z \cdot y3\right)\right) + t\_1 \cdot \left(t \cdot j - y \cdot k\right)\right) + \left(t \cdot y2 - y \cdot y3\right) \cdot \left(a \cdot y5 - c \cdot y4\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\\ \mathbf{if}\;t\_2 \leq \infty:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot t\_1\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (- (* b y4) (* i y5)))
        (t_2
         (+
          (+
           (+
            (+
             (+
              (* (- (* x y) (* z t)) (- (* a b) (* c i)))
              (* (- (* b y0) (* i y1)) (- (* z k) (* x j))))
             (* (- (* c y0) (* a y1)) (- (* x y2) (* z y3))))
            (* t_1 (- (* t j) (* y k))))
           (* (- (* t y2) (* y y3)) (- (* a y5) (* c y4))))
          (* (- (* k y2) (* j y3)) (- (* y1 y4) (* y0 y5))))))
   (if (<= t_2 INFINITY)
     t_2
     (*
      j
      (+
       (+ (* y3 (- (* y0 y5) (* y1 y4))) (* t t_1))
       (* x (- (* i y1) (* b 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 t_1 = (b * y4) - (i * y5);
	double t_2 = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) + (((b * y0) - (i * y1)) * ((z * k) - (x * j)))) + (((c * y0) - (a * y1)) * ((x * y2) - (z * y3)))) + (t_1 * ((t * j) - (y * k)))) + (((t * y2) - (y * y3)) * ((a * y5) - (c * y4)))) + (((k * y2) - (j * y3)) * ((y1 * y4) - (y0 * y5)));
	double tmp;
	if (t_2 <= ((double) INFINITY)) {
		tmp = t_2;
	} else {
		tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_1)) + (x * ((i * y1) - (b * y0))));
	}
	return tmp;
}

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 * y4) - (i * y5);
	double t_2 = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) + (((b * y0) - (i * y1)) * ((z * k) - (x * j)))) + (((c * y0) - (a * y1)) * ((x * y2) - (z * y3)))) + (t_1 * ((t * j) - (y * k)))) + (((t * y2) - (y * y3)) * ((a * y5) - (c * y4)))) + (((k * y2) - (j * y3)) * ((y1 * y4) - (y0 * y5)));
	double tmp;
	if (t_2 <= Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else {
		tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_1)) + (x * ((i * y1) - (b * y0))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (b * y4) - (i * y5)
	t_2 = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) + (((b * y0) - (i * y1)) * ((z * k) - (x * j)))) + (((c * y0) - (a * y1)) * ((x * y2) - (z * y3)))) + (t_1 * ((t * j) - (y * k)))) + (((t * y2) - (y * y3)) * ((a * y5) - (c * y4)))) + (((k * y2) - (j * y3)) * ((y1 * y4) - (y0 * y5)))
	tmp = 0
	if t_2 <= math.inf:
		tmp = t_2
	else:
		tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_1)) + (x * ((i * y1) - (b * y0))))
	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 * y4) - Float64(i * y5))
	t_2 = Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * y) - Float64(z * t)) * Float64(Float64(a * b) - Float64(c * i))) + Float64(Float64(Float64(b * y0) - Float64(i * y1)) * Float64(Float64(z * k) - Float64(x * j)))) + Float64(Float64(Float64(c * y0) - Float64(a * y1)) * Float64(Float64(x * y2) - Float64(z * y3)))) + Float64(t_1 * Float64(Float64(t * j) - Float64(y * k)))) + Float64(Float64(Float64(t * y2) - Float64(y * y3)) * Float64(Float64(a * y5) - Float64(c * y4)))) + Float64(Float64(Float64(k * y2) - Float64(j * y3)) * Float64(Float64(y1 * y4) - Float64(y0 * y5))))
	tmp = 0.0
	if (t_2 <= Inf)
		tmp = t_2;
	else
		tmp = Float64(j * Float64(Float64(Float64(y3 * Float64(Float64(y0 * y5) - Float64(y1 * y4))) + Float64(t * t_1)) + Float64(x * Float64(Float64(i * y1) - Float64(b * y0)))));
	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 * y4) - (i * y5);
	t_2 = (((((((x * y) - (z * t)) * ((a * b) - (c * i))) + (((b * y0) - (i * y1)) * ((z * k) - (x * j)))) + (((c * y0) - (a * y1)) * ((x * y2) - (z * y3)))) + (t_1 * ((t * j) - (y * k)))) + (((t * y2) - (y * y3)) * ((a * y5) - (c * y4)))) + (((k * y2) - (j * y3)) * ((y1 * y4) - (y0 * y5)));
	tmp = 0.0;
	if (t_2 <= Inf)
		tmp = t_2;
	else
		tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * t_1)) + (x * ((i * y1) - (b * y0))));
	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 * y4), $MachinePrecision] - N[(i * y5), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = 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[(b * y0), $MachinePrecision] - N[(i * y1), $MachinePrecision]), $MachinePrecision] * N[(N[(z * k), $MachinePrecision] - N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(c * y0), $MachinePrecision] - N[(a * y1), $MachinePrecision]), $MachinePrecision] * N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t$95$1 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(a * y5), $MachinePrecision] - N[(c * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision] * N[(N[(y1 * y4), $MachinePrecision] - N[(y0 * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, Infinity], t$95$2, N[(j * N[(N[(N[(y3 * N[(N[(y0 * y5), $MachinePrecision] - N[(y1 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t * t$95$1), $MachinePrecision]), $MachinePrecision] + N[(x * N[(N[(i * y1), $MachinePrecision] - N[(b * y0), $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 88.7%

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

   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 j around inf 41.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 58.9%

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

Alternative 3: 37.2% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := j \cdot y3 - k \cdot y2\\ t_2 := y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot t\_1\right)\right)\\ \mathbf{if}\;y1 \leq -1.25 \cdot 10^{+87}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -2.25 \cdot 10^{-198}:\\ \;\;\;\;y5 \cdot \left(y0 \cdot t\_1 + y \cdot \left(i \cdot k - a \cdot y3\right)\right)\\ \mathbf{elif}\;y1 \leq 6.2 \cdot 10^{-261}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;y1 \leq 4.8 \cdot 10^{-119}:\\ \;\;\;\;y3 \cdot \left(y \cdot \left(c \cdot y4 - a \cdot y5\right) + \left(j \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + z \cdot \left(a \cdot y1 - c \cdot y0\right)\right)\right)\\ \mathbf{elif}\;y1 \leq 1.1 \cdot 10^{-68}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 3.7 \cdot 10^{+53}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;y1 \leq 1.55 \cdot 10^{+137}:\\ \;\;\;\;i \cdot \left(y \cdot \left(k \cdot y5 - x \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (- (* j y3) (* k y2)))
        (t_2
         (*
          y4
          (-
           (* c (- (* y y3) (* t y2)))
           (+ (* b (- (* y k) (* t j))) (* y1 t_1))))))
   (if (<= y1 -1.25e+87)
     (* j (* y1 (- (* x i) (* y3 y4))))
     (if (<= y1 -2.25e-198)
       (* y5 (+ (* y0 t_1) (* y (- (* i k) (* a y3)))))
       (if (<= y1 6.2e-261)
         t_2
         (if (<= y1 4.8e-119)
           (*
            y3
            (+
             (* y (- (* c y4) (* a y5)))
             (+ (* j (- (* y0 y5) (* y1 y4))) (* z (- (* a y1) (* c y0))))))
           (if (<= y1 1.1e-68)
             (* c (* t (- (* z i) (* y2 y4))))
             (if (<= y1 3.7e+53)
               t_2
               (if (<= y1 1.55e+137)
                 (* i (* y (- (* k y5) (* x c))))
                 (* y3 (* y1 (- (* z a) (* j y4)))))))))))))

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 * y3) - (k * y2);
	double t_2 = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)));
	double tmp;
	if (y1 <= -1.25e+87) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -2.25e-198) {
		tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))));
	} else if (y1 <= 6.2e-261) {
		tmp = t_2;
	} else if (y1 <= 4.8e-119) {
		tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))));
	} else if (y1 <= 1.1e-68) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else if (y1 <= 3.7e+53) {
		tmp = t_2;
	} else if (y1 <= 1.55e+137) {
		tmp = i * (y * ((k * y5) - (x * c)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	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 = (j * y3) - (k * y2)
    t_2 = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)))
    if (y1 <= (-1.25d+87)) then
        tmp = j * (y1 * ((x * i) - (y3 * y4)))
    else if (y1 <= (-2.25d-198)) then
        tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))))
    else if (y1 <= 6.2d-261) then
        tmp = t_2
    else if (y1 <= 4.8d-119) then
        tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))))
    else if (y1 <= 1.1d-68) then
        tmp = c * (t * ((z * i) - (y2 * y4)))
    else if (y1 <= 3.7d+53) then
        tmp = t_2
    else if (y1 <= 1.55d+137) then
        tmp = i * (y * ((k * y5) - (x * c)))
    else
        tmp = y3 * (y1 * ((z * a) - (j * y4)))
    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 = (j * y3) - (k * y2);
	double t_2 = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)));
	double tmp;
	if (y1 <= -1.25e+87) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -2.25e-198) {
		tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))));
	} else if (y1 <= 6.2e-261) {
		tmp = t_2;
	} else if (y1 <= 4.8e-119) {
		tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))));
	} else if (y1 <= 1.1e-68) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else if (y1 <= 3.7e+53) {
		tmp = t_2;
	} else if (y1 <= 1.55e+137) {
		tmp = i * (y * ((k * y5) - (x * c)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (j * y3) - (k * y2)
	t_2 = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)))
	tmp = 0
	if y1 <= -1.25e+87:
		tmp = j * (y1 * ((x * i) - (y3 * y4)))
	elif y1 <= -2.25e-198:
		tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))))
	elif y1 <= 6.2e-261:
		tmp = t_2
	elif y1 <= 4.8e-119:
		tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))))
	elif y1 <= 1.1e-68:
		tmp = c * (t * ((z * i) - (y2 * y4)))
	elif y1 <= 3.7e+53:
		tmp = t_2
	elif y1 <= 1.55e+137:
		tmp = i * (y * ((k * y5) - (x * c)))
	else:
		tmp = y3 * (y1 * ((z * a) - (j * y4)))
	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 * y3) - Float64(k * y2))
	t_2 = Float64(y4 * Float64(Float64(c * Float64(Float64(y * y3) - Float64(t * y2))) - Float64(Float64(b * Float64(Float64(y * k) - Float64(t * j))) + Float64(y1 * t_1))))
	tmp = 0.0
	if (y1 <= -1.25e+87)
		tmp = Float64(j * Float64(y1 * Float64(Float64(x * i) - Float64(y3 * y4))));
	elseif (y1 <= -2.25e-198)
		tmp = Float64(y5 * Float64(Float64(y0 * t_1) + Float64(y * Float64(Float64(i * k) - Float64(a * y3)))));
	elseif (y1 <= 6.2e-261)
		tmp = t_2;
	elseif (y1 <= 4.8e-119)
		tmp = Float64(y3 * Float64(Float64(y * Float64(Float64(c * y4) - Float64(a * y5))) + Float64(Float64(j * Float64(Float64(y0 * y5) - Float64(y1 * y4))) + Float64(z * Float64(Float64(a * y1) - Float64(c * y0))))));
	elseif (y1 <= 1.1e-68)
		tmp = Float64(c * Float64(t * Float64(Float64(z * i) - Float64(y2 * y4))));
	elseif (y1 <= 3.7e+53)
		tmp = t_2;
	elseif (y1 <= 1.55e+137)
		tmp = Float64(i * Float64(y * Float64(Float64(k * y5) - Float64(x * c))));
	else
		tmp = Float64(y3 * Float64(y1 * Float64(Float64(z * a) - Float64(j * y4))));
	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 = (j * y3) - (k * y2);
	t_2 = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)));
	tmp = 0.0;
	if (y1 <= -1.25e+87)
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	elseif (y1 <= -2.25e-198)
		tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))));
	elseif (y1 <= 6.2e-261)
		tmp = t_2;
	elseif (y1 <= 4.8e-119)
		tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))));
	elseif (y1 <= 1.1e-68)
		tmp = c * (t * ((z * i) - (y2 * y4)));
	elseif (y1 <= 3.7e+53)
		tmp = t_2;
	elseif (y1 <= 1.55e+137)
		tmp = i * (y * ((k * y5) - (x * c)));
	else
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	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[(j * y3), $MachinePrecision] - N[(k * y2), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y4 * N[(N[(c * N[(N[(y * y3), $MachinePrecision] - N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(b * N[(N[(y * k), $MachinePrecision] - N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y1, -1.25e+87], N[(j * N[(y1 * N[(N[(x * i), $MachinePrecision] - N[(y3 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, -2.25e-198], N[(y5 * N[(N[(y0 * t$95$1), $MachinePrecision] + N[(y * N[(N[(i * k), $MachinePrecision] - N[(a * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 6.2e-261], t$95$2, If[LessEqual[y1, 4.8e-119], N[(y3 * N[(N[(y * N[(N[(c * y4), $MachinePrecision] - N[(a * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(j * N[(N[(y0 * y5), $MachinePrecision] - N[(y1 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(z * N[(N[(a * y1), $MachinePrecision] - N[(c * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 1.1e-68], N[(c * N[(t * N[(N[(z * i), $MachinePrecision] - N[(y2 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 3.7e+53], t$95$2, If[LessEqual[y1, 1.55e+137], N[(i * N[(y * N[(N[(k * y5), $MachinePrecision] - N[(x * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y3 * N[(y1 * N[(N[(z * a), $MachinePrecision] - N[(j * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]

Derivation

1. Split input into 7 regimes

2. if y1 < -1.24999999999999995e87

   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 j around inf 49.0%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 55.2%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 55.2%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 55.2%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   if -1.24999999999999995e87 < y1 < -2.2499999999999999e-198

   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 y around inf 47.9%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y5 around inf 57.0%

      \[\leadsto \color{blue}{y5 \cdot \left(-1 \cdot \left(y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + y \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]

   if -2.2499999999999999e-198 < y1 < 6.1999999999999997e-261 or 1.10000000000000001e-68 < y1 < 3.7e53

   1. Initial program 32.4%

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

   3. Taylor expanded in y4 around inf 56.6%

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

   if 6.1999999999999997e-261 < y1 < 4.80000000000000017e-119

   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 y3 around -inf 59.1%

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

   if 4.80000000000000017e-119 < y1 < 1.10000000000000001e-68

   1. Initial program 36.4%

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

   3. Taylor expanded in t around inf 63.7%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in c around inf 64.4%

      \[\leadsto \color{blue}{c \cdot \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right)} \]

   if 3.7e53 < y1 < 1.55e137

   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 y around inf 33.9%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in i around inf 60.4%

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

   if 1.55e137 < y1

   1. Initial program 21.3%

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

   3. Taylor expanded in y3 around -inf 37.8%

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

   4. Taylor expanded in y1 around inf 58.7%

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

      1. +-commutative 58.7%

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

      2. mul-1-neg 58.7%

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

      3. unsub-neg 58.7%

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

      4. *-commutative 58.7%

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

   6. Simplified 58.7%

      \[\leadsto -1 \cdot \left(y3 \cdot \color{blue}{\left(y1 \cdot \left(y4 \cdot j - a \cdot z\right)\right)}\right) \]
3. Recombined 7 regimes into one program.

4. Final simplification 57.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -1.25 \cdot 10^{+87}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -2.25 \cdot 10^{-198}:\\ \;\;\;\;y5 \cdot \left(y0 \cdot \left(j \cdot y3 - k \cdot y2\right) + y \cdot \left(i \cdot k - a \cdot y3\right)\right)\\ \mathbf{elif}\;y1 \leq 6.2 \cdot 10^{-261}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \mathbf{elif}\;y1 \leq 4.8 \cdot 10^{-119}:\\ \;\;\;\;y3 \cdot \left(y \cdot \left(c \cdot y4 - a \cdot y5\right) + \left(j \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + z \cdot \left(a \cdot y1 - c \cdot y0\right)\right)\right)\\ \mathbf{elif}\;y1 \leq 1.1 \cdot 10^{-68}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 3.7 \cdot 10^{+53}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \mathbf{elif}\;y1 \leq 1.55 \cdot 10^{+137}:\\ \;\;\;\;i \cdot \left(y \cdot \left(k \cdot y5 - x \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 40.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ t_2 := j \cdot y3 - k \cdot y2\\ \mathbf{if}\;y4 \leq -2.7 \cdot 10^{+154}:\\ \;\;\;\;y4 \cdot \left(y \cdot \left(c \cdot y3 - b \cdot k\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq -2.1 \cdot 10^{-297}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y4 \leq 2.5 \cdot 10^{-99}:\\ \;\;\;\;z \cdot \left(y0 \cdot \left(b \cdot k - c \cdot y3\right) + \frac{y0 \cdot \left(\left(y5 \cdot t\_2 + c \cdot \left(x \cdot y2\right)\right) - b \cdot \left(x \cdot j\right)\right)}{z}\right)\\ \mathbf{elif}\;y4 \leq 7.5 \cdot 10^{+90}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot t\_2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1
         (*
          j
          (+
           (+ (* y3 (- (* y0 y5) (* y1 y4))) (* t (- (* b y4) (* i y5))))
           (* x (- (* i y1) (* b y0))))))
        (t_2 (- (* j y3) (* k y2))))
   (if (<= y4 -2.7e+154)
     (* y4 (+ (* y (- (* c y3) (* b k))) (* y1 (- (* k y2) (* j y3)))))
     (if (<= y4 -2.1e-297)
       t_1
       (if (<= y4 2.5e-99)
         (*
          z
          (+
           (* y0 (- (* b k) (* c y3)))
           (/ (* y0 (- (+ (* y5 t_2) (* c (* x y2))) (* b (* x j)))) z)))
         (if (<= y4 7.5e+90)
           t_1
           (*
            y4
            (-
             (* c (- (* y y3) (* t y2)))
             (+ (* b (- (* y k) (* t j))) (* y1 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 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))));
	double t_2 = (j * y3) - (k * y2);
	double tmp;
	if (y4 <= -2.7e+154) {
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))));
	} else if (y4 <= -2.1e-297) {
		tmp = t_1;
	} else if (y4 <= 2.5e-99) {
		tmp = z * ((y0 * ((b * k) - (c * y3))) + ((y0 * (((y5 * t_2) + (c * (x * y2))) - (b * (x * j)))) / z));
	} else if (y4 <= 7.5e+90) {
		tmp = t_1;
	} else {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_2)));
	}
	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 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))))
    t_2 = (j * y3) - (k * y2)
    if (y4 <= (-2.7d+154)) then
        tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))))
    else if (y4 <= (-2.1d-297)) then
        tmp = t_1
    else if (y4 <= 2.5d-99) then
        tmp = z * ((y0 * ((b * k) - (c * y3))) + ((y0 * (((y5 * t_2) + (c * (x * y2))) - (b * (x * j)))) / z))
    else if (y4 <= 7.5d+90) then
        tmp = t_1
    else
        tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_2)))
    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 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))));
	double t_2 = (j * y3) - (k * y2);
	double tmp;
	if (y4 <= -2.7e+154) {
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))));
	} else if (y4 <= -2.1e-297) {
		tmp = t_1;
	} else if (y4 <= 2.5e-99) {
		tmp = z * ((y0 * ((b * k) - (c * y3))) + ((y0 * (((y5 * t_2) + (c * (x * y2))) - (b * (x * j)))) / z));
	} else if (y4 <= 7.5e+90) {
		tmp = t_1;
	} else {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_2)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))))
	t_2 = (j * y3) - (k * y2)
	tmp = 0
	if y4 <= -2.7e+154:
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))))
	elif y4 <= -2.1e-297:
		tmp = t_1
	elif y4 <= 2.5e-99:
		tmp = z * ((y0 * ((b * k) - (c * y3))) + ((y0 * (((y5 * t_2) + (c * (x * y2))) - (b * (x * j)))) / z))
	elif y4 <= 7.5e+90:
		tmp = t_1
	else:
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * 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(j * Float64(Float64(Float64(y3 * Float64(Float64(y0 * y5) - Float64(y1 * y4))) + Float64(t * Float64(Float64(b * y4) - Float64(i * y5)))) + Float64(x * Float64(Float64(i * y1) - Float64(b * y0)))))
	t_2 = Float64(Float64(j * y3) - Float64(k * y2))
	tmp = 0.0
	if (y4 <= -2.7e+154)
		tmp = Float64(y4 * Float64(Float64(y * Float64(Float64(c * y3) - Float64(b * k))) + Float64(y1 * Float64(Float64(k * y2) - Float64(j * y3)))));
	elseif (y4 <= -2.1e-297)
		tmp = t_1;
	elseif (y4 <= 2.5e-99)
		tmp = Float64(z * Float64(Float64(y0 * Float64(Float64(b * k) - Float64(c * y3))) + Float64(Float64(y0 * Float64(Float64(Float64(y5 * t_2) + Float64(c * Float64(x * y2))) - Float64(b * Float64(x * j)))) / z)));
	elseif (y4 <= 7.5e+90)
		tmp = t_1;
	else
		tmp = Float64(y4 * Float64(Float64(c * Float64(Float64(y * y3) - Float64(t * y2))) - Float64(Float64(b * Float64(Float64(y * k) - Float64(t * j))) + Float64(y1 * t_2))));
	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 = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))));
	t_2 = (j * y3) - (k * y2);
	tmp = 0.0;
	if (y4 <= -2.7e+154)
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))));
	elseif (y4 <= -2.1e-297)
		tmp = t_1;
	elseif (y4 <= 2.5e-99)
		tmp = z * ((y0 * ((b * k) - (c * y3))) + ((y0 * (((y5 * t_2) + (c * (x * y2))) - (b * (x * j)))) / z));
	elseif (y4 <= 7.5e+90)
		tmp = t_1;
	else
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_2)));
	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[(j * N[(N[(N[(y3 * N[(N[(y0 * y5), $MachinePrecision] - N[(y1 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t * N[(N[(b * y4), $MachinePrecision] - N[(i * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x * N[(N[(i * y1), $MachinePrecision] - N[(b * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(j * y3), $MachinePrecision] - N[(k * y2), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y4, -2.7e+154], N[(y4 * N[(N[(y * N[(N[(c * y3), $MachinePrecision] - N[(b * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y1 * N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, -2.1e-297], t$95$1, If[LessEqual[y4, 2.5e-99], N[(z * N[(N[(y0 * N[(N[(b * k), $MachinePrecision] - N[(c * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(y0 * N[(N[(N[(y5 * t$95$2), $MachinePrecision] + N[(c * N[(x * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b * N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 7.5e+90], t$95$1, N[(y4 * N[(N[(c * N[(N[(y * y3), $MachinePrecision] - N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(b * N[(N[(y * k), $MachinePrecision] - N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y1 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

1. Split input into 4 regimes

2. if y4 < -2.70000000000000006e154

   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 y around inf 45.3%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y4 around inf 64.8%

      \[\leadsto \color{blue}{y4 \cdot \left(y \cdot \left(-1 \cdot \left(b \cdot k\right) - -1 \cdot \left(c \cdot y3\right)\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)} \]

   if -2.70000000000000006e154 < y4 < -2.10000000000000013e-297 or 2.49999999999999985e-99 < y4 < 7.50000000000000014e90

   1. Initial program 29.5%

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

   3. Taylor expanded in j around inf 52.4%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   if -2.10000000000000013e-297 < y4 < 2.49999999999999985e-99

   1. Initial program 39.6%

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

   3. Taylor expanded in y0 around inf 54.1%

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

   4. Taylor expanded in z around inf 54.5%

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

   if 7.50000000000000014e90 < y4

   1. Initial program 38.9%

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

   3. Taylor expanded in y4 around inf 71.8%

      \[\leadsto \color{blue}{y4 \cdot \left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 58.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -2.7 \cdot 10^{+154}:\\ \;\;\;\;y4 \cdot \left(y \cdot \left(c \cdot y3 - b \cdot k\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq -2.1 \cdot 10^{-297}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \mathbf{elif}\;y4 \leq 2.5 \cdot 10^{-99}:\\ \;\;\;\;z \cdot \left(y0 \cdot \left(b \cdot k - c \cdot y3\right) + \frac{y0 \cdot \left(\left(y5 \cdot \left(j \cdot y3 - k \cdot y2\right) + c \cdot \left(x \cdot y2\right)\right) - b \cdot \left(x \cdot j\right)\right)}{z}\right)\\ \mathbf{elif}\;y4 \leq 7.5 \cdot 10^{+90}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 41.6% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := j \cdot y3 - k \cdot y2\\ t_2 := y \cdot k - t \cdot j\\ t_3 := y5 \cdot \left(a \cdot \left(t \cdot y2 - y \cdot y3\right) + \left(y0 \cdot t\_1 + i \cdot t\_2\right)\right)\\ \mathbf{if}\;y5 \leq -1.5 \cdot 10^{+121}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;y5 \leq -1.15 \cdot 10^{-184}:\\ \;\;\;\;y3 \cdot \left(y \cdot \left(c \cdot y4 - a \cdot y5\right) + \left(j \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + z \cdot \left(a \cdot y1 - c \cdot y0\right)\right)\right)\\ \mathbf{elif}\;y5 \leq 6000000000000:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot t\_2 + y1 \cdot t\_1\right)\right)\\ \mathbf{elif}\;y5 \leq 7.8 \cdot 10^{+80}:\\ \;\;\;\;y0 \cdot \left(b \cdot \left(z \cdot k - x \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (- (* j y3) (* k y2)))
        (t_2 (- (* y k) (* t j)))
        (t_3 (* y5 (+ (* a (- (* t y2) (* y y3))) (+ (* y0 t_1) (* i t_2))))))
   (if (<= y5 -1.5e+121)
     t_3
     (if (<= y5 -1.15e-184)
       (*
        y3
        (+
         (* y (- (* c y4) (* a y5)))
         (+ (* j (- (* y0 y5) (* y1 y4))) (* z (- (* a y1) (* c y0))))))
       (if (<= y5 6000000000000.0)
         (* y4 (- (* c (- (* y y3) (* t y2))) (+ (* b t_2) (* y1 t_1))))
         (if (<= y5 7.8e+80) (* y0 (* b (- (* z k) (* x j)))) t_3))))))

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 * y3) - (k * y2);
	double t_2 = (y * k) - (t * j);
	double t_3 = y5 * ((a * ((t * y2) - (y * y3))) + ((y0 * t_1) + (i * t_2)));
	double tmp;
	if (y5 <= -1.5e+121) {
		tmp = t_3;
	} else if (y5 <= -1.15e-184) {
		tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))));
	} else if (y5 <= 6000000000000.0) {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * t_2) + (y1 * t_1)));
	} else if (y5 <= 7.8e+80) {
		tmp = y0 * (b * ((z * k) - (x * j)));
	} else {
		tmp = t_3;
	}
	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) :: t_3
    real(8) :: tmp
    t_1 = (j * y3) - (k * y2)
    t_2 = (y * k) - (t * j)
    t_3 = y5 * ((a * ((t * y2) - (y * y3))) + ((y0 * t_1) + (i * t_2)))
    if (y5 <= (-1.5d+121)) then
        tmp = t_3
    else if (y5 <= (-1.15d-184)) then
        tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))))
    else if (y5 <= 6000000000000.0d0) then
        tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * t_2) + (y1 * t_1)))
    else if (y5 <= 7.8d+80) then
        tmp = y0 * (b * ((z * k) - (x * j)))
    else
        tmp = t_3
    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 = (j * y3) - (k * y2);
	double t_2 = (y * k) - (t * j);
	double t_3 = y5 * ((a * ((t * y2) - (y * y3))) + ((y0 * t_1) + (i * t_2)));
	double tmp;
	if (y5 <= -1.5e+121) {
		tmp = t_3;
	} else if (y5 <= -1.15e-184) {
		tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))));
	} else if (y5 <= 6000000000000.0) {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * t_2) + (y1 * t_1)));
	} else if (y5 <= 7.8e+80) {
		tmp = y0 * (b * ((z * k) - (x * j)));
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (j * y3) - (k * y2)
	t_2 = (y * k) - (t * j)
	t_3 = y5 * ((a * ((t * y2) - (y * y3))) + ((y0 * t_1) + (i * t_2)))
	tmp = 0
	if y5 <= -1.5e+121:
		tmp = t_3
	elif y5 <= -1.15e-184:
		tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))))
	elif y5 <= 6000000000000.0:
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * t_2) + (y1 * t_1)))
	elif y5 <= 7.8e+80:
		tmp = y0 * (b * ((z * k) - (x * j)))
	else:
		tmp = t_3
	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 * y3) - Float64(k * y2))
	t_2 = Float64(Float64(y * k) - Float64(t * j))
	t_3 = Float64(y5 * Float64(Float64(a * Float64(Float64(t * y2) - Float64(y * y3))) + Float64(Float64(y0 * t_1) + Float64(i * t_2))))
	tmp = 0.0
	if (y5 <= -1.5e+121)
		tmp = t_3;
	elseif (y5 <= -1.15e-184)
		tmp = Float64(y3 * Float64(Float64(y * Float64(Float64(c * y4) - Float64(a * y5))) + Float64(Float64(j * Float64(Float64(y0 * y5) - Float64(y1 * y4))) + Float64(z * Float64(Float64(a * y1) - Float64(c * y0))))));
	elseif (y5 <= 6000000000000.0)
		tmp = Float64(y4 * Float64(Float64(c * Float64(Float64(y * y3) - Float64(t * y2))) - Float64(Float64(b * t_2) + Float64(y1 * t_1))));
	elseif (y5 <= 7.8e+80)
		tmp = Float64(y0 * Float64(b * Float64(Float64(z * k) - Float64(x * j))));
	else
		tmp = t_3;
	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 = (j * y3) - (k * y2);
	t_2 = (y * k) - (t * j);
	t_3 = y5 * ((a * ((t * y2) - (y * y3))) + ((y0 * t_1) + (i * t_2)));
	tmp = 0.0;
	if (y5 <= -1.5e+121)
		tmp = t_3;
	elseif (y5 <= -1.15e-184)
		tmp = y3 * ((y * ((c * y4) - (a * y5))) + ((j * ((y0 * y5) - (y1 * y4))) + (z * ((a * y1) - (c * y0)))));
	elseif (y5 <= 6000000000000.0)
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * t_2) + (y1 * t_1)));
	elseif (y5 <= 7.8e+80)
		tmp = y0 * (b * ((z * k) - (x * j)));
	else
		tmp = t_3;
	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[(j * y3), $MachinePrecision] - N[(k * y2), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(y * k), $MachinePrecision] - N[(t * j), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(y5 * N[(N[(a * N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(y0 * t$95$1), $MachinePrecision] + N[(i * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y5, -1.5e+121], t$95$3, If[LessEqual[y5, -1.15e-184], N[(y3 * N[(N[(y * N[(N[(c * y4), $MachinePrecision] - N[(a * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(j * N[(N[(y0 * y5), $MachinePrecision] - N[(y1 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(z * N[(N[(a * y1), $MachinePrecision] - N[(c * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 6000000000000.0], N[(y4 * N[(N[(c * N[(N[(y * y3), $MachinePrecision] - N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(b * t$95$2), $MachinePrecision] + N[(y1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y5, 7.8e+80], N[(y0 * N[(b * N[(N[(z * k), $MachinePrecision] - N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]]

Derivation

1. Split input into 4 regimes

2. if y5 < -1.5000000000000001e121 or 7.79999999999999998e80 < y5

   1. Initial program 28.9%

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

   3. Taylor expanded in y5 around -inf 59.4%

      \[\leadsto \color{blue}{-1 \cdot \left(y5 \cdot \left(\left(i \cdot \left(j \cdot t - k \cdot y\right) + y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - a \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\right)} \]

   if -1.5000000000000001e121 < y5 < -1.15e-184

   1. Initial program 33.5%

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

   3. Taylor expanded in y3 around -inf 50.6%

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

   if -1.15e-184 < y5 < 6e12

   1. Initial program 37.3%

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

   3. Taylor expanded in y4 around inf 47.4%

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

   if 6e12 < y5 < 7.79999999999999998e80

   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 y0 around inf 29.9%

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

   4. Taylor expanded in b around inf 70.7%

      \[\leadsto y0 \cdot \color{blue}{\left(b \cdot \left(k \cdot z - j \cdot x\right)\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 53.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y5 \leq -1.5 \cdot 10^{+121}:\\ \;\;\;\;y5 \cdot \left(a \cdot \left(t \cdot y2 - y \cdot y3\right) + \left(y0 \cdot \left(j \cdot y3 - k \cdot y2\right) + i \cdot \left(y \cdot k - t \cdot j\right)\right)\right)\\ \mathbf{elif}\;y5 \leq -1.15 \cdot 10^{-184}:\\ \;\;\;\;y3 \cdot \left(y \cdot \left(c \cdot y4 - a \cdot y5\right) + \left(j \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + z \cdot \left(a \cdot y1 - c \cdot y0\right)\right)\right)\\ \mathbf{elif}\;y5 \leq 6000000000000:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \mathbf{elif}\;y5 \leq 7.8 \cdot 10^{+80}:\\ \;\;\;\;y0 \cdot \left(b \cdot \left(z \cdot k - x \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y5 \cdot \left(a \cdot \left(t \cdot y2 - y \cdot y3\right) + \left(y0 \cdot \left(j \cdot y3 - k \cdot y2\right) + i \cdot \left(y \cdot k - t \cdot j\right)\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 37.4% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := j \cdot y3 - k \cdot y2\\ \mathbf{if}\;y1 \leq -1.3 \cdot 10^{+87}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -2.95 \cdot 10^{-198}:\\ \;\;\;\;y5 \cdot \left(y0 \cdot t\_1 + y \cdot \left(i \cdot k - a \cdot y3\right)\right)\\ \mathbf{elif}\;y1 \leq 1.22 \cdot 10^{+66}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot t\_1\right)\right)\\ \mathbf{elif}\;y1 \leq 9 \cdot 10^{+134}:\\ \;\;\;\;i \cdot \left(y \cdot \left(k \cdot y5 - x \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (- (* j y3) (* k y2))))
   (if (<= y1 -1.3e+87)
     (* j (* y1 (- (* x i) (* y3 y4))))
     (if (<= y1 -2.95e-198)
       (* y5 (+ (* y0 t_1) (* y (- (* i k) (* a y3)))))
       (if (<= y1 1.22e+66)
         (*
          y4
          (-
           (* c (- (* y y3) (* t y2)))
           (+ (* b (- (* y k) (* t j))) (* y1 t_1))))
         (if (<= y1 9e+134)
           (* i (* y (- (* k y5) (* x c))))
           (* y3 (* y1 (- (* z a) (* j y4))))))))))

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 * y3) - (k * y2);
	double tmp;
	if (y1 <= -1.3e+87) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -2.95e-198) {
		tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))));
	} else if (y1 <= 1.22e+66) {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)));
	} else if (y1 <= 9e+134) {
		tmp = i * (y * ((k * y5) - (x * c)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	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 = (j * y3) - (k * y2)
    if (y1 <= (-1.3d+87)) then
        tmp = j * (y1 * ((x * i) - (y3 * y4)))
    else if (y1 <= (-2.95d-198)) then
        tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))))
    else if (y1 <= 1.22d+66) then
        tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)))
    else if (y1 <= 9d+134) then
        tmp = i * (y * ((k * y5) - (x * c)))
    else
        tmp = y3 * (y1 * ((z * a) - (j * y4)))
    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 = (j * y3) - (k * y2);
	double tmp;
	if (y1 <= -1.3e+87) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -2.95e-198) {
		tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))));
	} else if (y1 <= 1.22e+66) {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)));
	} else if (y1 <= 9e+134) {
		tmp = i * (y * ((k * y5) - (x * c)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (j * y3) - (k * y2)
	tmp = 0
	if y1 <= -1.3e+87:
		tmp = j * (y1 * ((x * i) - (y3 * y4)))
	elif y1 <= -2.95e-198:
		tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))))
	elif y1 <= 1.22e+66:
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)))
	elif y1 <= 9e+134:
		tmp = i * (y * ((k * y5) - (x * c)))
	else:
		tmp = y3 * (y1 * ((z * a) - (j * y4)))
	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 * y3) - Float64(k * y2))
	tmp = 0.0
	if (y1 <= -1.3e+87)
		tmp = Float64(j * Float64(y1 * Float64(Float64(x * i) - Float64(y3 * y4))));
	elseif (y1 <= -2.95e-198)
		tmp = Float64(y5 * Float64(Float64(y0 * t_1) + Float64(y * Float64(Float64(i * k) - Float64(a * y3)))));
	elseif (y1 <= 1.22e+66)
		tmp = Float64(y4 * Float64(Float64(c * Float64(Float64(y * y3) - Float64(t * y2))) - Float64(Float64(b * Float64(Float64(y * k) - Float64(t * j))) + Float64(y1 * t_1))));
	elseif (y1 <= 9e+134)
		tmp = Float64(i * Float64(y * Float64(Float64(k * y5) - Float64(x * c))));
	else
		tmp = Float64(y3 * Float64(y1 * Float64(Float64(z * a) - Float64(j * y4))));
	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 = (j * y3) - (k * y2);
	tmp = 0.0;
	if (y1 <= -1.3e+87)
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	elseif (y1 <= -2.95e-198)
		tmp = y5 * ((y0 * t_1) + (y * ((i * k) - (a * y3))));
	elseif (y1 <= 1.22e+66)
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * t_1)));
	elseif (y1 <= 9e+134)
		tmp = i * (y * ((k * y5) - (x * c)));
	else
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	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[(j * y3), $MachinePrecision] - N[(k * y2), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y1, -1.3e+87], N[(j * N[(y1 * N[(N[(x * i), $MachinePrecision] - N[(y3 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, -2.95e-198], N[(y5 * N[(N[(y0 * t$95$1), $MachinePrecision] + N[(y * N[(N[(i * k), $MachinePrecision] - N[(a * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 1.22e+66], N[(y4 * N[(N[(c * N[(N[(y * y3), $MachinePrecision] - N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(b * N[(N[(y * k), $MachinePrecision] - N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 9e+134], N[(i * N[(y * N[(N[(k * y5), $MachinePrecision] - N[(x * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y3 * N[(y1 * N[(N[(z * a), $MachinePrecision] - N[(j * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 5 regimes

2. if y1 < -1.29999999999999999e87

   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 j around inf 49.0%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 55.2%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 55.2%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 55.2%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   if -1.29999999999999999e87 < y1 < -2.94999999999999987e-198

   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 y around inf 47.9%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y5 around inf 57.0%

      \[\leadsto \color{blue}{y5 \cdot \left(-1 \cdot \left(y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + y \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]

   if -2.94999999999999987e-198 < y1 < 1.21999999999999993e66

   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 y4 around inf 45.4%

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

   if 1.21999999999999993e66 < y1 < 8.9999999999999995e134

   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 y around inf 33.9%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in i around inf 60.4%

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

   if 8.9999999999999995e134 < y1

   1. Initial program 21.3%

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

   3. Taylor expanded in y3 around -inf 37.8%

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

   4. Taylor expanded in y1 around inf 58.7%

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

      1. +-commutative 58.7%

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

      2. mul-1-neg 58.7%

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

      3. unsub-neg 58.7%

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

      4. *-commutative 58.7%

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

   6. Simplified 58.7%

      \[\leadsto -1 \cdot \left(y3 \cdot \color{blue}{\left(y1 \cdot \left(y4 \cdot j - a \cdot z\right)\right)}\right) \]
3. Recombined 5 regimes into one program.

4. Final simplification 52.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -1.3 \cdot 10^{+87}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -2.95 \cdot 10^{-198}:\\ \;\;\;\;y5 \cdot \left(y0 \cdot \left(j \cdot y3 - k \cdot y2\right) + y \cdot \left(i \cdot k - a \cdot y3\right)\right)\\ \mathbf{elif}\;y1 \leq 1.22 \cdot 10^{+66}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \mathbf{elif}\;y1 \leq 9 \cdot 10^{+134}:\\ \;\;\;\;i \cdot \left(y \cdot \left(k \cdot y5 - x \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 34.9% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y1 \leq -4 \cdot 10^{+85}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -7.8 \cdot 10^{-199}:\\ \;\;\;\;y5 \cdot \left(y0 \cdot \left(j \cdot y3 - k \cdot y2\right) + y \cdot \left(i \cdot k - a \cdot y3\right)\right)\\ \mathbf{elif}\;y1 \leq 9 \cdot 10^{-201}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 135000000000:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 2.9 \cdot 10^{+136}:\\ \;\;\;\;y \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right) - b \cdot \left(k \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y1 -4e+85)
   (* j (* y1 (- (* x i) (* y3 y4))))
   (if (<= y1 -7.8e-199)
     (* y5 (+ (* y0 (- (* j y3) (* k y2))) (* y (- (* i k) (* a y3)))))
     (if (<= y1 9e-201)
       (* b (* y4 (- (* t j) (* y k))))
       (if (<= y1 135000000000.0)
         (* c (* t (- (* z i) (* y2 y4))))
         (if (<= y1 2.9e+136)
           (* y (- (* y3 (- (* c y4) (* a y5))) (* b (* k y4))))
           (* y3 (* y1 (- (* z a) (* j y4))))))))))

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 (y1 <= -4e+85) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -7.8e-199) {
		tmp = y5 * ((y0 * ((j * y3) - (k * y2))) + (y * ((i * k) - (a * y3))));
	} else if (y1 <= 9e-201) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 135000000000.0) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else if (y1 <= 2.9e+136) {
		tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	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 (y1 <= (-4d+85)) then
        tmp = j * (y1 * ((x * i) - (y3 * y4)))
    else if (y1 <= (-7.8d-199)) then
        tmp = y5 * ((y0 * ((j * y3) - (k * y2))) + (y * ((i * k) - (a * y3))))
    else if (y1 <= 9d-201) then
        tmp = b * (y4 * ((t * j) - (y * k)))
    else if (y1 <= 135000000000.0d0) then
        tmp = c * (t * ((z * i) - (y2 * y4)))
    else if (y1 <= 2.9d+136) then
        tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)))
    else
        tmp = y3 * (y1 * ((z * a) - (j * y4)))
    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 (y1 <= -4e+85) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -7.8e-199) {
		tmp = y5 * ((y0 * ((j * y3) - (k * y2))) + (y * ((i * k) - (a * y3))));
	} else if (y1 <= 9e-201) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 135000000000.0) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else if (y1 <= 2.9e+136) {
		tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y1 <= -4e+85:
		tmp = j * (y1 * ((x * i) - (y3 * y4)))
	elif y1 <= -7.8e-199:
		tmp = y5 * ((y0 * ((j * y3) - (k * y2))) + (y * ((i * k) - (a * y3))))
	elif y1 <= 9e-201:
		tmp = b * (y4 * ((t * j) - (y * k)))
	elif y1 <= 135000000000.0:
		tmp = c * (t * ((z * i) - (y2 * y4)))
	elif y1 <= 2.9e+136:
		tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)))
	else:
		tmp = y3 * (y1 * ((z * a) - (j * y4)))
	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 (y1 <= -4e+85)
		tmp = Float64(j * Float64(y1 * Float64(Float64(x * i) - Float64(y3 * y4))));
	elseif (y1 <= -7.8e-199)
		tmp = Float64(y5 * Float64(Float64(y0 * Float64(Float64(j * y3) - Float64(k * y2))) + Float64(y * Float64(Float64(i * k) - Float64(a * y3)))));
	elseif (y1 <= 9e-201)
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	elseif (y1 <= 135000000000.0)
		tmp = Float64(c * Float64(t * Float64(Float64(z * i) - Float64(y2 * y4))));
	elseif (y1 <= 2.9e+136)
		tmp = Float64(y * Float64(Float64(y3 * Float64(Float64(c * y4) - Float64(a * y5))) - Float64(b * Float64(k * y4))));
	else
		tmp = Float64(y3 * Float64(y1 * Float64(Float64(z * a) - Float64(j * y4))));
	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 (y1 <= -4e+85)
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	elseif (y1 <= -7.8e-199)
		tmp = y5 * ((y0 * ((j * y3) - (k * y2))) + (y * ((i * k) - (a * y3))));
	elseif (y1 <= 9e-201)
		tmp = b * (y4 * ((t * j) - (y * k)));
	elseif (y1 <= 135000000000.0)
		tmp = c * (t * ((z * i) - (y2 * y4)));
	elseif (y1 <= 2.9e+136)
		tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)));
	else
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	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[y1, -4e+85], N[(j * N[(y1 * N[(N[(x * i), $MachinePrecision] - N[(y3 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, -7.8e-199], N[(y5 * N[(N[(y0 * N[(N[(j * y3), $MachinePrecision] - N[(k * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y * N[(N[(i * k), $MachinePrecision] - N[(a * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 9e-201], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 135000000000.0], N[(c * N[(t * N[(N[(z * i), $MachinePrecision] - N[(y2 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 2.9e+136], N[(y * N[(N[(y3 * N[(N[(c * y4), $MachinePrecision] - N[(a * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b * N[(k * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y3 * N[(y1 * N[(N[(z * a), $MachinePrecision] - N[(j * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 6 regimes

2. if y1 < -4.0000000000000001e85

   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 j around inf 49.0%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 55.2%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 55.2%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 55.2%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   if -4.0000000000000001e85 < y1 < -7.8000000000000002e-199

   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 y around inf 47.9%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y5 around inf 57.0%

      \[\leadsto \color{blue}{y5 \cdot \left(-1 \cdot \left(y0 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) + y \cdot \left(i \cdot k - a \cdot y3\right)\right)} \]

   if -7.8000000000000002e-199 < y1 < 9.0000000000000004e-201

   1. Initial program 36.2%

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

   3. Taylor expanded in y4 around inf 46.4%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 47.7%

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

   if 9.0000000000000004e-201 < y1 < 1.35e11

   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 t around inf 45.7%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in c around inf 44.9%

      \[\leadsto \color{blue}{c \cdot \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right)} \]

   if 1.35e11 < y1 < 2.89999999999999974e136

   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 y4 around inf 33.7%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y around -inf 48.6%

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

      1. mul-1-neg 48.6%

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

   6. Simplified 48.6%

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

   if 2.89999999999999974e136 < y1

   1. Initial program 21.3%

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

   3. Taylor expanded in y3 around -inf 37.8%

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

   4. Taylor expanded in y1 around inf 58.7%

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

      1. +-commutative 58.7%

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

      2. mul-1-neg 58.7%

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

      3. unsub-neg 58.7%

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

      4. *-commutative 58.7%

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

   6. Simplified 58.7%

      \[\leadsto -1 \cdot \left(y3 \cdot \color{blue}{\left(y1 \cdot \left(y4 \cdot j - a \cdot z\right)\right)}\right) \]
3. Recombined 6 regimes into one program.

4. Final simplification 52.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -4 \cdot 10^{+85}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -7.8 \cdot 10^{-199}:\\ \;\;\;\;y5 \cdot \left(y0 \cdot \left(j \cdot y3 - k \cdot y2\right) + y \cdot \left(i \cdot k - a \cdot y3\right)\right)\\ \mathbf{elif}\;y1 \leq 9 \cdot 10^{-201}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 135000000000:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 2.9 \cdot 10^{+136}:\\ \;\;\;\;y \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right) - b \cdot \left(k \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 41.5% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y4 \leq -3.5 \cdot 10^{+154}:\\ \;\;\;\;y4 \cdot \left(y \cdot \left(c \cdot y3 - b \cdot k\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 6.8 \cdot 10^{+90}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y4 -3.5e+154)
   (* y4 (+ (* y (- (* c y3) (* b k))) (* y1 (- (* k y2) (* j y3)))))
   (if (<= y4 6.8e+90)
     (*
      j
      (+
       (+ (* y3 (- (* y0 y5) (* y1 y4))) (* t (- (* b y4) (* i y5))))
       (* x (- (* i y1) (* b y0)))))
     (*
      y4
      (-
       (* c (- (* y y3) (* t y2)))
       (+ (* b (- (* y k) (* t j))) (* y1 (- (* j y3) (* k y2)))))))))

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 (y4 <= -3.5e+154) {
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))));
	} else if (y4 <= 6.8e+90) {
		tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))));
	} else {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))));
	}
	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 (y4 <= (-3.5d+154)) then
        tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))))
    else if (y4 <= 6.8d+90) then
        tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))))
    else
        tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))))
    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 (y4 <= -3.5e+154) {
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))));
	} else if (y4 <= 6.8e+90) {
		tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))));
	} else {
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y4 <= -3.5e+154:
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))))
	elif y4 <= 6.8e+90:
		tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))))
	else:
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))))
	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 (y4 <= -3.5e+154)
		tmp = Float64(y4 * Float64(Float64(y * Float64(Float64(c * y3) - Float64(b * k))) + Float64(y1 * Float64(Float64(k * y2) - Float64(j * y3)))));
	elseif (y4 <= 6.8e+90)
		tmp = Float64(j * Float64(Float64(Float64(y3 * Float64(Float64(y0 * y5) - Float64(y1 * y4))) + Float64(t * Float64(Float64(b * y4) - Float64(i * y5)))) + Float64(x * Float64(Float64(i * y1) - Float64(b * y0)))));
	else
		tmp = Float64(y4 * Float64(Float64(c * Float64(Float64(y * y3) - Float64(t * y2))) - Float64(Float64(b * Float64(Float64(y * k) - Float64(t * j))) + Float64(y1 * Float64(Float64(j * y3) - Float64(k * y2))))));
	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 (y4 <= -3.5e+154)
		tmp = y4 * ((y * ((c * y3) - (b * k))) + (y1 * ((k * y2) - (j * y3))));
	elseif (y4 <= 6.8e+90)
		tmp = j * (((y3 * ((y0 * y5) - (y1 * y4))) + (t * ((b * y4) - (i * y5)))) + (x * ((i * y1) - (b * y0))));
	else
		tmp = y4 * ((c * ((y * y3) - (t * y2))) - ((b * ((y * k) - (t * j))) + (y1 * ((j * y3) - (k * y2)))));
	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[y4, -3.5e+154], N[(y4 * N[(N[(y * N[(N[(c * y3), $MachinePrecision] - N[(b * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y1 * N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 6.8e+90], N[(j * N[(N[(N[(y3 * N[(N[(y0 * y5), $MachinePrecision] - N[(y1 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t * N[(N[(b * y4), $MachinePrecision] - N[(i * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x * N[(N[(i * y1), $MachinePrecision] - N[(b * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y4 * N[(N[(c * N[(N[(y * y3), $MachinePrecision] - N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(b * N[(N[(y * k), $MachinePrecision] - N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(y1 * N[(N[(j * y3), $MachinePrecision] - N[(k * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y4 < -3.5000000000000002e154

   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 y around inf 45.3%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y4 around inf 64.8%

      \[\leadsto \color{blue}{y4 \cdot \left(y \cdot \left(-1 \cdot \left(b \cdot k\right) - -1 \cdot \left(c \cdot y3\right)\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)} \]

   if -3.5000000000000002e154 < y4 < 6.80000000000000036e90

   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 j around inf 48.4%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   if 6.80000000000000036e90 < y4

   1. Initial program 38.9%

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

   3. Taylor expanded in y4 around inf 71.8%

      \[\leadsto \color{blue}{y4 \cdot \left(\left(b \cdot \left(j \cdot t - k \cdot y\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right) - c \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 54.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -3.5 \cdot 10^{+154}:\\ \;\;\;\;y4 \cdot \left(y \cdot \left(c \cdot y3 - b \cdot k\right) + y1 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 6.8 \cdot 10^{+90}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot \left(y0 \cdot y5 - y1 \cdot y4\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y4 \cdot \left(c \cdot \left(y \cdot y3 - t \cdot y2\right) - \left(b \cdot \left(y \cdot k - t \cdot j\right) + y1 \cdot \left(j \cdot y3 - k \cdot y2\right)\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 22.0% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;j \leq -7.2 \cdot 10^{-12}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;j \leq -9.2 \cdot 10^{-64}:\\ \;\;\;\;y2 \cdot \left(a \cdot \left(t \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq -6.5 \cdot 10^{-143}:\\ \;\;\;\;a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq 2.7 \cdot 10^{-208}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;j \leq 1.5 \cdot 10^{+33}:\\ \;\;\;\;b \cdot \left(\left(y \cdot y4\right) \cdot \left(-k\right)\right)\\ \mathbf{elif}\;j \leq 8 \cdot 10^{+105}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= j -7.2e-12)
   (* i (* y1 (* x j)))
   (if (<= j -9.2e-64)
     (* y2 (* a (* t y5)))
     (if (<= j -6.5e-143)
       (* a (- (* y (* y3 y5))))
       (if (<= j 2.7e-208)
         (* c (* x (* y0 y2)))
         (if (<= j 1.5e+33)
           (* b (* (* y y4) (- k)))
           (if (<= j 8e+105)
             (* i (* j (* x y1)))
             (* (* y3 y4) (* 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 (j <= -7.2e-12) {
		tmp = i * (y1 * (x * j));
	} else if (j <= -9.2e-64) {
		tmp = y2 * (a * (t * y5));
	} else if (j <= -6.5e-143) {
		tmp = a * -(y * (y3 * y5));
	} else if (j <= 2.7e-208) {
		tmp = c * (x * (y0 * y2));
	} else if (j <= 1.5e+33) {
		tmp = b * ((y * y4) * -k);
	} else if (j <= 8e+105) {
		tmp = i * (j * (x * y1));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	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 (j <= (-7.2d-12)) then
        tmp = i * (y1 * (x * j))
    else if (j <= (-9.2d-64)) then
        tmp = y2 * (a * (t * y5))
    else if (j <= (-6.5d-143)) then
        tmp = a * -(y * (y3 * y5))
    else if (j <= 2.7d-208) then
        tmp = c * (x * (y0 * y2))
    else if (j <= 1.5d+33) then
        tmp = b * ((y * y4) * -k)
    else if (j <= 8d+105) then
        tmp = i * (j * (x * y1))
    else
        tmp = (y3 * y4) * (j * -y1)
    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 (j <= -7.2e-12) {
		tmp = i * (y1 * (x * j));
	} else if (j <= -9.2e-64) {
		tmp = y2 * (a * (t * y5));
	} else if (j <= -6.5e-143) {
		tmp = a * -(y * (y3 * y5));
	} else if (j <= 2.7e-208) {
		tmp = c * (x * (y0 * y2));
	} else if (j <= 1.5e+33) {
		tmp = b * ((y * y4) * -k);
	} else if (j <= 8e+105) {
		tmp = i * (j * (x * y1));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if j <= -7.2e-12:
		tmp = i * (y1 * (x * j))
	elif j <= -9.2e-64:
		tmp = y2 * (a * (t * y5))
	elif j <= -6.5e-143:
		tmp = a * -(y * (y3 * y5))
	elif j <= 2.7e-208:
		tmp = c * (x * (y0 * y2))
	elif j <= 1.5e+33:
		tmp = b * ((y * y4) * -k)
	elif j <= 8e+105:
		tmp = i * (j * (x * y1))
	else:
		tmp = (y3 * y4) * (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 (j <= -7.2e-12)
		tmp = Float64(i * Float64(y1 * Float64(x * j)));
	elseif (j <= -9.2e-64)
		tmp = Float64(y2 * Float64(a * Float64(t * y5)));
	elseif (j <= -6.5e-143)
		tmp = Float64(a * Float64(-Float64(y * Float64(y3 * y5))));
	elseif (j <= 2.7e-208)
		tmp = Float64(c * Float64(x * Float64(y0 * y2)));
	elseif (j <= 1.5e+33)
		tmp = Float64(b * Float64(Float64(y * y4) * Float64(-k)));
	elseif (j <= 8e+105)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	else
		tmp = Float64(Float64(y3 * y4) * Float64(j * Float64(-y1)));
	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 (j <= -7.2e-12)
		tmp = i * (y1 * (x * j));
	elseif (j <= -9.2e-64)
		tmp = y2 * (a * (t * y5));
	elseif (j <= -6.5e-143)
		tmp = a * -(y * (y3 * y5));
	elseif (j <= 2.7e-208)
		tmp = c * (x * (y0 * y2));
	elseif (j <= 1.5e+33)
		tmp = b * ((y * y4) * -k);
	elseif (j <= 8e+105)
		tmp = i * (j * (x * y1));
	else
		tmp = (y3 * y4) * (j * -y1);
	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[j, -7.2e-12], N[(i * N[(y1 * N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, -9.2e-64], N[(y2 * N[(a * N[(t * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, -6.5e-143], N[(a * (-N[(y * N[(y3 * y5), $MachinePrecision]), $MachinePrecision])), $MachinePrecision], If[LessEqual[j, 2.7e-208], N[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 1.5e+33], N[(b * N[(N[(y * y4), $MachinePrecision] * (-k)), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 8e+105], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y3 * y4), $MachinePrecision] * N[(j * (-y1)), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

1. Split input into 7 regimes

2. if j < -7.2e-12

   1. Initial program 36.5%

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

   3. Taylor expanded in j around inf 54.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 34.8%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 34.8%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 34.8%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 27.0%

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

      1. associate-*r* 33.1%

         \[\leadsto i \cdot \color{blue}{\left(\left(j \cdot x\right) \cdot y1\right)} \]

   9. Simplified 33.1%

      \[\leadsto \color{blue}{i \cdot \left(\left(j \cdot x\right) \cdot y1\right)} \]

   if -7.2e-12 < j < -9.2000000000000006e-64

   1. Initial program 42.0%

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

   3. Taylor expanded in y2 around inf 34.8%

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

   4. Taylor expanded in y5 around -inf 26.7%

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

      1. mul-1-neg 26.7%

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

   6. Simplified 26.7%

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

   7. Taylor expanded in k around 0 42.8%

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

      1. *-commutative 42.8%

         \[\leadsto y2 \cdot \left(a \cdot \color{blue}{\left(y5 \cdot t\right)}\right) \]

   9. Simplified 42.8%

      \[\leadsto y2 \cdot \color{blue}{\left(a \cdot \left(y5 \cdot t\right)\right)} \]

   if -9.2000000000000006e-64 < j < -6.4999999999999999e-143

   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 y4 around inf 35.6%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 53.7%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around 0 53.7%

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

      1. mul-1-neg 53.7%

         \[\leadsto \color{blue}{-a \cdot \left(y \cdot \left(y3 \cdot y5\right)\right)} \]

      2. distribute-rgt-neg-in 53.7%

         \[\leadsto \color{blue}{a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)} \]

      3. *-commutative 53.7%

         \[\leadsto a \cdot \left(-\color{blue}{\left(y3 \cdot y5\right) \cdot y}\right) \]

      4. distribute-rgt-neg-in 53.7%

         \[\leadsto a \cdot \color{blue}{\left(\left(y3 \cdot y5\right) \cdot \left(-y\right)\right)} \]

   7. Simplified 53.7%

      \[\leadsto \color{blue}{a \cdot \left(\left(y3 \cdot y5\right) \cdot \left(-y\right)\right)} \]

   if -6.4999999999999999e-143 < j < 2.7e-208

   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 42.9%

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

   4. Taylor expanded in c around inf 33.7%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 29.5%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if 2.7e-208 < j < 1.49999999999999992e33

   1. Initial program 32.5%

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

   3. Taylor expanded in y4 around inf 39.9%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in k around inf 36.0%

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

   5. Taylor expanded in b around inf 33.6%

      \[\leadsto \color{blue}{-1 \cdot \left(b \cdot \left(k \cdot \left(y \cdot y4\right)\right)\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 33.6%

         \[\leadsto \color{blue}{-b \cdot \left(k \cdot \left(y \cdot y4\right)\right)} \]

      2. *-commutative 33.6%

         \[\leadsto -\color{blue}{\left(k \cdot \left(y \cdot y4\right)\right) \cdot b} \]

      3. distribute-rgt-neg-in 33.6%

         \[\leadsto \color{blue}{\left(k \cdot \left(y \cdot y4\right)\right) \cdot \left(-b\right)} \]

      4. *-commutative 33.6%

         \[\leadsto \left(k \cdot \color{blue}{\left(y4 \cdot y\right)}\right) \cdot \left(-b\right) \]

   7. Simplified 33.6%

      \[\leadsto \color{blue}{\left(k \cdot \left(y4 \cdot y\right)\right) \cdot \left(-b\right)} \]

   if 1.49999999999999992e33 < j < 7.9999999999999995e105

   1. Initial program 45.3%

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

   3. Taylor expanded in j around inf 45.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 29.1%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 29.1%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 29.1%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 47.0%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if 7.9999999999999995e105 < j

   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 j around inf 62.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 44.7%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 44.7%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 44.7%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around inf 36.8%

      \[\leadsto \color{blue}{-1 \cdot \left(j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)\right)} \]
   8. Step-by-step derivation

      1. mul-1-neg 36.8%

         \[\leadsto \color{blue}{-j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)} \]

      2. associate-*r* 40.7%

         \[\leadsto -\color{blue}{\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]

   9. Simplified 40.7%

      \[\leadsto \color{blue}{-\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]
3. Recombined 7 regimes into one program.

4. Final simplification 36.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;j \leq -7.2 \cdot 10^{-12}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;j \leq -9.2 \cdot 10^{-64}:\\ \;\;\;\;y2 \cdot \left(a \cdot \left(t \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq -6.5 \cdot 10^{-143}:\\ \;\;\;\;a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq 2.7 \cdot 10^{-208}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;j \leq 1.5 \cdot 10^{+33}:\\ \;\;\;\;b \cdot \left(\left(y \cdot y4\right) \cdot \left(-k\right)\right)\\ \mathbf{elif}\;j \leq 8 \cdot 10^{+105}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 22.0% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;j \leq -3 \cdot 10^{-11}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;j \leq -8 \cdot 10^{-64}:\\ \;\;\;\;y2 \cdot \left(a \cdot \left(t \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq -1.45 \cdot 10^{-147}:\\ \;\;\;\;a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq 1.45 \cdot 10^{-208}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;j \leq 1.55 \cdot 10^{+31}:\\ \;\;\;\;k \cdot \left(b \cdot \left(y \cdot \left(-y4\right)\right)\right)\\ \mathbf{elif}\;j \leq 2.4 \cdot 10^{+105}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= j -3e-11)
   (* i (* y1 (* x j)))
   (if (<= j -8e-64)
     (* y2 (* a (* t y5)))
     (if (<= j -1.45e-147)
       (* a (- (* y (* y3 y5))))
       (if (<= j 1.45e-208)
         (* c (* x (* y0 y2)))
         (if (<= j 1.55e+31)
           (* k (* b (* y (- y4))))
           (if (<= j 2.4e+105)
             (* i (* j (* x y1)))
             (* (* y3 y4) (* 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 (j <= -3e-11) {
		tmp = i * (y1 * (x * j));
	} else if (j <= -8e-64) {
		tmp = y2 * (a * (t * y5));
	} else if (j <= -1.45e-147) {
		tmp = a * -(y * (y3 * y5));
	} else if (j <= 1.45e-208) {
		tmp = c * (x * (y0 * y2));
	} else if (j <= 1.55e+31) {
		tmp = k * (b * (y * -y4));
	} else if (j <= 2.4e+105) {
		tmp = i * (j * (x * y1));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	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 (j <= (-3d-11)) then
        tmp = i * (y1 * (x * j))
    else if (j <= (-8d-64)) then
        tmp = y2 * (a * (t * y5))
    else if (j <= (-1.45d-147)) then
        tmp = a * -(y * (y3 * y5))
    else if (j <= 1.45d-208) then
        tmp = c * (x * (y0 * y2))
    else if (j <= 1.55d+31) then
        tmp = k * (b * (y * -y4))
    else if (j <= 2.4d+105) then
        tmp = i * (j * (x * y1))
    else
        tmp = (y3 * y4) * (j * -y1)
    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 (j <= -3e-11) {
		tmp = i * (y1 * (x * j));
	} else if (j <= -8e-64) {
		tmp = y2 * (a * (t * y5));
	} else if (j <= -1.45e-147) {
		tmp = a * -(y * (y3 * y5));
	} else if (j <= 1.45e-208) {
		tmp = c * (x * (y0 * y2));
	} else if (j <= 1.55e+31) {
		tmp = k * (b * (y * -y4));
	} else if (j <= 2.4e+105) {
		tmp = i * (j * (x * y1));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if j <= -3e-11:
		tmp = i * (y1 * (x * j))
	elif j <= -8e-64:
		tmp = y2 * (a * (t * y5))
	elif j <= -1.45e-147:
		tmp = a * -(y * (y3 * y5))
	elif j <= 1.45e-208:
		tmp = c * (x * (y0 * y2))
	elif j <= 1.55e+31:
		tmp = k * (b * (y * -y4))
	elif j <= 2.4e+105:
		tmp = i * (j * (x * y1))
	else:
		tmp = (y3 * y4) * (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 (j <= -3e-11)
		tmp = Float64(i * Float64(y1 * Float64(x * j)));
	elseif (j <= -8e-64)
		tmp = Float64(y2 * Float64(a * Float64(t * y5)));
	elseif (j <= -1.45e-147)
		tmp = Float64(a * Float64(-Float64(y * Float64(y3 * y5))));
	elseif (j <= 1.45e-208)
		tmp = Float64(c * Float64(x * Float64(y0 * y2)));
	elseif (j <= 1.55e+31)
		tmp = Float64(k * Float64(b * Float64(y * Float64(-y4))));
	elseif (j <= 2.4e+105)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	else
		tmp = Float64(Float64(y3 * y4) * Float64(j * Float64(-y1)));
	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 (j <= -3e-11)
		tmp = i * (y1 * (x * j));
	elseif (j <= -8e-64)
		tmp = y2 * (a * (t * y5));
	elseif (j <= -1.45e-147)
		tmp = a * -(y * (y3 * y5));
	elseif (j <= 1.45e-208)
		tmp = c * (x * (y0 * y2));
	elseif (j <= 1.55e+31)
		tmp = k * (b * (y * -y4));
	elseif (j <= 2.4e+105)
		tmp = i * (j * (x * y1));
	else
		tmp = (y3 * y4) * (j * -y1);
	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[j, -3e-11], N[(i * N[(y1 * N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, -8e-64], N[(y2 * N[(a * N[(t * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, -1.45e-147], N[(a * (-N[(y * N[(y3 * y5), $MachinePrecision]), $MachinePrecision])), $MachinePrecision], If[LessEqual[j, 1.45e-208], N[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 1.55e+31], N[(k * N[(b * N[(y * (-y4)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 2.4e+105], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y3 * y4), $MachinePrecision] * N[(j * (-y1)), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

1. Split input into 7 regimes

2. if j < -3e-11

   1. Initial program 36.5%

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

   3. Taylor expanded in j around inf 54.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 34.8%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 34.8%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 34.8%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 27.0%

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

      1. associate-*r* 33.1%

         \[\leadsto i \cdot \color{blue}{\left(\left(j \cdot x\right) \cdot y1\right)} \]

   9. Simplified 33.1%

      \[\leadsto \color{blue}{i \cdot \left(\left(j \cdot x\right) \cdot y1\right)} \]

   if -3e-11 < j < -7.99999999999999972e-64

   1. Initial program 42.0%

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

   3. Taylor expanded in y2 around inf 34.8%

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

   4. Taylor expanded in y5 around -inf 26.7%

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

      1. mul-1-neg 26.7%

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

   6. Simplified 26.7%

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

   7. Taylor expanded in k around 0 42.8%

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

      1. *-commutative 42.8%

         \[\leadsto y2 \cdot \left(a \cdot \color{blue}{\left(y5 \cdot t\right)}\right) \]

   9. Simplified 42.8%

      \[\leadsto y2 \cdot \color{blue}{\left(a \cdot \left(y5 \cdot t\right)\right)} \]

   if -7.99999999999999972e-64 < j < -1.4500000000000001e-147

   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 y4 around inf 35.6%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 53.7%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around 0 53.7%

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

      1. mul-1-neg 53.7%

         \[\leadsto \color{blue}{-a \cdot \left(y \cdot \left(y3 \cdot y5\right)\right)} \]

      2. distribute-rgt-neg-in 53.7%

         \[\leadsto \color{blue}{a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)} \]

      3. *-commutative 53.7%

         \[\leadsto a \cdot \left(-\color{blue}{\left(y3 \cdot y5\right) \cdot y}\right) \]

      4. distribute-rgt-neg-in 53.7%

         \[\leadsto a \cdot \color{blue}{\left(\left(y3 \cdot y5\right) \cdot \left(-y\right)\right)} \]

   7. Simplified 53.7%

      \[\leadsto \color{blue}{a \cdot \left(\left(y3 \cdot y5\right) \cdot \left(-y\right)\right)} \]

   if -1.4500000000000001e-147 < j < 1.45e-208

   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 42.9%

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

   4. Taylor expanded in c around inf 33.7%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 29.5%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if 1.45e-208 < j < 1.5500000000000001e31

   1. Initial program 32.5%

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

   3. Taylor expanded in y4 around inf 39.9%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in k around inf 36.0%

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

   5. Taylor expanded in b around inf 31.4%

      \[\leadsto k \cdot \color{blue}{\left(-1 \cdot \left(b \cdot \left(y \cdot y4\right)\right)\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 31.4%

         \[\leadsto k \cdot \color{blue}{\left(-b \cdot \left(y \cdot y4\right)\right)} \]

      2. *-commutative 31.4%

         \[\leadsto k \cdot \left(-b \cdot \color{blue}{\left(y4 \cdot y\right)}\right) \]

      3. distribute-rgt-neg-in 31.4%

         \[\leadsto k \cdot \color{blue}{\left(b \cdot \left(-y4 \cdot y\right)\right)} \]

      4. distribute-rgt-neg-in 31.4%

         \[\leadsto k \cdot \left(b \cdot \color{blue}{\left(y4 \cdot \left(-y\right)\right)}\right) \]

   7. Simplified 31.4%

      \[\leadsto k \cdot \color{blue}{\left(b \cdot \left(y4 \cdot \left(-y\right)\right)\right)} \]

   if 1.5500000000000001e31 < j < 2.39999999999999975e105

   1. Initial program 45.3%

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

   3. Taylor expanded in j around inf 45.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 29.1%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 29.1%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 29.1%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 47.0%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if 2.39999999999999975e105 < j

   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 j around inf 62.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 44.7%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 44.7%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 44.7%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around inf 36.8%

      \[\leadsto \color{blue}{-1 \cdot \left(j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)\right)} \]
   8. Step-by-step derivation

      1. mul-1-neg 36.8%

         \[\leadsto \color{blue}{-j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)} \]

      2. associate-*r* 40.7%

         \[\leadsto -\color{blue}{\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]

   9. Simplified 40.7%

      \[\leadsto \color{blue}{-\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]
3. Recombined 7 regimes into one program.

4. Final simplification 36.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;j \leq -3 \cdot 10^{-11}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;j \leq -8 \cdot 10^{-64}:\\ \;\;\;\;y2 \cdot \left(a \cdot \left(t \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq -1.45 \cdot 10^{-147}:\\ \;\;\;\;a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq 1.45 \cdot 10^{-208}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;j \leq 1.55 \cdot 10^{+31}:\\ \;\;\;\;k \cdot \left(b \cdot \left(y \cdot \left(-y4\right)\right)\right)\\ \mathbf{elif}\;j \leq 2.4 \cdot 10^{+105}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 20.4% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(k \cdot y0\right) \cdot \left(y2 \cdot \left(-y5\right)\right)\\ \mathbf{if}\;y2 \leq -1.3 \cdot 10^{+148}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq -6200000:\\ \;\;\;\;b \cdot \left(\left(y \cdot y4\right) \cdot \left(-k\right)\right)\\ \mathbf{elif}\;y2 \leq 9.6 \cdot 10^{-163}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 1.55 \cdot 10^{-43}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq 6 \cdot 10^{+89}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 1.55 \cdot 10^{+239}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* (* k y0) (* y2 (- y5)))))
   (if (<= y2 -1.3e+148)
     t_1
     (if (<= y2 -6200000.0)
       (* b (* (* y y4) (- k)))
       (if (<= y2 9.6e-163)
         (* b (* y4 (* t j)))
         (if (<= y2 1.55e-43)
           t_1
           (if (<= y2 6e+89)
             (* i (* j (* x y1)))
             (if (<= y2 1.55e+239)
               (* c (* x (* y0 y2)))
               (* a (* y5 (* t y2)))))))))))

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 <= -1.3e+148) {
		tmp = t_1;
	} else if (y2 <= -6200000.0) {
		tmp = b * ((y * y4) * -k);
	} else if (y2 <= 9.6e-163) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 1.55e-43) {
		tmp = t_1;
	} else if (y2 <= 6e+89) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 1.55e+239) {
		tmp = c * (x * (y0 * y2));
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 * y0) * (y2 * -y5)
    if (y2 <= (-1.3d+148)) then
        tmp = t_1
    else if (y2 <= (-6200000.0d0)) then
        tmp = b * ((y * y4) * -k)
    else if (y2 <= 9.6d-163) then
        tmp = b * (y4 * (t * j))
    else if (y2 <= 1.55d-43) then
        tmp = t_1
    else if (y2 <= 6d+89) then
        tmp = i * (j * (x * y1))
    else if (y2 <= 1.55d+239) then
        tmp = c * (x * (y0 * y2))
    else
        tmp = a * (y5 * (t * y2))
    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 * y0) * (y2 * -y5);
	double tmp;
	if (y2 <= -1.3e+148) {
		tmp = t_1;
	} else if (y2 <= -6200000.0) {
		tmp = b * ((y * y4) * -k);
	} else if (y2 <= 9.6e-163) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 1.55e-43) {
		tmp = t_1;
	} else if (y2 <= 6e+89) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 1.55e+239) {
		tmp = c * (x * (y0 * y2));
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = (k * y0) * (y2 * -y5)
	tmp = 0
	if y2 <= -1.3e+148:
		tmp = t_1
	elif y2 <= -6200000.0:
		tmp = b * ((y * y4) * -k)
	elif y2 <= 9.6e-163:
		tmp = b * (y4 * (t * j))
	elif y2 <= 1.55e-43:
		tmp = t_1
	elif y2 <= 6e+89:
		tmp = i * (j * (x * y1))
	elif y2 <= 1.55e+239:
		tmp = c * (x * (y0 * y2))
	else:
		tmp = a * (y5 * (t * y2))
	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 * y0) * Float64(y2 * Float64(-y5)))
	tmp = 0.0
	if (y2 <= -1.3e+148)
		tmp = t_1;
	elseif (y2 <= -6200000.0)
		tmp = Float64(b * Float64(Float64(y * y4) * Float64(-k)));
	elseif (y2 <= 9.6e-163)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	elseif (y2 <= 1.55e-43)
		tmp = t_1;
	elseif (y2 <= 6e+89)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	elseif (y2 <= 1.55e+239)
		tmp = Float64(c * Float64(x * Float64(y0 * y2)));
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 * y0) * (y2 * -y5);
	tmp = 0.0;
	if (y2 <= -1.3e+148)
		tmp = t_1;
	elseif (y2 <= -6200000.0)
		tmp = b * ((y * y4) * -k);
	elseif (y2 <= 9.6e-163)
		tmp = b * (y4 * (t * j));
	elseif (y2 <= 1.55e-43)
		tmp = t_1;
	elseif (y2 <= 6e+89)
		tmp = i * (j * (x * y1));
	elseif (y2 <= 1.55e+239)
		tmp = c * (x * (y0 * y2));
	else
		tmp = a * (y5 * (t * y2));
	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[(k * y0), $MachinePrecision] * N[(y2 * (-y5)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y2, -1.3e+148], t$95$1, If[LessEqual[y2, -6200000.0], N[(b * N[(N[(y * y4), $MachinePrecision] * (-k)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 9.6e-163], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 1.55e-43], t$95$1, If[LessEqual[y2, 6e+89], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 1.55e+239], N[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if y2 < -1.3e148 or 9.6000000000000003e-163 < y2 < 1.55e-43

   1. Initial program 28.7%

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

   3. Taylor expanded in y2 around inf 38.7%

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

   4. Taylor expanded in y5 around -inf 39.0%

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

      1. mul-1-neg 39.0%

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

   6. Simplified 39.0%

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

   7. Taylor expanded in k around inf 30.3%

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

      1. mul-1-neg 30.3%

         \[\leadsto \color{blue}{-k \cdot \left(y0 \cdot \left(y2 \cdot y5\right)\right)} \]

      2. associate-*r* 34.6%

         \[\leadsto -\color{blue}{\left(k \cdot y0\right) \cdot \left(y2 \cdot y5\right)} \]

      3. *-commutative 34.6%

         \[\leadsto -\color{blue}{\left(y0 \cdot k\right)} \cdot \left(y2 \cdot y5\right) \]

      4. distribute-rgt-neg-in 34.6%

         \[\leadsto \color{blue}{\left(y0 \cdot k\right) \cdot \left(-y2 \cdot y5\right)} \]

      5. *-commutative 34.6%

         \[\leadsto \left(y0 \cdot k\right) \cdot \left(-\color{blue}{y5 \cdot y2}\right) \]

   9. Simplified 34.6%

      \[\leadsto \color{blue}{\left(y0 \cdot k\right) \cdot \left(-y5 \cdot y2\right)} \]

   if -1.3e148 < y2 < -6.2e6

   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 y4 around inf 47.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in k around inf 47.5%

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

   5. Taylor expanded in b around inf 41.4%

      \[\leadsto \color{blue}{-1 \cdot \left(b \cdot \left(k \cdot \left(y \cdot y4\right)\right)\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 41.4%

         \[\leadsto \color{blue}{-b \cdot \left(k \cdot \left(y \cdot y4\right)\right)} \]

      2. *-commutative 41.4%

         \[\leadsto -\color{blue}{\left(k \cdot \left(y \cdot y4\right)\right) \cdot b} \]

      3. distribute-rgt-neg-in 41.4%

         \[\leadsto \color{blue}{\left(k \cdot \left(y \cdot y4\right)\right) \cdot \left(-b\right)} \]

      4. *-commutative 41.4%

         \[\leadsto \left(k \cdot \color{blue}{\left(y4 \cdot y\right)}\right) \cdot \left(-b\right) \]

   7. Simplified 41.4%

      \[\leadsto \color{blue}{\left(k \cdot \left(y4 \cdot y\right)\right) \cdot \left(-b\right)} \]

   if -6.2e6 < y2 < 9.6000000000000003e-163

   1. Initial program 38.9%

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

   3. Taylor expanded in y4 around inf 42.9%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 32.5%

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

   5. Taylor expanded in j around inf 27.6%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 29.8%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 29.8%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 1.55e-43 < y2 < 6.00000000000000025e89

   1. Initial program 39.5%

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

   3. Taylor expanded in j around inf 45.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 37.1%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 37.1%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 37.1%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 34.7%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if 6.00000000000000025e89 < y2 < 1.55e239

   1. Initial program 12.3%

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

   3. Taylor expanded in y0 around inf 52.3%

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

   4. Taylor expanded in c around inf 37.9%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 49.2%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if 1.55e239 < y2

   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 y4 around inf 40.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 67.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 67.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 6 regimes into one program.

4. Final simplification 37.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -1.3 \cdot 10^{+148}:\\ \;\;\;\;\left(k \cdot y0\right) \cdot \left(y2 \cdot \left(-y5\right)\right)\\ \mathbf{elif}\;y2 \leq -6200000:\\ \;\;\;\;b \cdot \left(\left(y \cdot y4\right) \cdot \left(-k\right)\right)\\ \mathbf{elif}\;y2 \leq 9.6 \cdot 10^{-163}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 1.55 \cdot 10^{-43}:\\ \;\;\;\;\left(k \cdot y0\right) \cdot \left(y2 \cdot \left(-y5\right)\right)\\ \mathbf{elif}\;y2 \leq 6 \cdot 10^{+89}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 1.55 \cdot 10^{+239}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 20.8% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := k \cdot \left(y0 \cdot \left(y2 \cdot \left(-y5\right)\right)\right)\\ \mathbf{if}\;y2 \leq -1.05 \cdot 10^{+178}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq -3900000:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(y \cdot \left(-k\right)\right)\right)\\ \mathbf{elif}\;y2 \leq 2.6 \cdot 10^{-131}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 1.56 \cdot 10^{-43}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq 2.8 \cdot 10^{+91}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 3.3 \cdot 10^{+239}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* k (* y0 (* y2 (- y5))))))
   (if (<= y2 -1.05e+178)
     t_1
     (if (<= y2 -3900000.0)
       (* b (* y4 (* y (- k))))
       (if (<= y2 2.6e-131)
         (* b (* y4 (* t j)))
         (if (<= y2 1.56e-43)
           t_1
           (if (<= y2 2.8e+91)
             (* i (* j (* x y1)))
             (if (<= y2 3.3e+239)
               (* c (* x (* y0 y2)))
               (* a (* y5 (* t y2)))))))))))

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 <= -1.05e+178) {
		tmp = t_1;
	} else if (y2 <= -3900000.0) {
		tmp = b * (y4 * (y * -k));
	} else if (y2 <= 2.6e-131) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 1.56e-43) {
		tmp = t_1;
	} else if (y2 <= 2.8e+91) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 3.3e+239) {
		tmp = c * (x * (y0 * y2));
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 * (y0 * (y2 * -y5))
    if (y2 <= (-1.05d+178)) then
        tmp = t_1
    else if (y2 <= (-3900000.0d0)) then
        tmp = b * (y4 * (y * -k))
    else if (y2 <= 2.6d-131) then
        tmp = b * (y4 * (t * j))
    else if (y2 <= 1.56d-43) then
        tmp = t_1
    else if (y2 <= 2.8d+91) then
        tmp = i * (j * (x * y1))
    else if (y2 <= 3.3d+239) then
        tmp = c * (x * (y0 * y2))
    else
        tmp = a * (y5 * (t * y2))
    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 * (y0 * (y2 * -y5));
	double tmp;
	if (y2 <= -1.05e+178) {
		tmp = t_1;
	} else if (y2 <= -3900000.0) {
		tmp = b * (y4 * (y * -k));
	} else if (y2 <= 2.6e-131) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 1.56e-43) {
		tmp = t_1;
	} else if (y2 <= 2.8e+91) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 3.3e+239) {
		tmp = c * (x * (y0 * y2));
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = k * (y0 * (y2 * -y5))
	tmp = 0
	if y2 <= -1.05e+178:
		tmp = t_1
	elif y2 <= -3900000.0:
		tmp = b * (y4 * (y * -k))
	elif y2 <= 2.6e-131:
		tmp = b * (y4 * (t * j))
	elif y2 <= 1.56e-43:
		tmp = t_1
	elif y2 <= 2.8e+91:
		tmp = i * (j * (x * y1))
	elif y2 <= 3.3e+239:
		tmp = c * (x * (y0 * y2))
	else:
		tmp = a * (y5 * (t * y2))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(k * Float64(y0 * Float64(y2 * Float64(-y5))))
	tmp = 0.0
	if (y2 <= -1.05e+178)
		tmp = t_1;
	elseif (y2 <= -3900000.0)
		tmp = Float64(b * Float64(y4 * Float64(y * Float64(-k))));
	elseif (y2 <= 2.6e-131)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	elseif (y2 <= 1.56e-43)
		tmp = t_1;
	elseif (y2 <= 2.8e+91)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	elseif (y2 <= 3.3e+239)
		tmp = Float64(c * Float64(x * Float64(y0 * y2)));
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 * (y0 * (y2 * -y5));
	tmp = 0.0;
	if (y2 <= -1.05e+178)
		tmp = t_1;
	elseif (y2 <= -3900000.0)
		tmp = b * (y4 * (y * -k));
	elseif (y2 <= 2.6e-131)
		tmp = b * (y4 * (t * j));
	elseif (y2 <= 1.56e-43)
		tmp = t_1;
	elseif (y2 <= 2.8e+91)
		tmp = i * (j * (x * y1));
	elseif (y2 <= 3.3e+239)
		tmp = c * (x * (y0 * y2));
	else
		tmp = a * (y5 * (t * y2));
	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[(k * N[(y0 * N[(y2 * (-y5)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y2, -1.05e+178], t$95$1, If[LessEqual[y2, -3900000.0], N[(b * N[(y4 * N[(y * (-k)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 2.6e-131], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 1.56e-43], t$95$1, If[LessEqual[y2, 2.8e+91], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 3.3e+239], N[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if y2 < -1.0499999999999999e178 or 2.59999999999999996e-131 < y2 < 1.5600000000000001e-43

   1. Initial program 24.6%

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

   3. Taylor expanded in y4 around inf 39.2%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in k around inf 37.7%

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

   5. Taylor expanded in y4 around 0 38.2%

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

      1. mul-1-neg 38.2%

         \[\leadsto \color{blue}{-k \cdot \left(y0 \cdot \left(y2 \cdot y5\right)\right)} \]

      2. distribute-rgt-neg-in 38.2%

         \[\leadsto \color{blue}{k \cdot \left(-y0 \cdot \left(y2 \cdot y5\right)\right)} \]

      3. distribute-rgt-neg-in 38.2%

         \[\leadsto k \cdot \color{blue}{\left(y0 \cdot \left(-y2 \cdot y5\right)\right)} \]

      4. *-commutative 38.2%

         \[\leadsto k \cdot \left(y0 \cdot \left(-\color{blue}{y5 \cdot y2}\right)\right) \]

      5. distribute-rgt-neg-in 38.2%

         \[\leadsto k \cdot \left(y0 \cdot \color{blue}{\left(y5 \cdot \left(-y2\right)\right)}\right) \]

   7. Simplified 38.2%

      \[\leadsto \color{blue}{k \cdot \left(y0 \cdot \left(y5 \cdot \left(-y2\right)\right)\right)} \]

   if -1.0499999999999999e178 < y2 < -3.9e6

   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 y4 around inf 46.2%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 36.9%

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

   5. Taylor expanded in j around 0 34.5%

      \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(-1 \cdot \left(k \cdot y\right)\right)}\right) \]
   6. Step-by-step derivation

      1. mul-1-neg 34.5%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(-k \cdot y\right)}\right) \]

      2. distribute-lft-neg-out 34.5%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(\left(-k\right) \cdot y\right)}\right) \]

      3. *-commutative 34.5%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(y \cdot \left(-k\right)\right)}\right) \]

   7. Simplified 34.5%

      \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(y \cdot \left(-k\right)\right)}\right) \]

   if -3.9e6 < y2 < 2.59999999999999996e-131

   1. Initial program 41.3%

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

   3. Taylor expanded in y4 around inf 42.9%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 32.3%

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

   5. Taylor expanded in j around inf 26.7%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 28.7%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 28.7%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 1.5600000000000001e-43 < y2 < 2.7999999999999999e91

   1. Initial program 39.5%

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

   3. Taylor expanded in j around inf 45.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 37.1%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 37.1%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 37.1%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 34.7%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if 2.7999999999999999e91 < y2 < 3.2999999999999998e239

   1. Initial program 12.3%

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

   3. Taylor expanded in y0 around inf 52.3%

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

   4. Taylor expanded in c around inf 37.9%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 49.2%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if 3.2999999999999998e239 < y2

   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 y4 around inf 40.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 67.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 67.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 6 regimes into one program.

4. Final simplification 36.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -1.05 \cdot 10^{+178}:\\ \;\;\;\;k \cdot \left(y0 \cdot \left(y2 \cdot \left(-y5\right)\right)\right)\\ \mathbf{elif}\;y2 \leq -3900000:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(y \cdot \left(-k\right)\right)\right)\\ \mathbf{elif}\;y2 \leq 2.6 \cdot 10^{-131}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 1.56 \cdot 10^{-43}:\\ \;\;\;\;k \cdot \left(y0 \cdot \left(y2 \cdot \left(-y5\right)\right)\right)\\ \mathbf{elif}\;y2 \leq 2.8 \cdot 10^{+91}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 3.3 \cdot 10^{+239}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 21.9% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{if}\;y2 \leq -9 \cdot 10^{+154}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq -8200000:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(y \cdot \left(-k\right)\right)\right)\\ \mathbf{elif}\;y2 \leq 1.55 \cdot 10^{-163}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 2 \cdot 10^{-43}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot y4\right) \cdot \left(-y1\right)\right)\\ \mathbf{elif}\;y2 \leq 4.5 \cdot 10^{+89}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 3.9 \cdot 10^{+238}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* c (* x (* y0 y2)))))
   (if (<= y2 -9e+154)
     t_1
     (if (<= y2 -8200000.0)
       (* b (* y4 (* y (- k))))
       (if (<= y2 1.55e-163)
         (* b (* y4 (* t j)))
         (if (<= y2 2e-43)
           (* j (* (* y3 y4) (- y1)))
           (if (<= y2 4.5e+89)
             (* i (* j (* x y1)))
             (if (<= y2 3.9e+238) t_1 (* a (* y5 (* t y2)))))))))))

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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -9e+154) {
		tmp = t_1;
	} else if (y2 <= -8200000.0) {
		tmp = b * (y4 * (y * -k));
	} else if (y2 <= 1.55e-163) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 2e-43) {
		tmp = j * ((y3 * y4) * -y1);
	} else if (y2 <= 4.5e+89) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 3.9e+238) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 = c * (x * (y0 * y2))
    if (y2 <= (-9d+154)) then
        tmp = t_1
    else if (y2 <= (-8200000.0d0)) then
        tmp = b * (y4 * (y * -k))
    else if (y2 <= 1.55d-163) then
        tmp = b * (y4 * (t * j))
    else if (y2 <= 2d-43) then
        tmp = j * ((y3 * y4) * -y1)
    else if (y2 <= 4.5d+89) then
        tmp = i * (j * (x * y1))
    else if (y2 <= 3.9d+238) then
        tmp = t_1
    else
        tmp = a * (y5 * (t * y2))
    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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -9e+154) {
		tmp = t_1;
	} else if (y2 <= -8200000.0) {
		tmp = b * (y4 * (y * -k));
	} else if (y2 <= 1.55e-163) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 2e-43) {
		tmp = j * ((y3 * y4) * -y1);
	} else if (y2 <= 4.5e+89) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 3.9e+238) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = c * (x * (y0 * y2))
	tmp = 0
	if y2 <= -9e+154:
		tmp = t_1
	elif y2 <= -8200000.0:
		tmp = b * (y4 * (y * -k))
	elif y2 <= 1.55e-163:
		tmp = b * (y4 * (t * j))
	elif y2 <= 2e-43:
		tmp = j * ((y3 * y4) * -y1)
	elif y2 <= 4.5e+89:
		tmp = i * (j * (x * y1))
	elif y2 <= 3.9e+238:
		tmp = t_1
	else:
		tmp = a * (y5 * (t * y2))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(c * Float64(x * Float64(y0 * y2)))
	tmp = 0.0
	if (y2 <= -9e+154)
		tmp = t_1;
	elseif (y2 <= -8200000.0)
		tmp = Float64(b * Float64(y4 * Float64(y * Float64(-k))));
	elseif (y2 <= 1.55e-163)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	elseif (y2 <= 2e-43)
		tmp = Float64(j * Float64(Float64(y3 * y4) * Float64(-y1)));
	elseif (y2 <= 4.5e+89)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	elseif (y2 <= 3.9e+238)
		tmp = t_1;
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 = c * (x * (y0 * y2));
	tmp = 0.0;
	if (y2 <= -9e+154)
		tmp = t_1;
	elseif (y2 <= -8200000.0)
		tmp = b * (y4 * (y * -k));
	elseif (y2 <= 1.55e-163)
		tmp = b * (y4 * (t * j));
	elseif (y2 <= 2e-43)
		tmp = j * ((y3 * y4) * -y1);
	elseif (y2 <= 4.5e+89)
		tmp = i * (j * (x * y1));
	elseif (y2 <= 3.9e+238)
		tmp = t_1;
	else
		tmp = a * (y5 * (t * y2));
	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[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y2, -9e+154], t$95$1, If[LessEqual[y2, -8200000.0], N[(b * N[(y4 * N[(y * (-k)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 1.55e-163], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 2e-43], N[(j * N[(N[(y3 * y4), $MachinePrecision] * (-y1)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 4.5e+89], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 3.9e+238], t$95$1, N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if y2 < -9.00000000000000018e154 or 4.5e89 < y2 < 3.89999999999999993e238

   1. Initial program 15.5%

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

   3. Taylor expanded in y0 around inf 36.9%

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

   4. Taylor expanded in c around inf 34.0%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 43.2%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if -9.00000000000000018e154 < y2 < -8.2e6

   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 y4 around inf 47.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 36.5%

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

   5. Taylor expanded in j around 0 36.6%

      \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(-1 \cdot \left(k \cdot y\right)\right)}\right) \]
   6. Step-by-step derivation

      1. mul-1-neg 36.6%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(-k \cdot y\right)}\right) \]

      2. distribute-lft-neg-out 36.6%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(\left(-k\right) \cdot y\right)}\right) \]

      3. *-commutative 36.6%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(y \cdot \left(-k\right)\right)}\right) \]

   7. Simplified 36.6%

      \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(y \cdot \left(-k\right)\right)}\right) \]

   if -8.2e6 < y2 < 1.54999999999999987e-163

   1. Initial program 38.9%

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

   3. Taylor expanded in y4 around inf 42.9%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 32.5%

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

   5. Taylor expanded in j around inf 27.6%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 29.8%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 29.8%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 1.54999999999999987e-163 < y2 < 2.00000000000000015e-43

   1. Initial program 37.3%

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

   3. Taylor expanded in j around inf 49.3%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 32.6%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 32.6%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 32.6%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around inf 29.6%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)\right)} \]
   8. Step-by-step derivation

      1. associate-*r* 29.6%

         \[\leadsto j \cdot \color{blue}{\left(\left(-1 \cdot y1\right) \cdot \left(y3 \cdot y4\right)\right)} \]

      2. neg-mul-1 29.6%

         \[\leadsto j \cdot \left(\color{blue}{\left(-y1\right)} \cdot \left(y3 \cdot y4\right)\right) \]

   9. Simplified 29.6%

      \[\leadsto j \cdot \color{blue}{\left(\left(-y1\right) \cdot \left(y3 \cdot y4\right)\right)} \]

   if 2.00000000000000015e-43 < y2 < 4.5e89

   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 j around inf 46.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 38.3%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 38.3%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 38.3%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 35.7%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if 3.89999999999999993e238 < y2

   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 y4 around inf 40.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 67.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 67.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 6 regimes into one program.

4. Final simplification 36.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -9 \cdot 10^{+154}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;y2 \leq -8200000:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(y \cdot \left(-k\right)\right)\right)\\ \mathbf{elif}\;y2 \leq 1.55 \cdot 10^{-163}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 2 \cdot 10^{-43}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot y4\right) \cdot \left(-y1\right)\right)\\ \mathbf{elif}\;y2 \leq 4.5 \cdot 10^{+89}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 3.9 \cdot 10^{+238}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 21.7% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{if}\;y2 \leq -2.1 \cdot 10^{+151}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq -7000000:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(y \cdot \left(-k\right)\right)\right)\\ \mathbf{elif}\;y2 \leq 4.5 \cdot 10^{-124}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 2.2 \cdot 10^{-43}:\\ \;\;\;\;\left(y3 \cdot y5\right) \cdot \left(y \cdot \left(-a\right)\right)\\ \mathbf{elif}\;y2 \leq 5.4 \cdot 10^{+91}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 5.1 \cdot 10^{+237}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* c (* x (* y0 y2)))))
   (if (<= y2 -2.1e+151)
     t_1
     (if (<= y2 -7000000.0)
       (* b (* y4 (* y (- k))))
       (if (<= y2 4.5e-124)
         (* b (* y4 (* t j)))
         (if (<= y2 2.2e-43)
           (* (* y3 y5) (* y (- a)))
           (if (<= y2 5.4e+91)
             (* i (* j (* x y1)))
             (if (<= y2 5.1e+237) t_1 (* a (* y5 (* t y2)))))))))))

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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -2.1e+151) {
		tmp = t_1;
	} else if (y2 <= -7000000.0) {
		tmp = b * (y4 * (y * -k));
	} else if (y2 <= 4.5e-124) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 2.2e-43) {
		tmp = (y3 * y5) * (y * -a);
	} else if (y2 <= 5.4e+91) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 5.1e+237) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 = c * (x * (y0 * y2))
    if (y2 <= (-2.1d+151)) then
        tmp = t_1
    else if (y2 <= (-7000000.0d0)) then
        tmp = b * (y4 * (y * -k))
    else if (y2 <= 4.5d-124) then
        tmp = b * (y4 * (t * j))
    else if (y2 <= 2.2d-43) then
        tmp = (y3 * y5) * (y * -a)
    else if (y2 <= 5.4d+91) then
        tmp = i * (j * (x * y1))
    else if (y2 <= 5.1d+237) then
        tmp = t_1
    else
        tmp = a * (y5 * (t * y2))
    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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -2.1e+151) {
		tmp = t_1;
	} else if (y2 <= -7000000.0) {
		tmp = b * (y4 * (y * -k));
	} else if (y2 <= 4.5e-124) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 2.2e-43) {
		tmp = (y3 * y5) * (y * -a);
	} else if (y2 <= 5.4e+91) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 5.1e+237) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = c * (x * (y0 * y2))
	tmp = 0
	if y2 <= -2.1e+151:
		tmp = t_1
	elif y2 <= -7000000.0:
		tmp = b * (y4 * (y * -k))
	elif y2 <= 4.5e-124:
		tmp = b * (y4 * (t * j))
	elif y2 <= 2.2e-43:
		tmp = (y3 * y5) * (y * -a)
	elif y2 <= 5.4e+91:
		tmp = i * (j * (x * y1))
	elif y2 <= 5.1e+237:
		tmp = t_1
	else:
		tmp = a * (y5 * (t * y2))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(c * Float64(x * Float64(y0 * y2)))
	tmp = 0.0
	if (y2 <= -2.1e+151)
		tmp = t_1;
	elseif (y2 <= -7000000.0)
		tmp = Float64(b * Float64(y4 * Float64(y * Float64(-k))));
	elseif (y2 <= 4.5e-124)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	elseif (y2 <= 2.2e-43)
		tmp = Float64(Float64(y3 * y5) * Float64(y * Float64(-a)));
	elseif (y2 <= 5.4e+91)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	elseif (y2 <= 5.1e+237)
		tmp = t_1;
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 = c * (x * (y0 * y2));
	tmp = 0.0;
	if (y2 <= -2.1e+151)
		tmp = t_1;
	elseif (y2 <= -7000000.0)
		tmp = b * (y4 * (y * -k));
	elseif (y2 <= 4.5e-124)
		tmp = b * (y4 * (t * j));
	elseif (y2 <= 2.2e-43)
		tmp = (y3 * y5) * (y * -a);
	elseif (y2 <= 5.4e+91)
		tmp = i * (j * (x * y1));
	elseif (y2 <= 5.1e+237)
		tmp = t_1;
	else
		tmp = a * (y5 * (t * y2));
	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[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y2, -2.1e+151], t$95$1, If[LessEqual[y2, -7000000.0], N[(b * N[(y4 * N[(y * (-k)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 4.5e-124], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 2.2e-43], N[(N[(y3 * y5), $MachinePrecision] * N[(y * (-a)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 5.4e+91], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 5.1e+237], t$95$1, N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 6 regimes

2. if y2 < -2.1000000000000001e151 or 5.4e91 < y2 < 5.09999999999999979e237

   1. Initial program 15.5%

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

   3. Taylor expanded in y0 around inf 36.9%

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

   4. Taylor expanded in c around inf 34.0%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 43.2%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if -2.1000000000000001e151 < y2 < -7e6

   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 y4 around inf 47.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 36.5%

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

   5. Taylor expanded in j around 0 36.6%

      \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(-1 \cdot \left(k \cdot y\right)\right)}\right) \]
   6. Step-by-step derivation

      1. mul-1-neg 36.6%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(-k \cdot y\right)}\right) \]

      2. distribute-lft-neg-out 36.6%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(\left(-k\right) \cdot y\right)}\right) \]

      3. *-commutative 36.6%

         \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(y \cdot \left(-k\right)\right)}\right) \]

   7. Simplified 36.6%

      \[\leadsto b \cdot \left(y4 \cdot \color{blue}{\left(y \cdot \left(-k\right)\right)}\right) \]

   if -7e6 < y2 < 4.4999999999999996e-124

   1. Initial program 42.6%

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

   3. Taylor expanded in y4 around inf 44.3%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 34.0%

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

   5. Taylor expanded in j around inf 26.7%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 28.6%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 28.6%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 4.4999999999999996e-124 < y2 < 2.19999999999999997e-43

   1. Initial program 21.1%

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

   3. Taylor expanded in y4 around inf 29.9%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 38.2%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around 0 34.2%

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

      1. mul-1-neg 34.2%

         \[\leadsto \color{blue}{-a \cdot \left(y \cdot \left(y3 \cdot y5\right)\right)} \]

      2. associate-*r* 34.2%

         \[\leadsto -\color{blue}{\left(a \cdot y\right) \cdot \left(y3 \cdot y5\right)} \]

   7. Simplified 34.2%

      \[\leadsto \color{blue}{-\left(a \cdot y\right) \cdot \left(y3 \cdot y5\right)} \]

   if 2.19999999999999997e-43 < y2 < 5.4e91

   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 j around inf 46.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 38.3%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 38.3%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 38.3%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 35.7%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if 5.09999999999999979e237 < y2

   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 y4 around inf 40.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 67.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 67.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 6 regimes into one program.

4. Final simplification 36.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -2.1 \cdot 10^{+151}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;y2 \leq -7000000:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(y \cdot \left(-k\right)\right)\right)\\ \mathbf{elif}\;y2 \leq 4.5 \cdot 10^{-124}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 2.2 \cdot 10^{-43}:\\ \;\;\;\;\left(y3 \cdot y5\right) \cdot \left(y \cdot \left(-a\right)\right)\\ \mathbf{elif}\;y2 \leq 5.4 \cdot 10^{+91}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 5.1 \cdot 10^{+237}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 32.6% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y1 \leq -3 \cdot 10^{-46}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -1.76 \cdot 10^{-211}:\\ \;\;\;\;y2 \cdot \left(y0 \cdot \left(x \cdot c - k \cdot y5\right)\right)\\ \mathbf{elif}\;y1 \leq 9 \cdot 10^{-203}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 1.22 \cdot 10^{+51}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 1.32 \cdot 10^{+131}:\\ \;\;\;\;i \cdot \left(y \cdot \left(k \cdot y5 - x \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y1 -3e-46)
   (* j (* y1 (- (* x i) (* y3 y4))))
   (if (<= y1 -1.76e-211)
     (* y2 (* y0 (- (* x c) (* k y5))))
     (if (<= y1 9e-203)
       (* b (* y4 (- (* t j) (* y k))))
       (if (<= y1 1.22e+51)
         (* c (* t (- (* z i) (* y2 y4))))
         (if (<= y1 1.32e+131)
           (* i (* y (- (* k y5) (* x c))))
           (* y3 (* y1 (- (* z a) (* j y4))))))))))

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 (y1 <= -3e-46) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -1.76e-211) {
		tmp = y2 * (y0 * ((x * c) - (k * y5)));
	} else if (y1 <= 9e-203) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 1.22e+51) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else if (y1 <= 1.32e+131) {
		tmp = i * (y * ((k * y5) - (x * c)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	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 (y1 <= (-3d-46)) then
        tmp = j * (y1 * ((x * i) - (y3 * y4)))
    else if (y1 <= (-1.76d-211)) then
        tmp = y2 * (y0 * ((x * c) - (k * y5)))
    else if (y1 <= 9d-203) then
        tmp = b * (y4 * ((t * j) - (y * k)))
    else if (y1 <= 1.22d+51) then
        tmp = c * (t * ((z * i) - (y2 * y4)))
    else if (y1 <= 1.32d+131) then
        tmp = i * (y * ((k * y5) - (x * c)))
    else
        tmp = y3 * (y1 * ((z * a) - (j * y4)))
    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 (y1 <= -3e-46) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -1.76e-211) {
		tmp = y2 * (y0 * ((x * c) - (k * y5)));
	} else if (y1 <= 9e-203) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 1.22e+51) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else if (y1 <= 1.32e+131) {
		tmp = i * (y * ((k * y5) - (x * c)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y1 <= -3e-46:
		tmp = j * (y1 * ((x * i) - (y3 * y4)))
	elif y1 <= -1.76e-211:
		tmp = y2 * (y0 * ((x * c) - (k * y5)))
	elif y1 <= 9e-203:
		tmp = b * (y4 * ((t * j) - (y * k)))
	elif y1 <= 1.22e+51:
		tmp = c * (t * ((z * i) - (y2 * y4)))
	elif y1 <= 1.32e+131:
		tmp = i * (y * ((k * y5) - (x * c)))
	else:
		tmp = y3 * (y1 * ((z * a) - (j * y4)))
	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 (y1 <= -3e-46)
		tmp = Float64(j * Float64(y1 * Float64(Float64(x * i) - Float64(y3 * y4))));
	elseif (y1 <= -1.76e-211)
		tmp = Float64(y2 * Float64(y0 * Float64(Float64(x * c) - Float64(k * y5))));
	elseif (y1 <= 9e-203)
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	elseif (y1 <= 1.22e+51)
		tmp = Float64(c * Float64(t * Float64(Float64(z * i) - Float64(y2 * y4))));
	elseif (y1 <= 1.32e+131)
		tmp = Float64(i * Float64(y * Float64(Float64(k * y5) - Float64(x * c))));
	else
		tmp = Float64(y3 * Float64(y1 * Float64(Float64(z * a) - Float64(j * y4))));
	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 (y1 <= -3e-46)
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	elseif (y1 <= -1.76e-211)
		tmp = y2 * (y0 * ((x * c) - (k * y5)));
	elseif (y1 <= 9e-203)
		tmp = b * (y4 * ((t * j) - (y * k)));
	elseif (y1 <= 1.22e+51)
		tmp = c * (t * ((z * i) - (y2 * y4)));
	elseif (y1 <= 1.32e+131)
		tmp = i * (y * ((k * y5) - (x * c)));
	else
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	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[y1, -3e-46], N[(j * N[(y1 * N[(N[(x * i), $MachinePrecision] - N[(y3 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, -1.76e-211], N[(y2 * N[(y0 * N[(N[(x * c), $MachinePrecision] - N[(k * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 9e-203], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 1.22e+51], N[(c * N[(t * N[(N[(z * i), $MachinePrecision] - N[(y2 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 1.32e+131], N[(i * N[(y * N[(N[(k * y5), $MachinePrecision] - N[(x * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y3 * N[(y1 * N[(N[(z * a), $MachinePrecision] - N[(j * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 6 regimes

2. if y1 < -2.99999999999999987e-46

   1. Initial program 41.1%

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

   3. Taylor expanded in j around inf 41.4%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 45.4%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 45.4%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 45.4%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   if -2.99999999999999987e-46 < y1 < -1.76000000000000002e-211

   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 y2 around inf 40.5%

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

   4. Taylor expanded in y0 around inf 44.5%

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

   if -1.76000000000000002e-211 < y1 < 9.0000000000000003e-203

   1. Initial program 37.7%

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

   3. Taylor expanded in y4 around inf 48.3%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 47.6%

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

   if 9.0000000000000003e-203 < y1 < 1.22000000000000005e51

   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 t around inf 45.2%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in c around inf 42.7%

      \[\leadsto \color{blue}{c \cdot \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right)} \]

   if 1.22000000000000005e51 < y1 < 1.32e131

   1. Initial program 31.3%

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

   3. Taylor expanded in y around inf 31.8%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in i around inf 56.6%

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

   if 1.32e131 < y1

   1. Initial program 21.3%

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

   3. Taylor expanded in y3 around -inf 37.8%

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

   4. Taylor expanded in y1 around inf 58.7%

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

      1. +-commutative 58.7%

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

      2. mul-1-neg 58.7%

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

      3. unsub-neg 58.7%

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

      4. *-commutative 58.7%

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

   6. Simplified 58.7%

      \[\leadsto -1 \cdot \left(y3 \cdot \color{blue}{\left(y1 \cdot \left(y4 \cdot j - a \cdot z\right)\right)}\right) \]
3. Recombined 6 regimes into one program.

4. Final simplification 48.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -3 \cdot 10^{-46}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -1.76 \cdot 10^{-211}:\\ \;\;\;\;y2 \cdot \left(y0 \cdot \left(x \cdot c - k \cdot y5\right)\right)\\ \mathbf{elif}\;y1 \leq 9 \cdot 10^{-203}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 1.22 \cdot 10^{+51}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 1.32 \cdot 10^{+131}:\\ \;\;\;\;i \cdot \left(y \cdot \left(k \cdot y5 - x \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 16: 32.5% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y1 \leq -5 \cdot 10^{+25}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -2.3 \cdot 10^{-170}:\\ \;\;\;\;b \cdot \left(y \cdot \left(x \cdot a - k \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 8.5 \cdot 10^{-201}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 1.3 \cdot 10^{+51}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 3.7 \cdot 10^{+126}:\\ \;\;\;\;i \cdot \left(y \cdot \left(k \cdot y5 - x \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y1 -5e+25)
   (* j (* y1 (- (* x i) (* y3 y4))))
   (if (<= y1 -2.3e-170)
     (* b (* y (- (* x a) (* k y4))))
     (if (<= y1 8.5e-201)
       (* b (* y4 (- (* t j) (* y k))))
       (if (<= y1 1.3e+51)
         (* c (* t (- (* z i) (* y2 y4))))
         (if (<= y1 3.7e+126)
           (* i (* y (- (* k y5) (* x c))))
           (* y3 (* y1 (- (* z a) (* j y4))))))))))

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 (y1 <= -5e+25) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -2.3e-170) {
		tmp = b * (y * ((x * a) - (k * y4)));
	} else if (y1 <= 8.5e-201) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 1.3e+51) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else if (y1 <= 3.7e+126) {
		tmp = i * (y * ((k * y5) - (x * c)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	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 (y1 <= (-5d+25)) then
        tmp = j * (y1 * ((x * i) - (y3 * y4)))
    else if (y1 <= (-2.3d-170)) then
        tmp = b * (y * ((x * a) - (k * y4)))
    else if (y1 <= 8.5d-201) then
        tmp = b * (y4 * ((t * j) - (y * k)))
    else if (y1 <= 1.3d+51) then
        tmp = c * (t * ((z * i) - (y2 * y4)))
    else if (y1 <= 3.7d+126) then
        tmp = i * (y * ((k * y5) - (x * c)))
    else
        tmp = y3 * (y1 * ((z * a) - (j * y4)))
    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 (y1 <= -5e+25) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= -2.3e-170) {
		tmp = b * (y * ((x * a) - (k * y4)));
	} else if (y1 <= 8.5e-201) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 1.3e+51) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else if (y1 <= 3.7e+126) {
		tmp = i * (y * ((k * y5) - (x * c)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y1 <= -5e+25:
		tmp = j * (y1 * ((x * i) - (y3 * y4)))
	elif y1 <= -2.3e-170:
		tmp = b * (y * ((x * a) - (k * y4)))
	elif y1 <= 8.5e-201:
		tmp = b * (y4 * ((t * j) - (y * k)))
	elif y1 <= 1.3e+51:
		tmp = c * (t * ((z * i) - (y2 * y4)))
	elif y1 <= 3.7e+126:
		tmp = i * (y * ((k * y5) - (x * c)))
	else:
		tmp = y3 * (y1 * ((z * a) - (j * y4)))
	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 (y1 <= -5e+25)
		tmp = Float64(j * Float64(y1 * Float64(Float64(x * i) - Float64(y3 * y4))));
	elseif (y1 <= -2.3e-170)
		tmp = Float64(b * Float64(y * Float64(Float64(x * a) - Float64(k * y4))));
	elseif (y1 <= 8.5e-201)
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	elseif (y1 <= 1.3e+51)
		tmp = Float64(c * Float64(t * Float64(Float64(z * i) - Float64(y2 * y4))));
	elseif (y1 <= 3.7e+126)
		tmp = Float64(i * Float64(y * Float64(Float64(k * y5) - Float64(x * c))));
	else
		tmp = Float64(y3 * Float64(y1 * Float64(Float64(z * a) - Float64(j * y4))));
	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 (y1 <= -5e+25)
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	elseif (y1 <= -2.3e-170)
		tmp = b * (y * ((x * a) - (k * y4)));
	elseif (y1 <= 8.5e-201)
		tmp = b * (y4 * ((t * j) - (y * k)));
	elseif (y1 <= 1.3e+51)
		tmp = c * (t * ((z * i) - (y2 * y4)));
	elseif (y1 <= 3.7e+126)
		tmp = i * (y * ((k * y5) - (x * c)));
	else
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	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[y1, -5e+25], N[(j * N[(y1 * N[(N[(x * i), $MachinePrecision] - N[(y3 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, -2.3e-170], N[(b * N[(y * N[(N[(x * a), $MachinePrecision] - N[(k * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 8.5e-201], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 1.3e+51], N[(c * N[(t * N[(N[(z * i), $MachinePrecision] - N[(y2 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 3.7e+126], N[(i * N[(y * N[(N[(k * y5), $MachinePrecision] - N[(x * c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y3 * N[(y1 * N[(N[(z * a), $MachinePrecision] - N[(j * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 6 regimes

2. if y1 < -5.00000000000000024e25

   1. Initial program 38.2%

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

   3. Taylor expanded in j around inf 42.4%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 49.3%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 49.3%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 49.3%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   if -5.00000000000000024e25 < y1 < -2.29999999999999987e-170

   1. Initial program 38.4%

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

   3. Taylor expanded in y around inf 49.2%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 39.7%

      \[\leadsto \color{blue}{b \cdot \left(y \cdot \left(-1 \cdot \left(k \cdot y4\right) + a \cdot x\right)\right)} \]

   if -2.29999999999999987e-170 < y1 < 8.5000000000000007e-201

   1. Initial program 32.9%

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

   3. Taylor expanded in y4 around inf 42.2%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 47.0%

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

   if 8.5000000000000007e-201 < y1 < 1.3000000000000001e51

   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 t around inf 45.2%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in c around inf 42.7%

      \[\leadsto \color{blue}{c \cdot \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right)} \]

   if 1.3000000000000001e51 < y1 < 3.6999999999999998e126

   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 y around inf 33.9%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in i around inf 60.4%

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

   if 3.6999999999999998e126 < y1

   1. Initial program 20.8%

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

   3. Taylor expanded in y3 around -inf 36.9%

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

   4. Taylor expanded in y1 around inf 57.4%

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

      1. +-commutative 57.4%

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

      2. mul-1-neg 57.4%

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

      3. unsub-neg 57.4%

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

      4. *-commutative 57.4%

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

   6. Simplified 57.4%

      \[\leadsto -1 \cdot \left(y3 \cdot \color{blue}{\left(y1 \cdot \left(y4 \cdot j - a \cdot z\right)\right)}\right) \]
3. Recombined 6 regimes into one program.

4. Final simplification 48.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -5 \cdot 10^{+25}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -2.3 \cdot 10^{-170}:\\ \;\;\;\;b \cdot \left(y \cdot \left(x \cdot a - k \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 8.5 \cdot 10^{-201}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 1.3 \cdot 10^{+51}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 3.7 \cdot 10^{+126}:\\ \;\;\;\;i \cdot \left(y \cdot \left(k \cdot y5 - x \cdot c\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 17: 21.3% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.8 \cdot 10^{+190}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;x \leq -1.05 \cdot 10^{-63}:\\ \;\;\;\;b \cdot \left(j \cdot \left(t \cdot y4\right)\right)\\ \mathbf{elif}\;x \leq 1.75 \cdot 10^{-303}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(z \cdot \left(-y3\right)\right)\right)\\ \mathbf{elif}\;x \leq 2.1 \cdot 10^{-207}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot y4\right) \cdot \left(-y1\right)\right)\\ \mathbf{elif}\;x \leq 4.4 \cdot 10^{+101}:\\ \;\;\;\;k \cdot \left(b \cdot \left(y \cdot \left(-y4\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= x -7.8e+190)
   (* i (* y1 (* x j)))
   (if (<= x -1.05e-63)
     (* b (* j (* t y4)))
     (if (<= x 1.75e-303)
       (* y0 (* c (* z (- y3))))
       (if (<= x 2.1e-207)
         (* j (* (* y3 y4) (- y1)))
         (if (<= x 4.4e+101)
           (* k (* b (* y (- y4))))
           (* y0 (* c (* x y2)))))))))

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 <= -7.8e+190) {
		tmp = i * (y1 * (x * j));
	} else if (x <= -1.05e-63) {
		tmp = b * (j * (t * y4));
	} else if (x <= 1.75e-303) {
		tmp = y0 * (c * (z * -y3));
	} else if (x <= 2.1e-207) {
		tmp = j * ((y3 * y4) * -y1);
	} else if (x <= 4.4e+101) {
		tmp = k * (b * (y * -y4));
	} else {
		tmp = y0 * (c * (x * y2));
	}
	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 (x <= (-7.8d+190)) then
        tmp = i * (y1 * (x * j))
    else if (x <= (-1.05d-63)) then
        tmp = b * (j * (t * y4))
    else if (x <= 1.75d-303) then
        tmp = y0 * (c * (z * -y3))
    else if (x <= 2.1d-207) then
        tmp = j * ((y3 * y4) * -y1)
    else if (x <= 4.4d+101) then
        tmp = k * (b * (y * -y4))
    else
        tmp = y0 * (c * (x * y2))
    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 (x <= -7.8e+190) {
		tmp = i * (y1 * (x * j));
	} else if (x <= -1.05e-63) {
		tmp = b * (j * (t * y4));
	} else if (x <= 1.75e-303) {
		tmp = y0 * (c * (z * -y3));
	} else if (x <= 2.1e-207) {
		tmp = j * ((y3 * y4) * -y1);
	} else if (x <= 4.4e+101) {
		tmp = k * (b * (y * -y4));
	} else {
		tmp = y0 * (c * (x * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if x <= -7.8e+190:
		tmp = i * (y1 * (x * j))
	elif x <= -1.05e-63:
		tmp = b * (j * (t * y4))
	elif x <= 1.75e-303:
		tmp = y0 * (c * (z * -y3))
	elif x <= 2.1e-207:
		tmp = j * ((y3 * y4) * -y1)
	elif x <= 4.4e+101:
		tmp = k * (b * (y * -y4))
	else:
		tmp = y0 * (c * (x * y2))
	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 <= -7.8e+190)
		tmp = Float64(i * Float64(y1 * Float64(x * j)));
	elseif (x <= -1.05e-63)
		tmp = Float64(b * Float64(j * Float64(t * y4)));
	elseif (x <= 1.75e-303)
		tmp = Float64(y0 * Float64(c * Float64(z * Float64(-y3))));
	elseif (x <= 2.1e-207)
		tmp = Float64(j * Float64(Float64(y3 * y4) * Float64(-y1)));
	elseif (x <= 4.4e+101)
		tmp = Float64(k * Float64(b * Float64(y * Float64(-y4))));
	else
		tmp = Float64(y0 * Float64(c * Float64(x * y2)));
	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 (x <= -7.8e+190)
		tmp = i * (y1 * (x * j));
	elseif (x <= -1.05e-63)
		tmp = b * (j * (t * y4));
	elseif (x <= 1.75e-303)
		tmp = y0 * (c * (z * -y3));
	elseif (x <= 2.1e-207)
		tmp = j * ((y3 * y4) * -y1);
	elseif (x <= 4.4e+101)
		tmp = k * (b * (y * -y4));
	else
		tmp = y0 * (c * (x * y2));
	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[x, -7.8e+190], N[(i * N[(y1 * N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -1.05e-63], N[(b * N[(j * N[(t * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.75e-303], N[(y0 * N[(c * N[(z * (-y3)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.1e-207], N[(j * N[(N[(y3 * y4), $MachinePrecision] * (-y1)), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.4e+101], N[(k * N[(b * N[(y * (-y4)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y0 * N[(c * N[(x * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 6 regimes

2. if x < -7.8000000000000007e190

   1. Initial program 15.9%

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

   3. Taylor expanded in j around inf 38.0%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 32.4%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 32.4%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 32.4%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 44.4%

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

      1. associate-*r* 50.4%

         \[\leadsto i \cdot \color{blue}{\left(\left(j \cdot x\right) \cdot y1\right)} \]

   9. Simplified 50.4%

      \[\leadsto \color{blue}{i \cdot \left(\left(j \cdot x\right) \cdot y1\right)} \]

   if -7.8000000000000007e190 < x < -1.05e-63

   1. Initial program 37.3%

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

   3. Taylor expanded in y4 around inf 48.8%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 33.0%

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

   5. Taylor expanded in j around inf 29.4%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]

   if -1.05e-63 < x < 1.75e-303

   1. Initial program 38.6%

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

   3. Taylor expanded in y0 around inf 39.6%

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

   4. Taylor expanded in c around inf 31.6%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around 0 27.9%

      \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(-1 \cdot \left(y3 \cdot z\right)\right)}\right) \]
   6. Step-by-step derivation

      1. mul-1-neg 27.9%

         \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(-y3 \cdot z\right)}\right) \]

      2. distribute-lft-neg-out 27.9%

         \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(\left(-y3\right) \cdot z\right)}\right) \]

      3. *-commutative 27.9%

         \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(z \cdot \left(-y3\right)\right)}\right) \]

   7. Simplified 27.9%

      \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(z \cdot \left(-y3\right)\right)}\right) \]

   if 1.75e-303 < x < 2.10000000000000003e-207

   1. Initial program 43.6%

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

   3. Taylor expanded in j around inf 39.0%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 38.9%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 38.9%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 38.9%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around inf 38.9%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)\right)} \]
   8. Step-by-step derivation

      1. associate-*r* 38.9%

         \[\leadsto j \cdot \color{blue}{\left(\left(-1 \cdot y1\right) \cdot \left(y3 \cdot y4\right)\right)} \]

      2. neg-mul-1 38.9%

         \[\leadsto j \cdot \left(\color{blue}{\left(-y1\right)} \cdot \left(y3 \cdot y4\right)\right) \]

   9. Simplified 38.9%

      \[\leadsto j \cdot \color{blue}{\left(\left(-y1\right) \cdot \left(y3 \cdot y4\right)\right)} \]

   if 2.10000000000000003e-207 < x < 4.4000000000000001e101

   1. Initial program 31.7%

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

   3. Taylor expanded in y4 around inf 54.2%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in k around inf 31.0%

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

   5. Taylor expanded in b around inf 31.0%

      \[\leadsto k \cdot \color{blue}{\left(-1 \cdot \left(b \cdot \left(y \cdot y4\right)\right)\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 31.0%

         \[\leadsto k \cdot \color{blue}{\left(-b \cdot \left(y \cdot y4\right)\right)} \]

      2. *-commutative 31.0%

         \[\leadsto k \cdot \left(-b \cdot \color{blue}{\left(y4 \cdot y\right)}\right) \]

      3. distribute-rgt-neg-in 31.0%

         \[\leadsto k \cdot \color{blue}{\left(b \cdot \left(-y4 \cdot y\right)\right)} \]

      4. distribute-rgt-neg-in 31.0%

         \[\leadsto k \cdot \left(b \cdot \color{blue}{\left(y4 \cdot \left(-y\right)\right)}\right) \]

   7. Simplified 31.0%

      \[\leadsto k \cdot \color{blue}{\left(b \cdot \left(y4 \cdot \left(-y\right)\right)\right)} \]

   if 4.4000000000000001e101 < x

   1. Initial program 27.9%

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

   3. Taylor expanded in y0 around inf 39.8%

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

   4. Taylor expanded in c around inf 41.2%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 43.2%

      \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(x \cdot y2\right)}\right) \]
   6. Step-by-step derivation

      1. *-commutative 43.2%

         \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(y2 \cdot x\right)}\right) \]

   7. Simplified 43.2%

      \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(y2 \cdot x\right)}\right) \]
3. Recombined 6 regimes into one program.

4. Final simplification 34.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.8 \cdot 10^{+190}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;x \leq -1.05 \cdot 10^{-63}:\\ \;\;\;\;b \cdot \left(j \cdot \left(t \cdot y4\right)\right)\\ \mathbf{elif}\;x \leq 1.75 \cdot 10^{-303}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(z \cdot \left(-y3\right)\right)\right)\\ \mathbf{elif}\;x \leq 2.1 \cdot 10^{-207}:\\ \;\;\;\;j \cdot \left(\left(y3 \cdot y4\right) \cdot \left(-y1\right)\right)\\ \mathbf{elif}\;x \leq 4.4 \cdot 10^{+101}:\\ \;\;\;\;k \cdot \left(b \cdot \left(y \cdot \left(-y4\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 18: 21.2% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;j \leq -5.7 \cdot 10^{-11}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;j \leq -2.1 \cdot 10^{-64}:\\ \;\;\;\;y2 \cdot \left(a \cdot \left(t \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq -6.2 \cdot 10^{-152}:\\ \;\;\;\;a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq 8 \cdot 10^{-207}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;j \leq 6.4 \cdot 10^{+105}:\\ \;\;\;\;j \cdot \left(i \cdot \left(x \cdot y1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= j -5.7e-11)
   (* i (* y1 (* x j)))
   (if (<= j -2.1e-64)
     (* y2 (* a (* t y5)))
     (if (<= j -6.2e-152)
       (* a (- (* y (* y3 y5))))
       (if (<= j 8e-207)
         (* c (* x (* y0 y2)))
         (if (<= j 6.4e+105)
           (* j (* i (* x y1)))
           (* (* y3 y4) (* 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 (j <= -5.7e-11) {
		tmp = i * (y1 * (x * j));
	} else if (j <= -2.1e-64) {
		tmp = y2 * (a * (t * y5));
	} else if (j <= -6.2e-152) {
		tmp = a * -(y * (y3 * y5));
	} else if (j <= 8e-207) {
		tmp = c * (x * (y0 * y2));
	} else if (j <= 6.4e+105) {
		tmp = j * (i * (x * y1));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	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 (j <= (-5.7d-11)) then
        tmp = i * (y1 * (x * j))
    else if (j <= (-2.1d-64)) then
        tmp = y2 * (a * (t * y5))
    else if (j <= (-6.2d-152)) then
        tmp = a * -(y * (y3 * y5))
    else if (j <= 8d-207) then
        tmp = c * (x * (y0 * y2))
    else if (j <= 6.4d+105) then
        tmp = j * (i * (x * y1))
    else
        tmp = (y3 * y4) * (j * -y1)
    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 (j <= -5.7e-11) {
		tmp = i * (y1 * (x * j));
	} else if (j <= -2.1e-64) {
		tmp = y2 * (a * (t * y5));
	} else if (j <= -6.2e-152) {
		tmp = a * -(y * (y3 * y5));
	} else if (j <= 8e-207) {
		tmp = c * (x * (y0 * y2));
	} else if (j <= 6.4e+105) {
		tmp = j * (i * (x * y1));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if j <= -5.7e-11:
		tmp = i * (y1 * (x * j))
	elif j <= -2.1e-64:
		tmp = y2 * (a * (t * y5))
	elif j <= -6.2e-152:
		tmp = a * -(y * (y3 * y5))
	elif j <= 8e-207:
		tmp = c * (x * (y0 * y2))
	elif j <= 6.4e+105:
		tmp = j * (i * (x * y1))
	else:
		tmp = (y3 * y4) * (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 (j <= -5.7e-11)
		tmp = Float64(i * Float64(y1 * Float64(x * j)));
	elseif (j <= -2.1e-64)
		tmp = Float64(y2 * Float64(a * Float64(t * y5)));
	elseif (j <= -6.2e-152)
		tmp = Float64(a * Float64(-Float64(y * Float64(y3 * y5))));
	elseif (j <= 8e-207)
		tmp = Float64(c * Float64(x * Float64(y0 * y2)));
	elseif (j <= 6.4e+105)
		tmp = Float64(j * Float64(i * Float64(x * y1)));
	else
		tmp = Float64(Float64(y3 * y4) * Float64(j * Float64(-y1)));
	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 (j <= -5.7e-11)
		tmp = i * (y1 * (x * j));
	elseif (j <= -2.1e-64)
		tmp = y2 * (a * (t * y5));
	elseif (j <= -6.2e-152)
		tmp = a * -(y * (y3 * y5));
	elseif (j <= 8e-207)
		tmp = c * (x * (y0 * y2));
	elseif (j <= 6.4e+105)
		tmp = j * (i * (x * y1));
	else
		tmp = (y3 * y4) * (j * -y1);
	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[j, -5.7e-11], N[(i * N[(y1 * N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, -2.1e-64], N[(y2 * N[(a * N[(t * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, -6.2e-152], N[(a * (-N[(y * N[(y3 * y5), $MachinePrecision]), $MachinePrecision])), $MachinePrecision], If[LessEqual[j, 8e-207], N[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 6.4e+105], N[(j * N[(i * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y3 * y4), $MachinePrecision] * N[(j * (-y1)), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 6 regimes

2. if j < -5.6999999999999997e-11

   1. Initial program 36.5%

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

   3. Taylor expanded in j around inf 54.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 34.8%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 34.8%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 34.8%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 27.0%

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

      1. associate-*r* 33.1%

         \[\leadsto i \cdot \color{blue}{\left(\left(j \cdot x\right) \cdot y1\right)} \]

   9. Simplified 33.1%

      \[\leadsto \color{blue}{i \cdot \left(\left(j \cdot x\right) \cdot y1\right)} \]

   if -5.6999999999999997e-11 < j < -2.10000000000000011e-64

   1. Initial program 42.0%

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

   3. Taylor expanded in y2 around inf 34.8%

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

   4. Taylor expanded in y5 around -inf 26.7%

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

      1. mul-1-neg 26.7%

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

   6. Simplified 26.7%

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

   7. Taylor expanded in k around 0 42.8%

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

      1. *-commutative 42.8%

         \[\leadsto y2 \cdot \left(a \cdot \color{blue}{\left(y5 \cdot t\right)}\right) \]

   9. Simplified 42.8%

      \[\leadsto y2 \cdot \color{blue}{\left(a \cdot \left(y5 \cdot t\right)\right)} \]

   if -2.10000000000000011e-64 < j < -6.1999999999999997e-152

   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 y4 around inf 35.6%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 53.7%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around 0 53.7%

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

      1. mul-1-neg 53.7%

         \[\leadsto \color{blue}{-a \cdot \left(y \cdot \left(y3 \cdot y5\right)\right)} \]

      2. distribute-rgt-neg-in 53.7%

         \[\leadsto \color{blue}{a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)} \]

      3. *-commutative 53.7%

         \[\leadsto a \cdot \left(-\color{blue}{\left(y3 \cdot y5\right) \cdot y}\right) \]

      4. distribute-rgt-neg-in 53.7%

         \[\leadsto a \cdot \color{blue}{\left(\left(y3 \cdot y5\right) \cdot \left(-y\right)\right)} \]

   7. Simplified 53.7%

      \[\leadsto \color{blue}{a \cdot \left(\left(y3 \cdot y5\right) \cdot \left(-y\right)\right)} \]

   if -6.1999999999999997e-152 < j < 7.9999999999999994e-207

   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 42.9%

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

   4. Taylor expanded in c around inf 33.7%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 29.5%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if 7.9999999999999994e-207 < j < 6.4e105

   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 j around inf 35.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 27.4%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 27.4%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 27.4%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 25.5%

      \[\leadsto j \cdot \color{blue}{\left(i \cdot \left(x \cdot y1\right)\right)} \]

   if 6.4e105 < j

   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 j around inf 62.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 44.7%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 44.7%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 44.7%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around inf 36.8%

      \[\leadsto \color{blue}{-1 \cdot \left(j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)\right)} \]
   8. Step-by-step derivation

      1. mul-1-neg 36.8%

         \[\leadsto \color{blue}{-j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)} \]

      2. associate-*r* 40.7%

         \[\leadsto -\color{blue}{\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]

   9. Simplified 40.7%

      \[\leadsto \color{blue}{-\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]
3. Recombined 6 regimes into one program.

4. Final simplification 34.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;j \leq -5.7 \cdot 10^{-11}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;j \leq -2.1 \cdot 10^{-64}:\\ \;\;\;\;y2 \cdot \left(a \cdot \left(t \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq -6.2 \cdot 10^{-152}:\\ \;\;\;\;a \cdot \left(-y \cdot \left(y3 \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq 8 \cdot 10^{-207}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;j \leq 6.4 \cdot 10^{+105}:\\ \;\;\;\;j \cdot \left(i \cdot \left(x \cdot y1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 19: 21.9% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{if}\;y2 \leq -7.3 \cdot 10^{+48}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq 7.2 \cdot 10^{-125}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 2.15 \cdot 10^{-43}:\\ \;\;\;\;\left(y3 \cdot y5\right) \cdot \left(y \cdot \left(-a\right)\right)\\ \mathbf{elif}\;y2 \leq 7.2 \cdot 10^{+90}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 5 \cdot 10^{+236}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* c (* x (* y0 y2)))))
   (if (<= y2 -7.3e+48)
     t_1
     (if (<= y2 7.2e-125)
       (* b (* y4 (* t j)))
       (if (<= y2 2.15e-43)
         (* (* y3 y5) (* y (- a)))
         (if (<= y2 7.2e+90)
           (* i (* j (* x y1)))
           (if (<= y2 5e+236) t_1 (* a (* y5 (* t y2))))))))))

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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -7.3e+48) {
		tmp = t_1;
	} else if (y2 <= 7.2e-125) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 2.15e-43) {
		tmp = (y3 * y5) * (y * -a);
	} else if (y2 <= 7.2e+90) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 5e+236) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 = c * (x * (y0 * y2))
    if (y2 <= (-7.3d+48)) then
        tmp = t_1
    else if (y2 <= 7.2d-125) then
        tmp = b * (y4 * (t * j))
    else if (y2 <= 2.15d-43) then
        tmp = (y3 * y5) * (y * -a)
    else if (y2 <= 7.2d+90) then
        tmp = i * (j * (x * y1))
    else if (y2 <= 5d+236) then
        tmp = t_1
    else
        tmp = a * (y5 * (t * y2))
    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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -7.3e+48) {
		tmp = t_1;
	} else if (y2 <= 7.2e-125) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 2.15e-43) {
		tmp = (y3 * y5) * (y * -a);
	} else if (y2 <= 7.2e+90) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 5e+236) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = c * (x * (y0 * y2))
	tmp = 0
	if y2 <= -7.3e+48:
		tmp = t_1
	elif y2 <= 7.2e-125:
		tmp = b * (y4 * (t * j))
	elif y2 <= 2.15e-43:
		tmp = (y3 * y5) * (y * -a)
	elif y2 <= 7.2e+90:
		tmp = i * (j * (x * y1))
	elif y2 <= 5e+236:
		tmp = t_1
	else:
		tmp = a * (y5 * (t * y2))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(c * Float64(x * Float64(y0 * y2)))
	tmp = 0.0
	if (y2 <= -7.3e+48)
		tmp = t_1;
	elseif (y2 <= 7.2e-125)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	elseif (y2 <= 2.15e-43)
		tmp = Float64(Float64(y3 * y5) * Float64(y * Float64(-a)));
	elseif (y2 <= 7.2e+90)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	elseif (y2 <= 5e+236)
		tmp = t_1;
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 = c * (x * (y0 * y2));
	tmp = 0.0;
	if (y2 <= -7.3e+48)
		tmp = t_1;
	elseif (y2 <= 7.2e-125)
		tmp = b * (y4 * (t * j));
	elseif (y2 <= 2.15e-43)
		tmp = (y3 * y5) * (y * -a);
	elseif (y2 <= 7.2e+90)
		tmp = i * (j * (x * y1));
	elseif (y2 <= 5e+236)
		tmp = t_1;
	else
		tmp = a * (y5 * (t * y2));
	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[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y2, -7.3e+48], t$95$1, If[LessEqual[y2, 7.2e-125], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 2.15e-43], N[(N[(y3 * y5), $MachinePrecision] * N[(y * (-a)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 7.2e+90], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 5e+236], t$95$1, N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

1. Split input into 5 regimes

2. if y2 < -7.3000000000000004e48 or 7.2e90 < y2 < 4.9999999999999997e236

   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 y0 around inf 37.0%

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

   4. Taylor expanded in c around inf 33.6%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 36.1%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if -7.3000000000000004e48 < y2 < 7.2000000000000004e-125

   1. Initial program 41.4%

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

   3. Taylor expanded in y4 around inf 43.8%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 33.2%

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

   5. Taylor expanded in j around inf 25.1%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 26.8%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 26.8%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 7.2000000000000004e-125 < y2 < 2.14999999999999982e-43

   1. Initial program 21.1%

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

   3. Taylor expanded in y4 around inf 29.9%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 38.2%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around 0 34.2%

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

      1. mul-1-neg 34.2%

         \[\leadsto \color{blue}{-a \cdot \left(y \cdot \left(y3 \cdot y5\right)\right)} \]

      2. associate-*r* 34.2%

         \[\leadsto -\color{blue}{\left(a \cdot y\right) \cdot \left(y3 \cdot y5\right)} \]

   7. Simplified 34.2%

      \[\leadsto \color{blue}{-\left(a \cdot y\right) \cdot \left(y3 \cdot y5\right)} \]

   if 2.14999999999999982e-43 < y2 < 7.2e90

   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 j around inf 46.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 38.3%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 38.3%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 38.3%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 35.7%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if 4.9999999999999997e236 < y2

   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 y4 around inf 40.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 67.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 67.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 5 regimes into one program.

4. Final simplification 33.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -7.3 \cdot 10^{+48}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;y2 \leq 7.2 \cdot 10^{-125}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 2.15 \cdot 10^{-43}:\\ \;\;\;\;\left(y3 \cdot y5\right) \cdot \left(y \cdot \left(-a\right)\right)\\ \mathbf{elif}\;y2 \leq 7.2 \cdot 10^{+90}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 5 \cdot 10^{+236}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 20: 31.7% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{if}\;y1 \leq -2.9 \cdot 10^{+27}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y1 \leq -1.6 \cdot 10^{-166}:\\ \;\;\;\;b \cdot \left(y \cdot \left(x \cdot a - k \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 1.65 \cdot 10^{-201}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 1.1 \cdot 10^{+201}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* j (* y1 (- (* x i) (* y3 y4))))))
   (if (<= y1 -2.9e+27)
     t_1
     (if (<= y1 -1.6e-166)
       (* b (* y (- (* x a) (* k y4))))
       (if (<= y1 1.65e-201)
         (* b (* y4 (- (* t j) (* y k))))
         (if (<= y1 1.1e+201) (* c (* t (- (* z i) (* y2 y4)))) 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 * (y1 * ((x * i) - (y3 * y4)));
	double tmp;
	if (y1 <= -2.9e+27) {
		tmp = t_1;
	} else if (y1 <= -1.6e-166) {
		tmp = b * (y * ((x * a) - (k * y4)));
	} else if (y1 <= 1.65e-201) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 1.1e+201) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} 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 = j * (y1 * ((x * i) - (y3 * y4)))
    if (y1 <= (-2.9d+27)) then
        tmp = t_1
    else if (y1 <= (-1.6d-166)) then
        tmp = b * (y * ((x * a) - (k * y4)))
    else if (y1 <= 1.65d-201) then
        tmp = b * (y4 * ((t * j) - (y * k)))
    else if (y1 <= 1.1d+201) then
        tmp = c * (t * ((z * i) - (y2 * y4)))
    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 = j * (y1 * ((x * i) - (y3 * y4)));
	double tmp;
	if (y1 <= -2.9e+27) {
		tmp = t_1;
	} else if (y1 <= -1.6e-166) {
		tmp = b * (y * ((x * a) - (k * y4)));
	} else if (y1 <= 1.65e-201) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 1.1e+201) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} 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 = j * (y1 * ((x * i) - (y3 * y4)))
	tmp = 0
	if y1 <= -2.9e+27:
		tmp = t_1
	elif y1 <= -1.6e-166:
		tmp = b * (y * ((x * a) - (k * y4)))
	elif y1 <= 1.65e-201:
		tmp = b * (y4 * ((t * j) - (y * k)))
	elif y1 <= 1.1e+201:
		tmp = c * (t * ((z * i) - (y2 * y4)))
	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(j * Float64(y1 * Float64(Float64(x * i) - Float64(y3 * y4))))
	tmp = 0.0
	if (y1 <= -2.9e+27)
		tmp = t_1;
	elseif (y1 <= -1.6e-166)
		tmp = Float64(b * Float64(y * Float64(Float64(x * a) - Float64(k * y4))));
	elseif (y1 <= 1.65e-201)
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	elseif (y1 <= 1.1e+201)
		tmp = Float64(c * Float64(t * Float64(Float64(z * i) - Float64(y2 * y4))));
	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 = j * (y1 * ((x * i) - (y3 * y4)));
	tmp = 0.0;
	if (y1 <= -2.9e+27)
		tmp = t_1;
	elseif (y1 <= -1.6e-166)
		tmp = b * (y * ((x * a) - (k * y4)));
	elseif (y1 <= 1.65e-201)
		tmp = b * (y4 * ((t * j) - (y * k)));
	elseif (y1 <= 1.1e+201)
		tmp = c * (t * ((z * i) - (y2 * y4)));
	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[(j * N[(y1 * N[(N[(x * i), $MachinePrecision] - N[(y3 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y1, -2.9e+27], t$95$1, If[LessEqual[y1, -1.6e-166], N[(b * N[(y * N[(N[(x * a), $MachinePrecision] - N[(k * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 1.65e-201], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 1.1e+201], N[(c * N[(t * N[(N[(z * i), $MachinePrecision] - N[(y2 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if y1 < -2.9000000000000001e27 or 1.1e201 < y1

   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 j around inf 49.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 57.0%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 57.0%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 57.0%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   if -2.9000000000000001e27 < y1 < -1.6e-166

   1. Initial program 38.4%

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

   3. Taylor expanded in y around inf 49.2%

      \[\leadsto \color{blue}{y \cdot \left(\left(-1 \cdot \left(k \cdot \left(b \cdot y4 - i \cdot y5\right)\right) + x \cdot \left(a \cdot b - c \cdot i\right)\right) - -1 \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 39.7%

      \[\leadsto \color{blue}{b \cdot \left(y \cdot \left(-1 \cdot \left(k \cdot y4\right) + a \cdot x\right)\right)} \]

   if -1.6e-166 < y1 < 1.6500000000000002e-201

   1. Initial program 32.9%

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

   3. Taylor expanded in y4 around inf 42.2%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 47.0%

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

   if 1.6500000000000002e-201 < y1 < 1.1e201

   1. Initial program 28.7%

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

   3. Taylor expanded in t around inf 42.6%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in c around inf 39.1%

      \[\leadsto \color{blue}{c \cdot \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 46.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -2.9 \cdot 10^{+27}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq -1.6 \cdot 10^{-166}:\\ \;\;\;\;b \cdot \left(y \cdot \left(x \cdot a - k \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 1.65 \cdot 10^{-201}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 1.1 \cdot 10^{+201}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 21: 21.3% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;j \leq -2.7 \cdot 10^{-12}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;j \leq -4.4 \cdot 10^{-132}:\\ \;\;\;\;y2 \cdot \left(a \cdot \left(t \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq 5 \cdot 10^{-207}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;j \leq 6.5 \cdot 10^{+105}:\\ \;\;\;\;j \cdot \left(i \cdot \left(x \cdot y1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= j -2.7e-12)
   (* i (* y1 (* x j)))
   (if (<= j -4.4e-132)
     (* y2 (* a (* t y5)))
     (if (<= j 5e-207)
       (* c (* x (* y0 y2)))
       (if (<= j 6.5e+105) (* j (* i (* x y1))) (* (* y3 y4) (* 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 (j <= -2.7e-12) {
		tmp = i * (y1 * (x * j));
	} else if (j <= -4.4e-132) {
		tmp = y2 * (a * (t * y5));
	} else if (j <= 5e-207) {
		tmp = c * (x * (y0 * y2));
	} else if (j <= 6.5e+105) {
		tmp = j * (i * (x * y1));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	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 (j <= (-2.7d-12)) then
        tmp = i * (y1 * (x * j))
    else if (j <= (-4.4d-132)) then
        tmp = y2 * (a * (t * y5))
    else if (j <= 5d-207) then
        tmp = c * (x * (y0 * y2))
    else if (j <= 6.5d+105) then
        tmp = j * (i * (x * y1))
    else
        tmp = (y3 * y4) * (j * -y1)
    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 (j <= -2.7e-12) {
		tmp = i * (y1 * (x * j));
	} else if (j <= -4.4e-132) {
		tmp = y2 * (a * (t * y5));
	} else if (j <= 5e-207) {
		tmp = c * (x * (y0 * y2));
	} else if (j <= 6.5e+105) {
		tmp = j * (i * (x * y1));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if j <= -2.7e-12:
		tmp = i * (y1 * (x * j))
	elif j <= -4.4e-132:
		tmp = y2 * (a * (t * y5))
	elif j <= 5e-207:
		tmp = c * (x * (y0 * y2))
	elif j <= 6.5e+105:
		tmp = j * (i * (x * y1))
	else:
		tmp = (y3 * y4) * (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 (j <= -2.7e-12)
		tmp = Float64(i * Float64(y1 * Float64(x * j)));
	elseif (j <= -4.4e-132)
		tmp = Float64(y2 * Float64(a * Float64(t * y5)));
	elseif (j <= 5e-207)
		tmp = Float64(c * Float64(x * Float64(y0 * y2)));
	elseif (j <= 6.5e+105)
		tmp = Float64(j * Float64(i * Float64(x * y1)));
	else
		tmp = Float64(Float64(y3 * y4) * Float64(j * Float64(-y1)));
	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 (j <= -2.7e-12)
		tmp = i * (y1 * (x * j));
	elseif (j <= -4.4e-132)
		tmp = y2 * (a * (t * y5));
	elseif (j <= 5e-207)
		tmp = c * (x * (y0 * y2));
	elseif (j <= 6.5e+105)
		tmp = j * (i * (x * y1));
	else
		tmp = (y3 * y4) * (j * -y1);
	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[j, -2.7e-12], N[(i * N[(y1 * N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, -4.4e-132], N[(y2 * N[(a * N[(t * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 5e-207], N[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[j, 6.5e+105], N[(j * N[(i * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y3 * y4), $MachinePrecision] * N[(j * (-y1)), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if j < -2.6999999999999998e-12

   1. Initial program 36.5%

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

   3. Taylor expanded in j around inf 54.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 34.8%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 34.8%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 34.8%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 27.0%

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

      1. associate-*r* 33.1%

         \[\leadsto i \cdot \color{blue}{\left(\left(j \cdot x\right) \cdot y1\right)} \]

   9. Simplified 33.1%

      \[\leadsto \color{blue}{i \cdot \left(\left(j \cdot x\right) \cdot y1\right)} \]

   if -2.6999999999999998e-12 < j < -4.39999999999999981e-132

   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 40.5%

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

   4. Taylor expanded in y5 around -inf 30.2%

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

      1. mul-1-neg 30.2%

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

   6. Simplified 30.2%

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

   7. Taylor expanded in k around 0 38.1%

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

      1. *-commutative 38.1%

         \[\leadsto y2 \cdot \left(a \cdot \color{blue}{\left(y5 \cdot t\right)}\right) \]

   9. Simplified 38.1%

      \[\leadsto y2 \cdot \color{blue}{\left(a \cdot \left(y5 \cdot t\right)\right)} \]

   if -4.39999999999999981e-132 < j < 5.00000000000000014e-207

   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 42.4%

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

   4. Taylor expanded in c around inf 35.7%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 28.1%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if 5.00000000000000014e-207 < j < 6.50000000000000049e105

   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 j around inf 35.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 27.4%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 27.4%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 27.4%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 25.5%

      \[\leadsto j \cdot \color{blue}{\left(i \cdot \left(x \cdot y1\right)\right)} \]

   if 6.50000000000000049e105 < j

   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 j around inf 62.6%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 44.7%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 44.7%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 44.7%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around inf 36.8%

      \[\leadsto \color{blue}{-1 \cdot \left(j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)\right)} \]
   8. Step-by-step derivation

      1. mul-1-neg 36.8%

         \[\leadsto \color{blue}{-j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)} \]

      2. associate-*r* 40.7%

         \[\leadsto -\color{blue}{\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]

   9. Simplified 40.7%

      \[\leadsto \color{blue}{-\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]
3. Recombined 5 regimes into one program.

4. Final simplification 32.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;j \leq -2.7 \cdot 10^{-12}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;j \leq -4.4 \cdot 10^{-132}:\\ \;\;\;\;y2 \cdot \left(a \cdot \left(t \cdot y5\right)\right)\\ \mathbf{elif}\;j \leq 5 \cdot 10^{-207}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;j \leq 6.5 \cdot 10^{+105}:\\ \;\;\;\;j \cdot \left(i \cdot \left(x \cdot y1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 22: 21.8% accurate, 3.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{if}\;y2 \leq -6.8 \cdot 10^{+52}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq 7.2 \cdot 10^{-119}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 3.8 \cdot 10^{+88}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 9 \cdot 10^{+237}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* c (* x (* y0 y2)))))
   (if (<= y2 -6.8e+52)
     t_1
     (if (<= y2 7.2e-119)
       (* b (* y4 (* t j)))
       (if (<= y2 3.8e+88)
         (* i (* j (* x y1)))
         (if (<= y2 9e+237) t_1 (* a (* y5 (* t y2)))))))))

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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -6.8e+52) {
		tmp = t_1;
	} else if (y2 <= 7.2e-119) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 3.8e+88) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 9e+237) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 = c * (x * (y0 * y2))
    if (y2 <= (-6.8d+52)) then
        tmp = t_1
    else if (y2 <= 7.2d-119) then
        tmp = b * (y4 * (t * j))
    else if (y2 <= 3.8d+88) then
        tmp = i * (j * (x * y1))
    else if (y2 <= 9d+237) then
        tmp = t_1
    else
        tmp = a * (y5 * (t * y2))
    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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -6.8e+52) {
		tmp = t_1;
	} else if (y2 <= 7.2e-119) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 3.8e+88) {
		tmp = i * (j * (x * y1));
	} else if (y2 <= 9e+237) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = c * (x * (y0 * y2))
	tmp = 0
	if y2 <= -6.8e+52:
		tmp = t_1
	elif y2 <= 7.2e-119:
		tmp = b * (y4 * (t * j))
	elif y2 <= 3.8e+88:
		tmp = i * (j * (x * y1))
	elif y2 <= 9e+237:
		tmp = t_1
	else:
		tmp = a * (y5 * (t * y2))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(c * Float64(x * Float64(y0 * y2)))
	tmp = 0.0
	if (y2 <= -6.8e+52)
		tmp = t_1;
	elseif (y2 <= 7.2e-119)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	elseif (y2 <= 3.8e+88)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	elseif (y2 <= 9e+237)
		tmp = t_1;
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 = c * (x * (y0 * y2));
	tmp = 0.0;
	if (y2 <= -6.8e+52)
		tmp = t_1;
	elseif (y2 <= 7.2e-119)
		tmp = b * (y4 * (t * j));
	elseif (y2 <= 3.8e+88)
		tmp = i * (j * (x * y1));
	elseif (y2 <= 9e+237)
		tmp = t_1;
	else
		tmp = a * (y5 * (t * y2));
	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[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y2, -6.8e+52], t$95$1, If[LessEqual[y2, 7.2e-119], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 3.8e+88], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 9e+237], t$95$1, N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if y2 < -6.8e52 or 3.7999999999999997e88 < y2 < 8.99999999999999928e237

   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 y0 around inf 37.0%

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

   4. Taylor expanded in c around inf 33.6%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 36.1%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if -6.8e52 < y2 < 7.2e-119

   1. Initial program 40.7%

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

   3. Taylor expanded in y4 around inf 43.9%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 32.7%

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

   5. Taylor expanded in j around inf 24.7%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 26.4%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 26.4%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 7.2e-119 < y2 < 3.7999999999999997e88

   1. Initial program 31.7%

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

   3. Taylor expanded in j around inf 44.7%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 36.2%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 36.2%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 36.2%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 27.7%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if 8.99999999999999928e237 < y2

   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 y4 around inf 40.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 67.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 67.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 4 regimes into one program.

4. Final simplification 31.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -6.8 \cdot 10^{+52}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;y2 \leq 7.2 \cdot 10^{-119}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 3.8 \cdot 10^{+88}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y2 \leq 9 \cdot 10^{+237}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 23: 35.5% accurate, 3.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y1 \leq -5.6 \cdot 10^{+26}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 6.5 \cdot 10^{+130}:\\ \;\;\;\;y \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right) - b \cdot \left(k \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y1 -5.6e+26)
   (* j (* y1 (- (* x i) (* y3 y4))))
   (if (<= y1 6.5e+130)
     (* y (- (* y3 (- (* c y4) (* a y5))) (* b (* k y4))))
     (* y3 (* y1 (- (* z a) (* j y4)))))))

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 (y1 <= -5.6e+26) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= 6.5e+130) {
		tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	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 (y1 <= (-5.6d+26)) then
        tmp = j * (y1 * ((x * i) - (y3 * y4)))
    else if (y1 <= 6.5d+130) then
        tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)))
    else
        tmp = y3 * (y1 * ((z * a) - (j * y4)))
    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 (y1 <= -5.6e+26) {
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	} else if (y1 <= 6.5e+130) {
		tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)));
	} else {
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y1 <= -5.6e+26:
		tmp = j * (y1 * ((x * i) - (y3 * y4)))
	elif y1 <= 6.5e+130:
		tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)))
	else:
		tmp = y3 * (y1 * ((z * a) - (j * y4)))
	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 (y1 <= -5.6e+26)
		tmp = Float64(j * Float64(y1 * Float64(Float64(x * i) - Float64(y3 * y4))));
	elseif (y1 <= 6.5e+130)
		tmp = Float64(y * Float64(Float64(y3 * Float64(Float64(c * y4) - Float64(a * y5))) - Float64(b * Float64(k * y4))));
	else
		tmp = Float64(y3 * Float64(y1 * Float64(Float64(z * a) - Float64(j * y4))));
	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 (y1 <= -5.6e+26)
		tmp = j * (y1 * ((x * i) - (y3 * y4)));
	elseif (y1 <= 6.5e+130)
		tmp = y * ((y3 * ((c * y4) - (a * y5))) - (b * (k * y4)));
	else
		tmp = y3 * (y1 * ((z * a) - (j * y4)));
	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[y1, -5.6e+26], N[(j * N[(y1 * N[(N[(x * i), $MachinePrecision] - N[(y3 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 6.5e+130], N[(y * N[(N[(y3 * N[(N[(c * y4), $MachinePrecision] - N[(a * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(b * N[(k * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y3 * N[(y1 * N[(N[(z * a), $MachinePrecision] - N[(j * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y1 < -5.59999999999999999e26

   1. Initial program 38.2%

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

   3. Taylor expanded in j around inf 42.4%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 49.3%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 49.3%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 49.3%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   if -5.59999999999999999e26 < y1 < 6.5e130

   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 y4 around inf 40.8%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y around -inf 40.3%

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

      1. mul-1-neg 40.3%

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

   6. Simplified 40.3%

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

   if 6.5e130 < y1

   1. Initial program 21.3%

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

   3. Taylor expanded in y3 around -inf 37.8%

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

   4. Taylor expanded in y1 around inf 58.7%

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

      1. +-commutative 58.7%

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

      2. mul-1-neg 58.7%

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

      3. unsub-neg 58.7%

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

      4. *-commutative 58.7%

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

   6. Simplified 58.7%

      \[\leadsto -1 \cdot \left(y3 \cdot \color{blue}{\left(y1 \cdot \left(y4 \cdot j - a \cdot z\right)\right)}\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 45.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -5.6 \cdot 10^{+26}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 6.5 \cdot 10^{+130}:\\ \;\;\;\;y \cdot \left(y3 \cdot \left(c \cdot y4 - a \cdot y5\right) - b \cdot \left(k \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y3 \cdot \left(y1 \cdot \left(z \cdot a - j \cdot y4\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 24: 31.7% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{if}\;y1 \leq -4.8 \cdot 10^{+22}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y1 \leq 1.85 \cdot 10^{-200}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 2.3 \cdot 10^{+198}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* j (* y1 (- (* x i) (* y3 y4))))))
   (if (<= y1 -4.8e+22)
     t_1
     (if (<= y1 1.85e-200)
       (* b (* y4 (- (* t j) (* y k))))
       (if (<= y1 2.3e+198) (* c (* t (- (* z i) (* y2 y4)))) 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 * (y1 * ((x * i) - (y3 * y4)));
	double tmp;
	if (y1 <= -4.8e+22) {
		tmp = t_1;
	} else if (y1 <= 1.85e-200) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 2.3e+198) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} 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 = j * (y1 * ((x * i) - (y3 * y4)))
    if (y1 <= (-4.8d+22)) then
        tmp = t_1
    else if (y1 <= 1.85d-200) then
        tmp = b * (y4 * ((t * j) - (y * k)))
    else if (y1 <= 2.3d+198) then
        tmp = c * (t * ((z * i) - (y2 * y4)))
    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 = j * (y1 * ((x * i) - (y3 * y4)));
	double tmp;
	if (y1 <= -4.8e+22) {
		tmp = t_1;
	} else if (y1 <= 1.85e-200) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 2.3e+198) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} 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 = j * (y1 * ((x * i) - (y3 * y4)))
	tmp = 0
	if y1 <= -4.8e+22:
		tmp = t_1
	elif y1 <= 1.85e-200:
		tmp = b * (y4 * ((t * j) - (y * k)))
	elif y1 <= 2.3e+198:
		tmp = c * (t * ((z * i) - (y2 * y4)))
	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(j * Float64(y1 * Float64(Float64(x * i) - Float64(y3 * y4))))
	tmp = 0.0
	if (y1 <= -4.8e+22)
		tmp = t_1;
	elseif (y1 <= 1.85e-200)
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	elseif (y1 <= 2.3e+198)
		tmp = Float64(c * Float64(t * Float64(Float64(z * i) - Float64(y2 * y4))));
	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 = j * (y1 * ((x * i) - (y3 * y4)));
	tmp = 0.0;
	if (y1 <= -4.8e+22)
		tmp = t_1;
	elseif (y1 <= 1.85e-200)
		tmp = b * (y4 * ((t * j) - (y * k)));
	elseif (y1 <= 2.3e+198)
		tmp = c * (t * ((z * i) - (y2 * y4)));
	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[(j * N[(y1 * N[(N[(x * i), $MachinePrecision] - N[(y3 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y1, -4.8e+22], t$95$1, If[LessEqual[y1, 1.85e-200], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 2.3e+198], N[(c * N[(t * N[(N[(z * i), $MachinePrecision] - N[(y2 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y1 < -4.8e22 or 2.3000000000000001e198 < y1

   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 j around inf 49.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 57.0%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 57.0%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 57.0%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   if -4.8e22 < y1 < 1.85000000000000005e-200

   1. Initial program 35.2%

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

   3. Taylor expanded in y4 around inf 45.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 38.7%

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

   if 1.85000000000000005e-200 < y1 < 2.3000000000000001e198

   1. Initial program 28.7%

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

   3. Taylor expanded in t around inf 42.6%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in c around inf 39.1%

      \[\leadsto \color{blue}{c \cdot \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 44.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -4.8 \cdot 10^{+22}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \mathbf{elif}\;y1 \leq 1.85 \cdot 10^{-200}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 2.3 \cdot 10^{+198}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;j \cdot \left(y1 \cdot \left(x \cdot i - y3 \cdot y4\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 25: 31.9% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y4 \leq -8.5 \cdot 10^{+58}:\\ \;\;\;\;y1 \cdot \left(y4 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 3.6 \cdot 10^{-113}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2 - z \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 2.25 \cdot 10^{+119}:\\ \;\;\;\;y0 \cdot \left(j \cdot \left(y3 \cdot y5 - x \cdot b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y4 -8.5e+58)
   (* y1 (* y4 (- (* k y2) (* j y3))))
   (if (<= y4 3.6e-113)
     (* y0 (* c (- (* x y2) (* z y3))))
     (if (<= y4 2.25e+119)
       (* y0 (* j (- (* y3 y5) (* x b))))
       (* b (* y4 (- (* t j) (* y 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 (y4 <= -8.5e+58) {
		tmp = y1 * (y4 * ((k * y2) - (j * y3)));
	} else if (y4 <= 3.6e-113) {
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	} else if (y4 <= 2.25e+119) {
		tmp = y0 * (j * ((y3 * y5) - (x * b)));
	} else {
		tmp = b * (y4 * ((t * j) - (y * k)));
	}
	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 (y4 <= (-8.5d+58)) then
        tmp = y1 * (y4 * ((k * y2) - (j * y3)))
    else if (y4 <= 3.6d-113) then
        tmp = y0 * (c * ((x * y2) - (z * y3)))
    else if (y4 <= 2.25d+119) then
        tmp = y0 * (j * ((y3 * y5) - (x * b)))
    else
        tmp = b * (y4 * ((t * j) - (y * k)))
    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 (y4 <= -8.5e+58) {
		tmp = y1 * (y4 * ((k * y2) - (j * y3)));
	} else if (y4 <= 3.6e-113) {
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	} else if (y4 <= 2.25e+119) {
		tmp = y0 * (j * ((y3 * y5) - (x * b)));
	} else {
		tmp = b * (y4 * ((t * j) - (y * k)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y4 <= -8.5e+58:
		tmp = y1 * (y4 * ((k * y2) - (j * y3)))
	elif y4 <= 3.6e-113:
		tmp = y0 * (c * ((x * y2) - (z * y3)))
	elif y4 <= 2.25e+119:
		tmp = y0 * (j * ((y3 * y5) - (x * b)))
	else:
		tmp = b * (y4 * ((t * j) - (y * 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 (y4 <= -8.5e+58)
		tmp = Float64(y1 * Float64(y4 * Float64(Float64(k * y2) - Float64(j * y3))));
	elseif (y4 <= 3.6e-113)
		tmp = Float64(y0 * Float64(c * Float64(Float64(x * y2) - Float64(z * y3))));
	elseif (y4 <= 2.25e+119)
		tmp = Float64(y0 * Float64(j * Float64(Float64(y3 * y5) - Float64(x * b))));
	else
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	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 (y4 <= -8.5e+58)
		tmp = y1 * (y4 * ((k * y2) - (j * y3)));
	elseif (y4 <= 3.6e-113)
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	elseif (y4 <= 2.25e+119)
		tmp = y0 * (j * ((y3 * y5) - (x * b)));
	else
		tmp = b * (y4 * ((t * j) - (y * k)));
	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[y4, -8.5e+58], N[(y1 * N[(y4 * N[(N[(k * y2), $MachinePrecision] - N[(j * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 3.6e-113], N[(y0 * N[(c * N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 2.25e+119], N[(y0 * N[(j * N[(N[(y3 * y5), $MachinePrecision] - N[(x * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if y4 < -8.50000000000000015e58

   1. Initial program 26.0%

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

   3. Taylor expanded in y4 around inf 42.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in y1 around inf 50.6%

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

   if -8.50000000000000015e58 < y4 < 3.59999999999999975e-113

   1. Initial program 32.7%

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

   3. Taylor expanded in y0 around inf 40.1%

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

   4. Taylor expanded in c around inf 34.7%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   if 3.59999999999999975e-113 < y4 < 2.2500000000000001e119

   1. Initial program 35.6%

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

   3. Taylor expanded in y0 around inf 40.2%

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

   4. Taylor expanded in j around inf 45.2%

      \[\leadsto y0 \cdot \color{blue}{\left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right)} \]

   if 2.2500000000000001e119 < y4

   1. Initial program 36.5%

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

   3. Taylor expanded in y4 around inf 51.2%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 56.7%

      \[\leadsto \color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 43.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -8.5 \cdot 10^{+58}:\\ \;\;\;\;y1 \cdot \left(y4 \cdot \left(k \cdot y2 - j \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 3.6 \cdot 10^{-113}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2 - z \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 2.25 \cdot 10^{+119}:\\ \;\;\;\;y0 \cdot \left(j \cdot \left(y3 \cdot y5 - x \cdot b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 26: 31.9% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{if}\;y4 \leq -1.9 \cdot 10^{+78}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y4 \leq 4.6 \cdot 10^{-113}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2 - z \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 6.8 \cdot 10^{+120}:\\ \;\;\;\;y0 \cdot \left(j \cdot \left(y3 \cdot y5 - x \cdot b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* b (* y4 (- (* t j) (* y k))))))
   (if (<= y4 -1.9e+78)
     t_1
     (if (<= y4 4.6e-113)
       (* y0 (* c (- (* x y2) (* z y3))))
       (if (<= y4 6.8e+120) (* y0 (* j (- (* y3 y5) (* x b)))) 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 * (y4 * ((t * j) - (y * k)));
	double tmp;
	if (y4 <= -1.9e+78) {
		tmp = t_1;
	} else if (y4 <= 4.6e-113) {
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	} else if (y4 <= 6.8e+120) {
		tmp = y0 * (j * ((y3 * y5) - (x * b)));
	} 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 = b * (y4 * ((t * j) - (y * k)))
    if (y4 <= (-1.9d+78)) then
        tmp = t_1
    else if (y4 <= 4.6d-113) then
        tmp = y0 * (c * ((x * y2) - (z * y3)))
    else if (y4 <= 6.8d+120) then
        tmp = y0 * (j * ((y3 * y5) - (x * b)))
    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 * (y4 * ((t * j) - (y * k)));
	double tmp;
	if (y4 <= -1.9e+78) {
		tmp = t_1;
	} else if (y4 <= 4.6e-113) {
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	} else if (y4 <= 6.8e+120) {
		tmp = y0 * (j * ((y3 * y5) - (x * b)));
	} 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 * (y4 * ((t * j) - (y * k)))
	tmp = 0
	if y4 <= -1.9e+78:
		tmp = t_1
	elif y4 <= 4.6e-113:
		tmp = y0 * (c * ((x * y2) - (z * y3)))
	elif y4 <= 6.8e+120:
		tmp = y0 * (j * ((y3 * y5) - (x * b)))
	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(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))))
	tmp = 0.0
	if (y4 <= -1.9e+78)
		tmp = t_1;
	elseif (y4 <= 4.6e-113)
		tmp = Float64(y0 * Float64(c * Float64(Float64(x * y2) - Float64(z * y3))));
	elseif (y4 <= 6.8e+120)
		tmp = Float64(y0 * Float64(j * Float64(Float64(y3 * y5) - Float64(x * b))));
	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 * (y4 * ((t * j) - (y * k)));
	tmp = 0.0;
	if (y4 <= -1.9e+78)
		tmp = t_1;
	elseif (y4 <= 4.6e-113)
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	elseif (y4 <= 6.8e+120)
		tmp = y0 * (j * ((y3 * y5) - (x * b)));
	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[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y4, -1.9e+78], t$95$1, If[LessEqual[y4, 4.6e-113], N[(y0 * N[(c * N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 6.8e+120], N[(y0 * N[(j * N[(N[(y3 * y5), $MachinePrecision] - N[(x * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y4 < -1.9e78 or 6.79999999999999998e120 < y4

   1. Initial program 30.0%

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

   3. Taylor expanded in y4 around inf 45.6%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 52.7%

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

   if -1.9e78 < y4 < 4.60000000000000016e-113

   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 y0 around inf 39.8%

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

   4. Taylor expanded in c around inf 34.5%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   if 4.60000000000000016e-113 < y4 < 6.79999999999999998e120

   1. Initial program 35.6%

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

   3. Taylor expanded in y0 around inf 40.2%

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

   4. Taylor expanded in j around inf 45.2%

      \[\leadsto y0 \cdot \color{blue}{\left(j \cdot \left(y3 \cdot y5 - b \cdot x\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 42.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -1.9 \cdot 10^{+78}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y4 \leq 4.6 \cdot 10^{-113}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2 - z \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 6.8 \cdot 10^{+120}:\\ \;\;\;\;y0 \cdot \left(j \cdot \left(y3 \cdot y5 - x \cdot b\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 27: 32.1% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{if}\;y4 \leq -3.2 \cdot 10^{+81}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y4 \leq 4.6 \cdot 10^{-178}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2 - z \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 7.2 \cdot 10^{+119}:\\ \;\;\;\;y0 \cdot \left(b \cdot \left(z \cdot k - x \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* b (* y4 (- (* t j) (* y k))))))
   (if (<= y4 -3.2e+81)
     t_1
     (if (<= y4 4.6e-178)
       (* y0 (* c (- (* x y2) (* z y3))))
       (if (<= y4 7.2e+119) (* y0 (* b (- (* z k) (* x 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 = b * (y4 * ((t * j) - (y * k)));
	double tmp;
	if (y4 <= -3.2e+81) {
		tmp = t_1;
	} else if (y4 <= 4.6e-178) {
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	} else if (y4 <= 7.2e+119) {
		tmp = y0 * (b * ((z * k) - (x * j)));
	} 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 = b * (y4 * ((t * j) - (y * k)))
    if (y4 <= (-3.2d+81)) then
        tmp = t_1
    else if (y4 <= 4.6d-178) then
        tmp = y0 * (c * ((x * y2) - (z * y3)))
    else if (y4 <= 7.2d+119) then
        tmp = y0 * (b * ((z * k) - (x * j)))
    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 * (y4 * ((t * j) - (y * k)));
	double tmp;
	if (y4 <= -3.2e+81) {
		tmp = t_1;
	} else if (y4 <= 4.6e-178) {
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	} else if (y4 <= 7.2e+119) {
		tmp = y0 * (b * ((z * k) - (x * j)));
	} 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 * (y4 * ((t * j) - (y * k)))
	tmp = 0
	if y4 <= -3.2e+81:
		tmp = t_1
	elif y4 <= 4.6e-178:
		tmp = y0 * (c * ((x * y2) - (z * y3)))
	elif y4 <= 7.2e+119:
		tmp = y0 * (b * ((z * k) - (x * 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(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))))
	tmp = 0.0
	if (y4 <= -3.2e+81)
		tmp = t_1;
	elseif (y4 <= 4.6e-178)
		tmp = Float64(y0 * Float64(c * Float64(Float64(x * y2) - Float64(z * y3))));
	elseif (y4 <= 7.2e+119)
		tmp = Float64(y0 * Float64(b * Float64(Float64(z * k) - Float64(x * j))));
	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 * (y4 * ((t * j) - (y * k)));
	tmp = 0.0;
	if (y4 <= -3.2e+81)
		tmp = t_1;
	elseif (y4 <= 4.6e-178)
		tmp = y0 * (c * ((x * y2) - (z * y3)));
	elseif (y4 <= 7.2e+119)
		tmp = y0 * (b * ((z * k) - (x * j)));
	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[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y4, -3.2e+81], t$95$1, If[LessEqual[y4, 4.6e-178], N[(y0 * N[(c * N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 7.2e+119], N[(y0 * N[(b * N[(N[(z * k), $MachinePrecision] - N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y4 < -3.2e81 or 7.20000000000000003e119 < y4

   1. Initial program 30.0%

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

   3. Taylor expanded in y4 around inf 45.6%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 52.7%

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

   if -3.2e81 < y4 < 4.59999999999999989e-178

   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 39.1%

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

   4. Taylor expanded in c around inf 35.1%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   if 4.59999999999999989e-178 < y4 < 7.20000000000000003e119

   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 y0 around inf 41.4%

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

   4. Taylor expanded in b around inf 38.4%

      \[\leadsto y0 \cdot \color{blue}{\left(b \cdot \left(k \cdot z - j \cdot x\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 42.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -3.2 \cdot 10^{+81}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y4 \leq 4.6 \cdot 10^{-178}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2 - z \cdot y3\right)\right)\\ \mathbf{elif}\;y4 \leq 7.2 \cdot 10^{+119}:\\ \;\;\;\;y0 \cdot \left(b \cdot \left(z \cdot k - x \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 28: 31.0% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{if}\;y4 \leq -4.2 \cdot 10^{+137}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y4 \leq -5 \cdot 10^{-196}:\\ \;\;\;\;j \cdot \left(t \cdot \left(b \cdot y4 - i \cdot y5\right)\right)\\ \mathbf{elif}\;y4 \leq 3.7 \cdot 10^{+118}:\\ \;\;\;\;y0 \cdot \left(b \cdot \left(z \cdot k - x \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* b (* y4 (- (* t j) (* y k))))))
   (if (<= y4 -4.2e+137)
     t_1
     (if (<= y4 -5e-196)
       (* j (* t (- (* b y4) (* i y5))))
       (if (<= y4 3.7e+118) (* y0 (* b (- (* z k) (* x 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 = b * (y4 * ((t * j) - (y * k)));
	double tmp;
	if (y4 <= -4.2e+137) {
		tmp = t_1;
	} else if (y4 <= -5e-196) {
		tmp = j * (t * ((b * y4) - (i * y5)));
	} else if (y4 <= 3.7e+118) {
		tmp = y0 * (b * ((z * k) - (x * j)));
	} 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 = b * (y4 * ((t * j) - (y * k)))
    if (y4 <= (-4.2d+137)) then
        tmp = t_1
    else if (y4 <= (-5d-196)) then
        tmp = j * (t * ((b * y4) - (i * y5)))
    else if (y4 <= 3.7d+118) then
        tmp = y0 * (b * ((z * k) - (x * j)))
    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 * (y4 * ((t * j) - (y * k)));
	double tmp;
	if (y4 <= -4.2e+137) {
		tmp = t_1;
	} else if (y4 <= -5e-196) {
		tmp = j * (t * ((b * y4) - (i * y5)));
	} else if (y4 <= 3.7e+118) {
		tmp = y0 * (b * ((z * k) - (x * j)));
	} 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 * (y4 * ((t * j) - (y * k)))
	tmp = 0
	if y4 <= -4.2e+137:
		tmp = t_1
	elif y4 <= -5e-196:
		tmp = j * (t * ((b * y4) - (i * y5)))
	elif y4 <= 3.7e+118:
		tmp = y0 * (b * ((z * k) - (x * 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(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))))
	tmp = 0.0
	if (y4 <= -4.2e+137)
		tmp = t_1;
	elseif (y4 <= -5e-196)
		tmp = Float64(j * Float64(t * Float64(Float64(b * y4) - Float64(i * y5))));
	elseif (y4 <= 3.7e+118)
		tmp = Float64(y0 * Float64(b * Float64(Float64(z * k) - Float64(x * j))));
	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 * (y4 * ((t * j) - (y * k)));
	tmp = 0.0;
	if (y4 <= -4.2e+137)
		tmp = t_1;
	elseif (y4 <= -5e-196)
		tmp = j * (t * ((b * y4) - (i * y5)));
	elseif (y4 <= 3.7e+118)
		tmp = y0 * (b * ((z * k) - (x * j)));
	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[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y4, -4.2e+137], t$95$1, If[LessEqual[y4, -5e-196], N[(j * N[(t * N[(N[(b * y4), $MachinePrecision] - N[(i * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 3.7e+118], N[(y0 * N[(b * N[(N[(z * k), $MachinePrecision] - N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y4 < -4.1999999999999998e137 or 3.69999999999999987e118 < y4

   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 y4 around inf 43.6%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 53.1%

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

   if -4.1999999999999998e137 < y4 < -5.0000000000000005e-196

   1. Initial program 26.9%

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

   3. Taylor expanded in j around inf 45.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in t around inf 37.1%

      \[\leadsto \color{blue}{j \cdot \left(t \cdot \left(b \cdot y4 - i \cdot y5\right)\right)} \]

   if -5.0000000000000005e-196 < y4 < 3.69999999999999987e118

   1. Initial program 37.1%

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

   3. Taylor expanded in y0 around inf 43.4%

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

   4. Taylor expanded in b around inf 37.1%

      \[\leadsto y0 \cdot \color{blue}{\left(b \cdot \left(k \cdot z - j \cdot x\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 42.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -4.2 \cdot 10^{+137}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y4 \leq -5 \cdot 10^{-196}:\\ \;\;\;\;j \cdot \left(t \cdot \left(b \cdot y4 - i \cdot y5\right)\right)\\ \mathbf{elif}\;y4 \leq 3.7 \cdot 10^{+118}:\\ \;\;\;\;y0 \cdot \left(b \cdot \left(z \cdot k - x \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 29: 29.5% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := j \cdot \left(x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \mathbf{if}\;y1 \leq -2.05 \cdot 10^{-29}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y1 \leq 1.05 \cdot 10^{-200}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 1.35 \cdot 10^{+184}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* j (* x (- (* i y1) (* b y0))))))
   (if (<= y1 -2.05e-29)
     t_1
     (if (<= y1 1.05e-200)
       (* b (* y4 (- (* t j) (* y k))))
       (if (<= y1 1.35e+184) (* c (* t (- (* z i) (* y2 y4)))) 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 * (x * ((i * y1) - (b * y0)));
	double tmp;
	if (y1 <= -2.05e-29) {
		tmp = t_1;
	} else if (y1 <= 1.05e-200) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 1.35e+184) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} 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 = j * (x * ((i * y1) - (b * y0)))
    if (y1 <= (-2.05d-29)) then
        tmp = t_1
    else if (y1 <= 1.05d-200) then
        tmp = b * (y4 * ((t * j) - (y * k)))
    else if (y1 <= 1.35d+184) then
        tmp = c * (t * ((z * i) - (y2 * y4)))
    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 = j * (x * ((i * y1) - (b * y0)));
	double tmp;
	if (y1 <= -2.05e-29) {
		tmp = t_1;
	} else if (y1 <= 1.05e-200) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 1.35e+184) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} 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 = j * (x * ((i * y1) - (b * y0)))
	tmp = 0
	if y1 <= -2.05e-29:
		tmp = t_1
	elif y1 <= 1.05e-200:
		tmp = b * (y4 * ((t * j) - (y * k)))
	elif y1 <= 1.35e+184:
		tmp = c * (t * ((z * i) - (y2 * y4)))
	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(j * Float64(x * Float64(Float64(i * y1) - Float64(b * y0))))
	tmp = 0.0
	if (y1 <= -2.05e-29)
		tmp = t_1;
	elseif (y1 <= 1.05e-200)
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	elseif (y1 <= 1.35e+184)
		tmp = Float64(c * Float64(t * Float64(Float64(z * i) - Float64(y2 * y4))));
	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 = j * (x * ((i * y1) - (b * y0)));
	tmp = 0.0;
	if (y1 <= -2.05e-29)
		tmp = t_1;
	elseif (y1 <= 1.05e-200)
		tmp = b * (y4 * ((t * j) - (y * k)));
	elseif (y1 <= 1.35e+184)
		tmp = c * (t * ((z * i) - (y2 * y4)));
	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[(j * N[(x * N[(N[(i * y1), $MachinePrecision] - N[(b * y0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y1, -2.05e-29], t$95$1, If[LessEqual[y1, 1.05e-200], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 1.35e+184], N[(c * N[(t * N[(N[(z * i), $MachinePrecision] - N[(y2 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y1 < -2.0499999999999999e-29 or 1.35e184 < y1

   1. Initial program 34.3%

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

   3. Taylor expanded in j around inf 46.7%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in x around inf 43.9%

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

   if -2.0499999999999999e-29 < y1 < 1.05e-200

   1. Initial program 33.5%

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

   3. Taylor expanded in y4 around inf 46.1%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 39.9%

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

   if 1.05e-200 < y1 < 1.35e184

   1. Initial program 29.8%

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

   3. Taylor expanded in t around inf 44.1%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in c around inf 39.2%

      \[\leadsto \color{blue}{c \cdot \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 41.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -2.05 \cdot 10^{-29}:\\ \;\;\;\;j \cdot \left(x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \mathbf{elif}\;y1 \leq 1.05 \cdot 10^{-200}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 1.35 \cdot 10^{+184}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;j \cdot \left(x \cdot \left(i \cdot y1 - b \cdot y0\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 30: 31.1% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{if}\;y4 \leq -9.8 \cdot 10^{+138}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y4 \leq -1.7 \cdot 10^{-159}:\\ \;\;\;\;j \cdot \left(t \cdot \left(b \cdot y4 - i \cdot y5\right)\right)\\ \mathbf{elif}\;y4 \leq 1.16 \cdot 10^{+128}:\\ \;\;\;\;c \cdot \left(y0 \cdot \left(x \cdot y2 - z \cdot y3\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* b (* y4 (- (* t j) (* y k))))))
   (if (<= y4 -9.8e+138)
     t_1
     (if (<= y4 -1.7e-159)
       (* j (* t (- (* b y4) (* i y5))))
       (if (<= y4 1.16e+128) (* c (* y0 (- (* x y2) (* z y3)))) 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 * (y4 * ((t * j) - (y * k)));
	double tmp;
	if (y4 <= -9.8e+138) {
		tmp = t_1;
	} else if (y4 <= -1.7e-159) {
		tmp = j * (t * ((b * y4) - (i * y5)));
	} else if (y4 <= 1.16e+128) {
		tmp = c * (y0 * ((x * y2) - (z * y3)));
	} 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 = b * (y4 * ((t * j) - (y * k)))
    if (y4 <= (-9.8d+138)) then
        tmp = t_1
    else if (y4 <= (-1.7d-159)) then
        tmp = j * (t * ((b * y4) - (i * y5)))
    else if (y4 <= 1.16d+128) then
        tmp = c * (y0 * ((x * y2) - (z * y3)))
    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 * (y4 * ((t * j) - (y * k)));
	double tmp;
	if (y4 <= -9.8e+138) {
		tmp = t_1;
	} else if (y4 <= -1.7e-159) {
		tmp = j * (t * ((b * y4) - (i * y5)));
	} else if (y4 <= 1.16e+128) {
		tmp = c * (y0 * ((x * y2) - (z * y3)));
	} 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 * (y4 * ((t * j) - (y * k)))
	tmp = 0
	if y4 <= -9.8e+138:
		tmp = t_1
	elif y4 <= -1.7e-159:
		tmp = j * (t * ((b * y4) - (i * y5)))
	elif y4 <= 1.16e+128:
		tmp = c * (y0 * ((x * y2) - (z * y3)))
	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(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))))
	tmp = 0.0
	if (y4 <= -9.8e+138)
		tmp = t_1;
	elseif (y4 <= -1.7e-159)
		tmp = Float64(j * Float64(t * Float64(Float64(b * y4) - Float64(i * y5))));
	elseif (y4 <= 1.16e+128)
		tmp = Float64(c * Float64(y0 * Float64(Float64(x * y2) - Float64(z * y3))));
	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 * (y4 * ((t * j) - (y * k)));
	tmp = 0.0;
	if (y4 <= -9.8e+138)
		tmp = t_1;
	elseif (y4 <= -1.7e-159)
		tmp = j * (t * ((b * y4) - (i * y5)));
	elseif (y4 <= 1.16e+128)
		tmp = c * (y0 * ((x * y2) - (z * y3)));
	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[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y4, -9.8e+138], t$95$1, If[LessEqual[y4, -1.7e-159], N[(j * N[(t * N[(N[(b * y4), $MachinePrecision] - N[(i * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y4, 1.16e+128], N[(c * N[(y0 * N[(N[(x * y2), $MachinePrecision] - N[(z * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if y4 < -9.79999999999999966e138 or 1.1600000000000001e128 < y4

   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 y4 around inf 42.7%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 53.8%

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

   if -9.79999999999999966e138 < y4 < -1.69999999999999992e-159

   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 j around inf 49.7%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in t around inf 38.9%

      \[\leadsto \color{blue}{j \cdot \left(t \cdot \left(b \cdot y4 - i \cdot y5\right)\right)} \]

   if -1.69999999999999992e-159 < y4 < 1.1600000000000001e128

   1. Initial program 34.3%

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

   3. Taylor expanded in y0 around inf 40.7%

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

   4. Taylor expanded in c around inf 31.5%

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

4. Final simplification 39.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -9.8 \cdot 10^{+138}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y4 \leq -1.7 \cdot 10^{-159}:\\ \;\;\;\;j \cdot \left(t \cdot \left(b \cdot y4 - i \cdot y5\right)\right)\\ \mathbf{elif}\;y4 \leq 1.16 \cdot 10^{+128}:\\ \;\;\;\;c \cdot \left(y0 \cdot \left(x \cdot y2 - z \cdot y3\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 31: 27.8% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y1 \leq -5.8 \cdot 10^{+28}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y1 \leq 4.9 \cdot 10^{-202}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 5.5 \cdot 10^{+201}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y1 -5.8e+28)
   (* i (* j (* x y1)))
   (if (<= y1 4.9e-202)
     (* b (* y4 (- (* t j) (* y k))))
     (if (<= y1 5.5e+201)
       (* c (* t (- (* z i) (* y2 y4))))
       (* (* y3 y4) (* 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 (y1 <= -5.8e+28) {
		tmp = i * (j * (x * y1));
	} else if (y1 <= 4.9e-202) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 5.5e+201) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	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 (y1 <= (-5.8d+28)) then
        tmp = i * (j * (x * y1))
    else if (y1 <= 4.9d-202) then
        tmp = b * (y4 * ((t * j) - (y * k)))
    else if (y1 <= 5.5d+201) then
        tmp = c * (t * ((z * i) - (y2 * y4)))
    else
        tmp = (y3 * y4) * (j * -y1)
    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 (y1 <= -5.8e+28) {
		tmp = i * (j * (x * y1));
	} else if (y1 <= 4.9e-202) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else if (y1 <= 5.5e+201) {
		tmp = c * (t * ((z * i) - (y2 * y4)));
	} else {
		tmp = (y3 * y4) * (j * -y1);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y1 <= -5.8e+28:
		tmp = i * (j * (x * y1))
	elif y1 <= 4.9e-202:
		tmp = b * (y4 * ((t * j) - (y * k)))
	elif y1 <= 5.5e+201:
		tmp = c * (t * ((z * i) - (y2 * y4)))
	else:
		tmp = (y3 * y4) * (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 (y1 <= -5.8e+28)
		tmp = Float64(i * Float64(j * Float64(x * y1)));
	elseif (y1 <= 4.9e-202)
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	elseif (y1 <= 5.5e+201)
		tmp = Float64(c * Float64(t * Float64(Float64(z * i) - Float64(y2 * y4))));
	else
		tmp = Float64(Float64(y3 * y4) * Float64(j * Float64(-y1)));
	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 (y1 <= -5.8e+28)
		tmp = i * (j * (x * y1));
	elseif (y1 <= 4.9e-202)
		tmp = b * (y4 * ((t * j) - (y * k)));
	elseif (y1 <= 5.5e+201)
		tmp = c * (t * ((z * i) - (y2 * y4)));
	else
		tmp = (y3 * y4) * (j * -y1);
	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[y1, -5.8e+28], N[(i * N[(j * N[(x * y1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 4.9e-202], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y1, 5.5e+201], N[(c * N[(t * N[(N[(z * i), $MachinePrecision] - N[(y2 * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(y3 * y4), $MachinePrecision] * N[(j * (-y1)), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if y1 < -5.8000000000000002e28

   1. Initial program 38.2%

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

   3. Taylor expanded in j around inf 42.4%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 49.3%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 49.3%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 49.3%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 33.4%

      \[\leadsto \color{blue}{i \cdot \left(j \cdot \left(x \cdot y1\right)\right)} \]

   if -5.8000000000000002e28 < y1 < 4.9000000000000004e-202

   1. Initial program 35.2%

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

   3. Taylor expanded in y4 around inf 45.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 38.7%

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

   if 4.9000000000000004e-202 < y1 < 5.49999999999999946e201

   1. Initial program 28.7%

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

   3. Taylor expanded in t around inf 42.6%

      \[\leadsto \color{blue}{t \cdot \left(\left(-1 \cdot \left(z \cdot \left(a \cdot b - c \cdot i\right)\right) + j \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - y2 \cdot \left(c \cdot y4 - a \cdot y5\right)\right)} + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in c around inf 39.1%

      \[\leadsto \color{blue}{c \cdot \left(t \cdot \left(i \cdot z - y2 \cdot y4\right)\right)} \]

   if 5.49999999999999946e201 < y1

   1. Initial program 24.6%

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

   3. Taylor expanded in j around inf 64.9%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 72.4%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 72.4%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 72.4%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around inf 48.9%

      \[\leadsto \color{blue}{-1 \cdot \left(j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)\right)} \]
   8. Step-by-step derivation

      1. mul-1-neg 48.9%

         \[\leadsto \color{blue}{-j \cdot \left(y1 \cdot \left(y3 \cdot y4\right)\right)} \]

      2. associate-*r* 52.6%

         \[\leadsto -\color{blue}{\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]

   9. Simplified 52.6%

      \[\leadsto \color{blue}{-\left(j \cdot y1\right) \cdot \left(y3 \cdot y4\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 39.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y1 \leq -5.8 \cdot 10^{+28}:\\ \;\;\;\;i \cdot \left(j \cdot \left(x \cdot y1\right)\right)\\ \mathbf{elif}\;y1 \leq 4.9 \cdot 10^{-202}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{elif}\;y1 \leq 5.5 \cdot 10^{+201}:\\ \;\;\;\;c \cdot \left(t \cdot \left(z \cdot i - y2 \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(y3 \cdot y4\right) \cdot \left(j \cdot \left(-y1\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 32: 23.2% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y2 \leq -1.3 \cdot 10^{+148}:\\ \;\;\;\;\left(k \cdot y0\right) \cdot \left(y2 \cdot \left(-y5\right)\right)\\ \mathbf{elif}\;y2 \leq -4200000:\\ \;\;\;\;b \cdot \left(\left(y \cdot y4\right) \cdot \left(-k\right)\right)\\ \mathbf{elif}\;y2 \leq 4.5 \cdot 10^{-125}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y2 -1.3e+148)
   (* (* k y0) (* y2 (- y5)))
   (if (<= y2 -4200000.0)
     (* b (* (* y y4) (- k)))
     (if (<= y2 4.5e-125)
       (* b (* y4 (* t j)))
       (* a (* y5 (- (* t y2) (* y y3))))))))

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 (y2 <= -1.3e+148) {
		tmp = (k * y0) * (y2 * -y5);
	} else if (y2 <= -4200000.0) {
		tmp = b * ((y * y4) * -k);
	} else if (y2 <= 4.5e-125) {
		tmp = b * (y4 * (t * j));
	} else {
		tmp = a * (y5 * ((t * y2) - (y * y3)));
	}
	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 (y2 <= (-1.3d+148)) then
        tmp = (k * y0) * (y2 * -y5)
    else if (y2 <= (-4200000.0d0)) then
        tmp = b * ((y * y4) * -k)
    else if (y2 <= 4.5d-125) then
        tmp = b * (y4 * (t * j))
    else
        tmp = a * (y5 * ((t * y2) - (y * y3)))
    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 (y2 <= -1.3e+148) {
		tmp = (k * y0) * (y2 * -y5);
	} else if (y2 <= -4200000.0) {
		tmp = b * ((y * y4) * -k);
	} else if (y2 <= 4.5e-125) {
		tmp = b * (y4 * (t * j));
	} else {
		tmp = a * (y5 * ((t * y2) - (y * y3)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y2 <= -1.3e+148:
		tmp = (k * y0) * (y2 * -y5)
	elif y2 <= -4200000.0:
		tmp = b * ((y * y4) * -k)
	elif y2 <= 4.5e-125:
		tmp = b * (y4 * (t * j))
	else:
		tmp = a * (y5 * ((t * y2) - (y * y3)))
	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 (y2 <= -1.3e+148)
		tmp = Float64(Float64(k * y0) * Float64(y2 * Float64(-y5)));
	elseif (y2 <= -4200000.0)
		tmp = Float64(b * Float64(Float64(y * y4) * Float64(-k)));
	elseif (y2 <= 4.5e-125)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	else
		tmp = Float64(a * Float64(y5 * Float64(Float64(t * y2) - Float64(y * y3))));
	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 (y2 <= -1.3e+148)
		tmp = (k * y0) * (y2 * -y5);
	elseif (y2 <= -4200000.0)
		tmp = b * ((y * y4) * -k);
	elseif (y2 <= 4.5e-125)
		tmp = b * (y4 * (t * j));
	else
		tmp = a * (y5 * ((t * y2) - (y * y3)));
	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[y2, -1.3e+148], N[(N[(k * y0), $MachinePrecision] * N[(y2 * (-y5)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, -4200000.0], N[(b * N[(N[(y * y4), $MachinePrecision] * (-k)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 4.5e-125], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * N[(y5 * N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if y2 < -1.3e148

   1. Initial program 20.6%

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

   3. Taylor expanded in y2 around inf 44.9%

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

   4. Taylor expanded in y5 around -inf 42.1%

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

      1. mul-1-neg 42.1%

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

   6. Simplified 42.1%

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

   7. Taylor expanded in k around inf 33.1%

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

      1. mul-1-neg 33.1%

         \[\leadsto \color{blue}{-k \cdot \left(y0 \cdot \left(y2 \cdot y5\right)\right)} \]

      2. associate-*r* 39.3%

         \[\leadsto -\color{blue}{\left(k \cdot y0\right) \cdot \left(y2 \cdot y5\right)} \]

      3. *-commutative 39.3%

         \[\leadsto -\color{blue}{\left(y0 \cdot k\right)} \cdot \left(y2 \cdot y5\right) \]

      4. distribute-rgt-neg-in 39.3%

         \[\leadsto \color{blue}{\left(y0 \cdot k\right) \cdot \left(-y2 \cdot y5\right)} \]

      5. *-commutative 39.3%

         \[\leadsto \left(y0 \cdot k\right) \cdot \left(-\color{blue}{y5 \cdot y2}\right) \]

   9. Simplified 39.3%

      \[\leadsto \color{blue}{\left(y0 \cdot k\right) \cdot \left(-y5 \cdot y2\right)} \]

   if -1.3e148 < y2 < -4.2e6

   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 y4 around inf 47.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in k around inf 47.5%

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

   5. Taylor expanded in b around inf 41.4%

      \[\leadsto \color{blue}{-1 \cdot \left(b \cdot \left(k \cdot \left(y \cdot y4\right)\right)\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 41.4%

         \[\leadsto \color{blue}{-b \cdot \left(k \cdot \left(y \cdot y4\right)\right)} \]

      2. *-commutative 41.4%

         \[\leadsto -\color{blue}{\left(k \cdot \left(y \cdot y4\right)\right) \cdot b} \]

      3. distribute-rgt-neg-in 41.4%

         \[\leadsto \color{blue}{\left(k \cdot \left(y \cdot y4\right)\right) \cdot \left(-b\right)} \]

      4. *-commutative 41.4%

         \[\leadsto \left(k \cdot \color{blue}{\left(y4 \cdot y\right)}\right) \cdot \left(-b\right) \]

   7. Simplified 41.4%

      \[\leadsto \color{blue}{\left(k \cdot \left(y4 \cdot y\right)\right) \cdot \left(-b\right)} \]

   if -4.2e6 < y2 < 4.50000000000000012e-125

   1. Initial program 42.6%

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

   3. Taylor expanded in y4 around inf 44.3%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 34.0%

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

   5. Taylor expanded in j around inf 26.7%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 28.6%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 28.6%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 4.50000000000000012e-125 < y2

   1. Initial program 24.2%

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

   3. Taylor expanded in y4 around inf 30.5%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 36.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 34.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -1.3 \cdot 10^{+148}:\\ \;\;\;\;\left(k \cdot y0\right) \cdot \left(y2 \cdot \left(-y5\right)\right)\\ \mathbf{elif}\;y2 \leq -4200000:\\ \;\;\;\;b \cdot \left(\left(y \cdot y4\right) \cdot \left(-k\right)\right)\\ \mathbf{elif}\;y2 \leq 4.5 \cdot 10^{-125}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 33: 20.6% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.6 \cdot 10^{+196}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;x \leq -1.1 \cdot 10^{-266}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;x \leq 7.5 \cdot 10^{-118}:\\ \;\;\;\;k \cdot \left(y4 \cdot \left(y1 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= x -6.6e+196)
   (* i (* y1 (* x j)))
   (if (<= x -1.1e-266)
     (* b (* y4 (* t j)))
     (if (<= x 7.5e-118) (* k (* y4 (* y1 y2))) (* y0 (* c (* x y2)))))))

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 <= -6.6e+196) {
		tmp = i * (y1 * (x * j));
	} else if (x <= -1.1e-266) {
		tmp = b * (y4 * (t * j));
	} else if (x <= 7.5e-118) {
		tmp = k * (y4 * (y1 * y2));
	} else {
		tmp = y0 * (c * (x * y2));
	}
	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 (x <= (-6.6d+196)) then
        tmp = i * (y1 * (x * j))
    else if (x <= (-1.1d-266)) then
        tmp = b * (y4 * (t * j))
    else if (x <= 7.5d-118) then
        tmp = k * (y4 * (y1 * y2))
    else
        tmp = y0 * (c * (x * y2))
    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 (x <= -6.6e+196) {
		tmp = i * (y1 * (x * j));
	} else if (x <= -1.1e-266) {
		tmp = b * (y4 * (t * j));
	} else if (x <= 7.5e-118) {
		tmp = k * (y4 * (y1 * y2));
	} else {
		tmp = y0 * (c * (x * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if x <= -6.6e+196:
		tmp = i * (y1 * (x * j))
	elif x <= -1.1e-266:
		tmp = b * (y4 * (t * j))
	elif x <= 7.5e-118:
		tmp = k * (y4 * (y1 * y2))
	else:
		tmp = y0 * (c * (x * y2))
	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 <= -6.6e+196)
		tmp = Float64(i * Float64(y1 * Float64(x * j)));
	elseif (x <= -1.1e-266)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	elseif (x <= 7.5e-118)
		tmp = Float64(k * Float64(y4 * Float64(y1 * y2)));
	else
		tmp = Float64(y0 * Float64(c * Float64(x * y2)));
	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 (x <= -6.6e+196)
		tmp = i * (y1 * (x * j));
	elseif (x <= -1.1e-266)
		tmp = b * (y4 * (t * j));
	elseif (x <= 7.5e-118)
		tmp = k * (y4 * (y1 * y2));
	else
		tmp = y0 * (c * (x * y2));
	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[x, -6.6e+196], N[(i * N[(y1 * N[(x * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -1.1e-266], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 7.5e-118], N[(k * N[(y4 * N[(y1 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y0 * N[(c * N[(x * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if x < -6.6000000000000003e196

   1. Initial program 15.9%

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

   3. Taylor expanded in j around inf 38.0%

      \[\leadsto \color{blue}{j \cdot \left(\left(-1 \cdot \left(y3 \cdot \left(y1 \cdot y4 - y0 \cdot y5\right)\right) + t \cdot \left(b \cdot y4 - i \cdot y5\right)\right) - x \cdot \left(b \cdot y0 - i \cdot y1\right)\right)} \]

   4. Taylor expanded in y1 around -inf 32.4%

      \[\leadsto j \cdot \color{blue}{\left(-1 \cdot \left(y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)\right)} \]
   5. Step-by-step derivation

      1. mul-1-neg 32.4%

         \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   6. Simplified 32.4%

      \[\leadsto j \cdot \color{blue}{\left(-y1 \cdot \left(y3 \cdot y4 - i \cdot x\right)\right)} \]

   7. Taylor expanded in y3 around 0 44.4%

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

      1. associate-*r* 50.4%

         \[\leadsto i \cdot \color{blue}{\left(\left(j \cdot x\right) \cdot y1\right)} \]

   9. Simplified 50.4%

      \[\leadsto \color{blue}{i \cdot \left(\left(j \cdot x\right) \cdot y1\right)} \]

   if -6.6000000000000003e196 < x < -1.1e-266

   1. Initial program 40.0%

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

   3. Taylor expanded in y4 around inf 47.8%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 30.7%

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

   5. Taylor expanded in j around inf 24.0%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 24.0%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 24.0%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if -1.1e-266 < x < 7.49999999999999978e-118

   1. Initial program 34.0%

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

   3. Taylor expanded in y4 around inf 48.8%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in k around inf 47.0%

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

   5. Taylor expanded in y1 around inf 22.2%

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

      1. associate-*r* 26.6%

         \[\leadsto k \cdot \color{blue}{\left(\left(y1 \cdot y2\right) \cdot y4\right)} \]

      2. *-commutative 26.6%

         \[\leadsto k \cdot \left(\color{blue}{\left(y2 \cdot y1\right)} \cdot y4\right) \]

   7. Simplified 26.6%

      \[\leadsto \color{blue}{k \cdot \left(\left(y2 \cdot y1\right) \cdot y4\right)} \]

   if 7.49999999999999978e-118 < x

   1. Initial program 28.8%

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

   3. Taylor expanded in y0 around inf 38.3%

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

   4. Taylor expanded in c around inf 32.7%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 32.8%

      \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(x \cdot y2\right)}\right) \]
   6. Step-by-step derivation

      1. *-commutative 32.8%

         \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(y2 \cdot x\right)}\right) \]

   7. Simplified 32.8%

      \[\leadsto y0 \cdot \left(c \cdot \color{blue}{\left(y2 \cdot x\right)}\right) \]
3. Recombined 4 regimes into one program.

4. Final simplification 30.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -6.6 \cdot 10^{+196}:\\ \;\;\;\;i \cdot \left(y1 \cdot \left(x \cdot j\right)\right)\\ \mathbf{elif}\;x \leq -1.1 \cdot 10^{-266}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;x \leq 7.5 \cdot 10^{-118}:\\ \;\;\;\;k \cdot \left(y4 \cdot \left(y1 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;y0 \cdot \left(c \cdot \left(x \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 34: 21.6% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{if}\;y2 \leq -3.7 \cdot 10^{+52}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y2 \leq 1.65 \cdot 10^{-96}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 8.2 \cdot 10^{+235}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (let* ((t_1 (* c (* x (* y0 y2)))))
   (if (<= y2 -3.7e+52)
     t_1
     (if (<= y2 1.65e-96)
       (* b (* y4 (* t j)))
       (if (<= y2 8.2e+235) t_1 (* a (* y5 (* t y2))))))))

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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -3.7e+52) {
		tmp = t_1;
	} else if (y2 <= 1.65e-96) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 8.2e+235) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 = c * (x * (y0 * y2))
    if (y2 <= (-3.7d+52)) then
        tmp = t_1
    else if (y2 <= 1.65d-96) then
        tmp = b * (y4 * (t * j))
    else if (y2 <= 8.2d+235) then
        tmp = t_1
    else
        tmp = a * (y5 * (t * y2))
    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 = c * (x * (y0 * y2));
	double tmp;
	if (y2 <= -3.7e+52) {
		tmp = t_1;
	} else if (y2 <= 1.65e-96) {
		tmp = b * (y4 * (t * j));
	} else if (y2 <= 8.2e+235) {
		tmp = t_1;
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	t_1 = c * (x * (y0 * y2))
	tmp = 0
	if y2 <= -3.7e+52:
		tmp = t_1
	elif y2 <= 1.65e-96:
		tmp = b * (y4 * (t * j))
	elif y2 <= 8.2e+235:
		tmp = t_1
	else:
		tmp = a * (y5 * (t * y2))
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5)
	t_1 = Float64(c * Float64(x * Float64(y0 * y2)))
	tmp = 0.0
	if (y2 <= -3.7e+52)
		tmp = t_1;
	elseif (y2 <= 1.65e-96)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	elseif (y2 <= 8.2e+235)
		tmp = t_1;
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 = c * (x * (y0 * y2));
	tmp = 0.0;
	if (y2 <= -3.7e+52)
		tmp = t_1;
	elseif (y2 <= 1.65e-96)
		tmp = b * (y4 * (t * j));
	elseif (y2 <= 8.2e+235)
		tmp = t_1;
	else
		tmp = a * (y5 * (t * y2));
	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[(c * N[(x * N[(y0 * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y2, -3.7e+52], t$95$1, If[LessEqual[y2, 1.65e-96], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 8.2e+235], t$95$1, N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if y2 < -3.7e52 or 1.64999999999999995e-96 < y2 < 8.2000000000000003e235

   1. Initial program 26.6%

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

   3. Taylor expanded in y0 around inf 34.7%

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

   4. Taylor expanded in c around inf 32.2%

      \[\leadsto y0 \cdot \color{blue}{\left(c \cdot \left(x \cdot y2 - y3 \cdot z\right)\right)} \]

   5. Taylor expanded in x around inf 29.6%

      \[\leadsto \color{blue}{c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)} \]

   if -3.7e52 < y2 < 1.64999999999999995e-96

   1. Initial program 39.7%

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

   3. Taylor expanded in y4 around inf 42.8%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 31.5%

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

   5. Taylor expanded in j around inf 24.0%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 25.6%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 25.6%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 8.2000000000000003e235 < y2

   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 y4 around inf 40.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 67.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 67.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 29.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq -3.7 \cdot 10^{+52}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{elif}\;y2 \leq 1.65 \cdot 10^{-96}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{elif}\;y2 \leq 8.2 \cdot 10^{+235}:\\ \;\;\;\;c \cdot \left(x \cdot \left(y0 \cdot y2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 35: 32.2% accurate, 4.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y4 \leq -1.76 \cdot 10^{+31} \lor \neg \left(y4 \leq 0.000152\right):\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (or (<= y4 -1.76e+31) (not (<= y4 0.000152)))
   (* b (* y4 (- (* t j) (* y k))))
   (* a (* y5 (- (* t y2) (* y y3))))))

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 ((y4 <= -1.76e+31) || !(y4 <= 0.000152)) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else {
		tmp = a * (y5 * ((t * y2) - (y * y3)));
	}
	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 ((y4 <= (-1.76d+31)) .or. (.not. (y4 <= 0.000152d0))) then
        tmp = b * (y4 * ((t * j) - (y * k)))
    else
        tmp = a * (y5 * ((t * y2) - (y * y3)))
    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 ((y4 <= -1.76e+31) || !(y4 <= 0.000152)) {
		tmp = b * (y4 * ((t * j) - (y * k)));
	} else {
		tmp = a * (y5 * ((t * y2) - (y * y3)));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if (y4 <= -1.76e+31) or not (y4 <= 0.000152):
		tmp = b * (y4 * ((t * j) - (y * k)))
	else:
		tmp = a * (y5 * ((t * y2) - (y * y3)))
	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 ((y4 <= -1.76e+31) || !(y4 <= 0.000152))
		tmp = Float64(b * Float64(y4 * Float64(Float64(t * j) - Float64(y * k))));
	else
		tmp = Float64(a * Float64(y5 * Float64(Float64(t * y2) - Float64(y * y3))));
	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 ((y4 <= -1.76e+31) || ~((y4 <= 0.000152)))
		tmp = b * (y4 * ((t * j) - (y * k)));
	else
		tmp = a * (y5 * ((t * y2) - (y * y3)));
	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[y4, -1.76e+31], N[Not[LessEqual[y4, 0.000152]], $MachinePrecision]], N[(b * N[(y4 * N[(N[(t * j), $MachinePrecision] - N[(y * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * N[(y5 * N[(N[(t * y2), $MachinePrecision] - N[(y * y3), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y4 < -1.76e31 or 1.5200000000000001e-4 < y4

   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 y4 around inf 44.6%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 44.5%

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

   if -1.76e31 < y4 < 1.5200000000000001e-4

   1. Initial program 34.6%

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

   3. Taylor expanded in y4 around inf 34.4%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 29.5%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 37.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y4 \leq -1.76 \cdot 10^{+31} \lor \neg \left(y4 \leq 0.000152\right):\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j - y \cdot k\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 36: 22.1% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y2 \leq -1.86 \cdot 10^{+53}:\\ \;\;\;\;a \cdot \left(t \cdot \left(y2 \cdot y5\right)\right)\\ \mathbf{elif}\;y2 \leq 10^{-68}:\\ \;\;\;\;b \cdot \left(j \cdot \left(t \cdot y4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y2 -1.86e+53)
   (* a (* t (* y2 y5)))
   (if (<= y2 1e-68) (* b (* j (* t y4))) (* a (* y5 (* t y2))))))

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 (y2 <= -1.86e+53) {
		tmp = a * (t * (y2 * y5));
	} else if (y2 <= 1e-68) {
		tmp = b * (j * (t * y4));
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 (y2 <= (-1.86d+53)) then
        tmp = a * (t * (y2 * y5))
    else if (y2 <= 1d-68) then
        tmp = b * (j * (t * y4))
    else
        tmp = a * (y5 * (t * y2))
    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 (y2 <= -1.86e+53) {
		tmp = a * (t * (y2 * y5));
	} else if (y2 <= 1e-68) {
		tmp = b * (j * (t * y4));
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y2 <= -1.86e+53:
		tmp = a * (t * (y2 * y5))
	elif y2 <= 1e-68:
		tmp = b * (j * (t * y4))
	else:
		tmp = a * (y5 * (t * y2))
	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 (y2 <= -1.86e+53)
		tmp = Float64(a * Float64(t * Float64(y2 * y5)));
	elseif (y2 <= 1e-68)
		tmp = Float64(b * Float64(j * Float64(t * y4)));
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 (y2 <= -1.86e+53)
		tmp = a * (t * (y2 * y5));
	elseif (y2 <= 1e-68)
		tmp = b * (j * (t * y4));
	else
		tmp = a * (y5 * (t * y2));
	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[y2, -1.86e+53], N[(a * N[(t * N[(y2 * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y2, 1e-68], N[(b * N[(j * N[(t * y4), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y2 < -1.85999999999999999e53

   1. Initial program 28.2%

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

   3. Taylor expanded in y4 around inf 47.8%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 18.6%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 16.8%

      \[\leadsto \color{blue}{a \cdot \left(t \cdot \left(y2 \cdot y5\right)\right)} \]

   if -1.85999999999999999e53 < y2 < 1.00000000000000007e-68

   1. Initial program 37.7%

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

   3. Taylor expanded in y4 around inf 41.4%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 33.1%

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

   5. Taylor expanded in j around inf 24.0%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]

   if 1.00000000000000007e-68 < y2

   1. Initial program 26.2%

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

   3. Taylor expanded in y4 around inf 31.4%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 37.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 28.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 37: 19.5% accurate, 7.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y2 \leq 1.9 \cdot 10^{-62}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (if (<= y2 1.9e-62) (* b (* y4 (* t j))) (* a (* y5 (* t y2)))))

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 (y2 <= 1.9e-62) {
		tmp = b * (y4 * (t * j));
	} else {
		tmp = a * (y5 * (t * y2));
	}
	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 (y2 <= 1.9d-62) then
        tmp = b * (y4 * (t * j))
    else
        tmp = a * (y5 * (t * y2))
    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 (y2 <= 1.9e-62) {
		tmp = b * (y4 * (t * j));
	} else {
		tmp = a * (y5 * (t * y2));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k, y0, y1, y2, y3, y4, y5):
	tmp = 0
	if y2 <= 1.9e-62:
		tmp = b * (y4 * (t * j))
	else:
		tmp = a * (y5 * (t * y2))
	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 (y2 <= 1.9e-62)
		tmp = Float64(b * Float64(y4 * Float64(t * j)));
	else
		tmp = Float64(a * Float64(y5 * Float64(t * y2)));
	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 (y2 <= 1.9e-62)
		tmp = b * (y4 * (t * j));
	else
		tmp = a * (y5 * (t * y2));
	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[y2, 1.9e-62], N[(b * N[(y4 * N[(t * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(a * N[(y5 * N[(t * y2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y2 < 1.90000000000000003e-62

   1. Initial program 35.3%

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

   3. Taylor expanded in y4 around inf 43.0%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in b around inf 32.2%

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

   5. Taylor expanded in j around inf 19.9%

      \[\leadsto \color{blue}{b \cdot \left(j \cdot \left(t \cdot y4\right)\right)} \]
   6. Step-by-step derivation

      1. associate-*r* 21.0%

         \[\leadsto b \cdot \color{blue}{\left(\left(j \cdot t\right) \cdot y4\right)} \]

   7. Simplified 21.0%

      \[\leadsto \color{blue}{b \cdot \left(\left(j \cdot t\right) \cdot y4\right)} \]

   if 1.90000000000000003e-62 < y2

   1. Initial program 26.2%

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

   3. Taylor expanded in y4 around inf 31.4%

      \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

   4. Taylor expanded in a around inf 37.3%

      \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

   5. Taylor expanded in t around inf 28.3%

      \[\leadsto a \cdot \left(y5 \cdot \color{blue}{\left(t \cdot y2\right)}\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 23.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y2 \leq 1.9 \cdot 10^{-62}:\\ \;\;\;\;b \cdot \left(y4 \cdot \left(t \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(y5 \cdot \left(t \cdot y2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 38: 17.2% accurate, 13.6× speedup

Math

\[\begin{array}{l} \\ a \cdot \left(t \cdot \left(y2 \cdot y5\right)\right) \end{array} \]

FPCore

(FPCore (x y z t a b c i j k y0 y1 y2 y3 y4 y5)
 :precision binary64
 (* 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) {
	return a * (t * (y2 * 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 = a * (t * (y2 * 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 a * (t * (y2 * y5));
}

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_, y0_, y1_, y2_, y3_, y4_, y5_] := N[(a * N[(t * N[(y2 * y5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 32.6%

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

3. Taylor expanded in y4 around inf 39.5%

   \[\leadsto \left(\color{blue}{b \cdot \left(y4 \cdot \left(j \cdot t - k \cdot y\right)\right)} - \left(t \cdot y2 - y \cdot y3\right) \cdot \left(y4 \cdot c - y5 \cdot a\right)\right) + \left(k \cdot y2 - j \cdot y3\right) \cdot \left(y4 \cdot y1 - y5 \cdot y0\right) \]

4. Taylor expanded in a around inf 23.6%

   \[\leadsto \color{blue}{a \cdot \left(y5 \cdot \left(t \cdot y2 - y \cdot y3\right)\right)} \]

5. Taylor expanded in t around inf 13.7%

   \[\leadsto \color{blue}{a \cdot \left(t \cdot \left(y2 \cdot y5\right)\right)} \]
6. Add Preprocessing

Developer Target 1: 28.2% 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 2024116 
(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)))))