Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, E

Percentage Accurate: 85.2% → 92.3%

Time: 32.6s

Alternatives: 22

Speedup: 0.9×

• 51.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \end{array} \]

FPCore

(FPCore (x y z t a b c i j k)
 :precision binary64
 (-
  (-
   (+ (- (* (* (* (* x 18.0) y) z) t) (* (* a 4.0) t)) (* b c))
   (* (* x 4.0) i))
  (* (* j 27.0) k)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	return (((((((x * 18.0) * y) * z) * t) - ((a * 4.0) * t)) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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
    code = (((((((x * 18.0d0) * y) * z) * t) - ((a * 4.0d0) * t)) + (b * c)) - ((x * 4.0d0) * i)) - ((j * 27.0d0) * k)
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) {
	return (((((((x * 18.0) * y) * z) * t) - ((a * 4.0) * t)) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
}

Python

def code(x, y, z, t, a, b, c, i, j, k):
	return (((((((x * 18.0) * y) * z) * t) - ((a * 4.0) * t)) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k)

Julia

function code(x, y, z, t, a, b, c, i, j, k)
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 18.0) * y) * z) * t) - Float64(Float64(a * 4.0) * t)) + Float64(b * c)) - Float64(Float64(x * 4.0) * i)) - Float64(Float64(j * 27.0) * k))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k)
	tmp = (((((((x * 18.0) * y) * z) * t) - ((a * 4.0) * t)) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := N[(N[(N[(N[(N[(N[(N[(N[(x * 18.0), $MachinePrecision] * y), $MachinePrecision] * z), $MachinePrecision] * t), $MachinePrecision] - N[(N[(a * 4.0), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision] + N[(b * c), $MachinePrecision]), $MachinePrecision] - N[(N[(x * 4.0), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision] - N[(N[(j * 27.0), $MachinePrecision] * k), $MachinePrecision]), $MachinePrecision]

Initial Program: 85.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \end{array} \]

FPCore

(FPCore (x y z t a b c i j k)
 :precision binary64
 (-
  (-
   (+ (- (* (* (* (* x 18.0) y) z) t) (* (* a 4.0) t)) (* b c))
   (* (* x 4.0) i))
  (* (* j 27.0) k)))

C

double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	return (((((((x * 18.0) * y) * z) * t) - ((a * 4.0) * t)) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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
    code = (((((((x * 18.0d0) * y) * z) * t) - ((a * 4.0d0) * t)) + (b * c)) - ((x * 4.0d0) * i)) - ((j * 27.0d0) * k)
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) {
	return (((((((x * 18.0) * y) * z) * t) - ((a * 4.0) * t)) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
}

Python

def code(x, y, z, t, a, b, c, i, j, k):
	return (((((((x * 18.0) * y) * z) * t) - ((a * 4.0) * t)) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k)

Julia

function code(x, y, z, t, a, b, c, i, j, k)
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 18.0) * y) * z) * t) - Float64(Float64(a * 4.0) * t)) + Float64(b * c)) - Float64(Float64(x * 4.0) * i)) - Float64(Float64(j * 27.0) * k))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c, i, j, k)
	tmp = (((((((x * 18.0) * y) * z) * t) - ((a * 4.0) * t)) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := N[(N[(N[(N[(N[(N[(N[(N[(x * 18.0), $MachinePrecision] * y), $MachinePrecision] * z), $MachinePrecision] * t), $MachinePrecision] - N[(N[(a * 4.0), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision] + N[(b * c), $MachinePrecision]), $MachinePrecision] - N[(N[(x * 4.0), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision] - N[(N[(j * 27.0), $MachinePrecision] * k), $MachinePrecision]), $MachinePrecision]

Alternative 1: 92.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := \left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - t \cdot \left(a \cdot 4\right)\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right)\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1
         (-
          (-
           (+ (- (* (* (* (* x 18.0) y) z) t) (* t (* a 4.0))) (* b c))
           (* (* x 4.0) i))
          (* (* j 27.0) k))))
   (if (<= t_1 (- INFINITY))
     (-
      (+ (* b c) (* t (- (* (* x 18.0) (* y z)) (* a 4.0))))
      (+ (* x (* 4.0 i)) (* j (* 27.0 k))))
     (if (<= t_1 INFINITY) t_1 (* x (- (* 18.0 (* t (* y z))) (* 4.0 i)))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (((((((x * 18.0) * y) * z) * t) - (t * (a * 4.0))) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = ((b * c) + (t * (((x * 18.0) * (y * z)) - (a * 4.0)))) - ((x * (4.0 * i)) + (j * (27.0 * k)));
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	}
	return tmp;
}

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (((((((x * 18.0) * y) * z) * t) - (t * (a * 4.0))) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = ((b * c) + (t * (((x * 18.0) * (y * z)) - (a * 4.0)))) - ((x * (4.0 * i)) + (j * (27.0 * k)));
	} else if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (((((((x * 18.0) * y) * z) * t) - (t * (a * 4.0))) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k)
	tmp = 0
	if t_1 <= -math.inf:
		tmp = ((b * c) + (t * (((x * 18.0) * (y * z)) - (a * 4.0)))) - ((x * (4.0 * i)) + (j * (27.0 * k)))
	elif t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i))
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 18.0) * y) * z) * t) - Float64(t * Float64(a * 4.0))) + Float64(b * c)) - Float64(Float64(x * 4.0) * i)) - Float64(Float64(j * 27.0) * k))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(Float64(b * c) + Float64(t * Float64(Float64(Float64(x * 18.0) * Float64(y * z)) - Float64(a * 4.0)))) - Float64(Float64(x * Float64(4.0 * i)) + Float64(j * Float64(27.0 * k))));
	elseif (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(x * Float64(Float64(18.0 * Float64(t * Float64(y * z))) - Float64(4.0 * i)));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (((((((x * 18.0) * y) * z) * t) - (t * (a * 4.0))) + (b * c)) - ((x * 4.0) * i)) - ((j * 27.0) * k);
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = ((b * c) + (t * (((x * 18.0) * (y * z)) - (a * 4.0)))) - ((x * (4.0 * i)) + (j * (27.0 * k)));
	elseif (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(N[(N[(N[(N[(N[(N[(x * 18.0), $MachinePrecision] * y), $MachinePrecision] * z), $MachinePrecision] * t), $MachinePrecision] - N[(t * N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(b * c), $MachinePrecision]), $MachinePrecision] - N[(N[(x * 4.0), $MachinePrecision] * i), $MachinePrecision]), $MachinePrecision] - N[(N[(j * 27.0), $MachinePrecision] * k), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(N[(x * 18.0), $MachinePrecision] * N[(y * z), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(x * N[(4.0 * i), $MachinePrecision]), $MachinePrecision] + N[(j * N[(27.0 * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, Infinity], t$95$1, N[(x * N[(N[(18.0 * N[(t * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (-.f64 (+.f64 (-.f64 (*.f64 (*.f64 (*.f64 (*.f64 x 18) y) z) t) (*.f64 (*.f64 a 4) t)) (*.f64 b c)) (*.f64 (*.f64 x 4) i)) (*.f64 (*.f64 j 27) k)) < -inf.0

   1. Initial program 91.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 98.3%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   if -inf.0 < (-.f64 (-.f64 (+.f64 (-.f64 (*.f64 (*.f64 (*.f64 (*.f64 x 18) y) z) t) (*.f64 (*.f64 a 4) t)) (*.f64 b c)) (*.f64 (*.f64 x 4) i)) (*.f64 (*.f64 j 27) k)) < +inf.0

   1. Initial program 98.8%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   if +inf.0 < (-.f64 (-.f64 (+.f64 (-.f64 (*.f64 (*.f64 (*.f64 (*.f64 x 18) y) z) t) (*.f64 (*.f64 a 4) t)) (*.f64 b c)) (*.f64 (*.f64 x 4) i)) (*.f64 (*.f64 j 27) k))

   1. Initial program 0.0%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 25.8%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 64.7%

      \[\leadsto \color{blue}{x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 94.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - t \cdot \left(a \cdot 4\right)\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \leq -\infty:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right)\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\\ \mathbf{elif}\;\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - t \cdot \left(a \cdot 4\right)\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \leq \infty:\\ \;\;\;\;\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - t \cdot \left(a \cdot 4\right)\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 35.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} \mathbf{if}\;b \cdot c \leq -1.05 \cdot 10^{+186}:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;b \cdot c \leq -2.3 \cdot 10^{+14}:\\ \;\;\;\;x \cdot \left(i \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq -55000000000000:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;b \cdot c \leq 2.9 \cdot 10^{-212}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 1.18 \cdot 10^{+157}:\\ \;\;\;\;18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot c\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (if (<= (* b c) -1.05e+186)
   (* b c)
   (if (<= (* b c) -2.3e+14)
     (* x (* i -4.0))
     (if (<= (* b c) -55000000000000.0)
       (* b c)
       (if (<= (* b c) 2.9e-212)
         (* a (* t -4.0))
         (if (<= (* b c) 1.18e+157) (* 18.0 (* t (* x (* y z)))) (* b c)))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double tmp;
	if ((b * c) <= -1.05e+186) {
		tmp = b * c;
	} else if ((b * c) <= -2.3e+14) {
		tmp = x * (i * -4.0);
	} else if ((b * c) <= -55000000000000.0) {
		tmp = b * c;
	} else if ((b * c) <= 2.9e-212) {
		tmp = a * (t * -4.0);
	} else if ((b * c) <= 1.18e+157) {
		tmp = 18.0 * (t * (x * (y * z)));
	} else {
		tmp = b * c;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: tmp
    if ((b * c) <= (-1.05d+186)) then
        tmp = b * c
    else if ((b * c) <= (-2.3d+14)) then
        tmp = x * (i * (-4.0d0))
    else if ((b * c) <= (-55000000000000.0d0)) then
        tmp = b * c
    else if ((b * c) <= 2.9d-212) then
        tmp = a * (t * (-4.0d0))
    else if ((b * c) <= 1.18d+157) then
        tmp = 18.0d0 * (t * (x * (y * z)))
    else
        tmp = b * c
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 tmp;
	if ((b * c) <= -1.05e+186) {
		tmp = b * c;
	} else if ((b * c) <= -2.3e+14) {
		tmp = x * (i * -4.0);
	} else if ((b * c) <= -55000000000000.0) {
		tmp = b * c;
	} else if ((b * c) <= 2.9e-212) {
		tmp = a * (t * -4.0);
	} else if ((b * c) <= 1.18e+157) {
		tmp = 18.0 * (t * (x * (y * z)));
	} else {
		tmp = b * c;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	tmp = 0
	if (b * c) <= -1.05e+186:
		tmp = b * c
	elif (b * c) <= -2.3e+14:
		tmp = x * (i * -4.0)
	elif (b * c) <= -55000000000000.0:
		tmp = b * c
	elif (b * c) <= 2.9e-212:
		tmp = a * (t * -4.0)
	elif (b * c) <= 1.18e+157:
		tmp = 18.0 * (t * (x * (y * z)))
	else:
		tmp = b * c
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0
	if (Float64(b * c) <= -1.05e+186)
		tmp = Float64(b * c);
	elseif (Float64(b * c) <= -2.3e+14)
		tmp = Float64(x * Float64(i * -4.0));
	elseif (Float64(b * c) <= -55000000000000.0)
		tmp = Float64(b * c);
	elseif (Float64(b * c) <= 2.9e-212)
		tmp = Float64(a * Float64(t * -4.0));
	elseif (Float64(b * c) <= 1.18e+157)
		tmp = Float64(18.0 * Float64(t * Float64(x * Float64(y * z))));
	else
		tmp = Float64(b * c);
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0;
	if ((b * c) <= -1.05e+186)
		tmp = b * c;
	elseif ((b * c) <= -2.3e+14)
		tmp = x * (i * -4.0);
	elseif ((b * c) <= -55000000000000.0)
		tmp = b * c;
	elseif ((b * c) <= 2.9e-212)
		tmp = a * (t * -4.0);
	elseif ((b * c) <= 1.18e+157)
		tmp = 18.0 * (t * (x * (y * z)));
	else
		tmp = b * c;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := If[LessEqual[N[(b * c), $MachinePrecision], -1.05e+186], N[(b * c), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], -2.3e+14], N[(x * N[(i * -4.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], -55000000000000.0], N[(b * c), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], 2.9e-212], N[(a * N[(t * -4.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], 1.18e+157], N[(18.0 * N[(t * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(b * c), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 b c) < -1.05e186 or -2.3e14 < (*.f64 b c) < -5.5e13 or 1.1799999999999999e157 < (*.f64 b c)

   1. Initial program 86.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 86.2%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 86.2%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 86.2%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 87.4%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 87.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 87.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 87.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 87.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 87.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 87.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in b around inf 66.9%

      \[\leadsto \color{blue}{b \cdot c} \]

   if -1.05e186 < (*.f64 b c) < -2.3e14

   1. Initial program 83.0%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 86.4%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 86.4%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 86.4%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 86.4%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 86.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 86.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around inf 40.4%

      \[\leadsto \color{blue}{-4 \cdot \left(i \cdot x\right)} \]
   8. Step-by-step derivation

      1. associate-*r* 40.4%

         \[\leadsto \color{blue}{\left(-4 \cdot i\right) \cdot x} \]

      2. *-commutative 40.4%

         \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} \]

   9. Simplified 40.4%

      \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} \]

   if -5.5e13 < (*.f64 b c) < 2.8999999999999999e-212

   1. Initial program 86.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 91.8%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 91.8%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 91.8%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 91.8%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 91.8%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 91.8%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 91.8%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 91.8%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 91.8%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 91.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 77.0%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. pow1 77.0%

         \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

      2. associate-*r* 76.9%

         \[\leadsto \left(b \cdot c + t \cdot \left({\color{blue}{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}}^{1} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   9. Applied egg-rr 76.9%

      \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
   10. Step-by-step derivation

       1. unpow1 76.9%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(18 \cdot x\right) \cdot \left(y \cdot z\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       2. associate-*r* 77.0%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{18 \cdot \left(x \cdot \left(y \cdot z\right)\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       3. *-commutative 77.0%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot 18} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       4. associate-*r* 74.8%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18 - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       5. associate-*l* 74.8%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   11. Simplified 74.8%

       \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   12. Taylor expanded in a around inf 38.5%

       \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} \]
   13. Step-by-step derivation

       1. associate-*r* 38.5%

          \[\leadsto \color{blue}{\left(-4 \cdot a\right) \cdot t} \]

       2. *-commutative 38.5%

          \[\leadsto \color{blue}{\left(a \cdot -4\right)} \cdot t \]

       3. associate-*l* 38.5%

          \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   14. Simplified 38.5%

       \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   if 2.8999999999999999e-212 < (*.f64 b c) < 1.1799999999999999e157

   1. Initial program 84.2%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 81.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 81.0%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 81.0%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 84.2%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 84.2%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 84.2%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 85.7%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 85.7%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 85.7%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 85.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in y around inf 42.6%

      \[\leadsto \color{blue}{18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 48.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \cdot c \leq -1.05 \cdot 10^{+186}:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;b \cdot c \leq -2.3 \cdot 10^{+14}:\\ \;\;\;\;x \cdot \left(i \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq -55000000000000:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;b \cdot c \leq 2.9 \cdot 10^{-212}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 1.18 \cdot 10^{+157}:\\ \;\;\;\;18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot c\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 35.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} \mathbf{if}\;b \cdot c \leq -2.2 \cdot 10^{+186}:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;b \cdot c \leq -2.3 \cdot 10^{+14}:\\ \;\;\;\;x \cdot \left(i \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq -38000000000000:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;b \cdot c \leq 8.2 \cdot 10^{-209}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 5 \cdot 10^{+161}:\\ \;\;\;\;\left(y \cdot \left(x \cdot z\right)\right) \cdot \left(18 \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot c\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (if (<= (* b c) -2.2e+186)
   (* b c)
   (if (<= (* b c) -2.3e+14)
     (* x (* i -4.0))
     (if (<= (* b c) -38000000000000.0)
       (* b c)
       (if (<= (* b c) 8.2e-209)
         (* a (* t -4.0))
         (if (<= (* b c) 5e+161) (* (* y (* x z)) (* 18.0 t)) (* b c)))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double tmp;
	if ((b * c) <= -2.2e+186) {
		tmp = b * c;
	} else if ((b * c) <= -2.3e+14) {
		tmp = x * (i * -4.0);
	} else if ((b * c) <= -38000000000000.0) {
		tmp = b * c;
	} else if ((b * c) <= 8.2e-209) {
		tmp = a * (t * -4.0);
	} else if ((b * c) <= 5e+161) {
		tmp = (y * (x * z)) * (18.0 * t);
	} else {
		tmp = b * c;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: tmp
    if ((b * c) <= (-2.2d+186)) then
        tmp = b * c
    else if ((b * c) <= (-2.3d+14)) then
        tmp = x * (i * (-4.0d0))
    else if ((b * c) <= (-38000000000000.0d0)) then
        tmp = b * c
    else if ((b * c) <= 8.2d-209) then
        tmp = a * (t * (-4.0d0))
    else if ((b * c) <= 5d+161) then
        tmp = (y * (x * z)) * (18.0d0 * t)
    else
        tmp = b * c
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 tmp;
	if ((b * c) <= -2.2e+186) {
		tmp = b * c;
	} else if ((b * c) <= -2.3e+14) {
		tmp = x * (i * -4.0);
	} else if ((b * c) <= -38000000000000.0) {
		tmp = b * c;
	} else if ((b * c) <= 8.2e-209) {
		tmp = a * (t * -4.0);
	} else if ((b * c) <= 5e+161) {
		tmp = (y * (x * z)) * (18.0 * t);
	} else {
		tmp = b * c;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	tmp = 0
	if (b * c) <= -2.2e+186:
		tmp = b * c
	elif (b * c) <= -2.3e+14:
		tmp = x * (i * -4.0)
	elif (b * c) <= -38000000000000.0:
		tmp = b * c
	elif (b * c) <= 8.2e-209:
		tmp = a * (t * -4.0)
	elif (b * c) <= 5e+161:
		tmp = (y * (x * z)) * (18.0 * t)
	else:
		tmp = b * c
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0
	if (Float64(b * c) <= -2.2e+186)
		tmp = Float64(b * c);
	elseif (Float64(b * c) <= -2.3e+14)
		tmp = Float64(x * Float64(i * -4.0));
	elseif (Float64(b * c) <= -38000000000000.0)
		tmp = Float64(b * c);
	elseif (Float64(b * c) <= 8.2e-209)
		tmp = Float64(a * Float64(t * -4.0));
	elseif (Float64(b * c) <= 5e+161)
		tmp = Float64(Float64(y * Float64(x * z)) * Float64(18.0 * t));
	else
		tmp = Float64(b * c);
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0;
	if ((b * c) <= -2.2e+186)
		tmp = b * c;
	elseif ((b * c) <= -2.3e+14)
		tmp = x * (i * -4.0);
	elseif ((b * c) <= -38000000000000.0)
		tmp = b * c;
	elseif ((b * c) <= 8.2e-209)
		tmp = a * (t * -4.0);
	elseif ((b * c) <= 5e+161)
		tmp = (y * (x * z)) * (18.0 * t);
	else
		tmp = b * c;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := If[LessEqual[N[(b * c), $MachinePrecision], -2.2e+186], N[(b * c), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], -2.3e+14], N[(x * N[(i * -4.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], -38000000000000.0], N[(b * c), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], 8.2e-209], N[(a * N[(t * -4.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], 5e+161], N[(N[(y * N[(x * z), $MachinePrecision]), $MachinePrecision] * N[(18.0 * t), $MachinePrecision]), $MachinePrecision], N[(b * c), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 b c) < -2.1999999999999998e186 or -2.3e14 < (*.f64 b c) < -3.8e13 or 4.9999999999999997e161 < (*.f64 b c)

   1. Initial program 86.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 86.2%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 86.2%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 86.2%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 87.4%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 87.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 87.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 87.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 87.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 87.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 87.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in b around inf 66.9%

      \[\leadsto \color{blue}{b \cdot c} \]

   if -2.1999999999999998e186 < (*.f64 b c) < -2.3e14

   1. Initial program 83.0%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 86.4%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 86.4%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 86.4%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 86.4%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 86.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 86.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around inf 40.4%

      \[\leadsto \color{blue}{-4 \cdot \left(i \cdot x\right)} \]
   8. Step-by-step derivation

      1. associate-*r* 40.4%

         \[\leadsto \color{blue}{\left(-4 \cdot i\right) \cdot x} \]

      2. *-commutative 40.4%

         \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} \]

   9. Simplified 40.4%

      \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} \]

   if -3.8e13 < (*.f64 b c) < 8.19999999999999955e-209

   1. Initial program 86.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 91.8%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 91.8%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 91.8%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 91.8%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 91.8%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 91.8%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 91.8%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 91.8%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 91.8%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 91.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 77.0%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. pow1 77.0%

         \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

      2. associate-*r* 76.9%

         \[\leadsto \left(b \cdot c + t \cdot \left({\color{blue}{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}}^{1} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   9. Applied egg-rr 76.9%

      \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
   10. Step-by-step derivation

       1. unpow1 76.9%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(18 \cdot x\right) \cdot \left(y \cdot z\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       2. associate-*r* 77.0%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{18 \cdot \left(x \cdot \left(y \cdot z\right)\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       3. *-commutative 77.0%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot 18} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       4. associate-*r* 74.8%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18 - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       5. associate-*l* 74.8%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   11. Simplified 74.8%

       \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   12. Taylor expanded in a around inf 38.5%

       \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} \]
   13. Step-by-step derivation

       1. associate-*r* 38.5%

          \[\leadsto \color{blue}{\left(-4 \cdot a\right) \cdot t} \]

       2. *-commutative 38.5%

          \[\leadsto \color{blue}{\left(a \cdot -4\right)} \cdot t \]

       3. associate-*l* 38.5%

          \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   14. Simplified 38.5%

       \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   if 8.19999999999999955e-209 < (*.f64 b c) < 4.9999999999999997e161

   1. Initial program 84.2%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 81.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 81.0%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 81.0%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 84.2%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 84.2%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 84.2%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 85.7%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 85.7%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 85.7%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 85.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 75.2%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. pow1 75.2%

         \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

      2. associate-*r* 75.2%

         \[\leadsto \left(b \cdot c + t \cdot \left({\color{blue}{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}}^{1} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   9. Applied egg-rr 75.2%

      \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
   10. Step-by-step derivation

       1. unpow1 75.2%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(18 \cdot x\right) \cdot \left(y \cdot z\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       2. associate-*r* 75.2%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{18 \cdot \left(x \cdot \left(y \cdot z\right)\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       3. *-commutative 75.2%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot 18} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       4. associate-*r* 81.5%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18 - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       5. associate-*l* 81.5%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   11. Simplified 81.5%

       \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   12. Taylor expanded in x around inf 42.6%

       \[\leadsto \color{blue}{18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)} \]
   13. Step-by-step derivation

       1. associate-*r* 42.6%

          \[\leadsto \color{blue}{\left(18 \cdot t\right) \cdot \left(x \cdot \left(y \cdot z\right)\right)} \]

       2. *-commutative 42.6%

          \[\leadsto \color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot \left(18 \cdot t\right)} \]

       3. *-commutative 42.6%

          \[\leadsto \color{blue}{\left(\left(y \cdot z\right) \cdot x\right)} \cdot \left(18 \cdot t\right) \]

       4. associate-*l* 39.7%

          \[\leadsto \color{blue}{\left(y \cdot \left(z \cdot x\right)\right)} \cdot \left(18 \cdot t\right) \]

   14. Simplified 39.7%

       \[\leadsto \color{blue}{\left(y \cdot \left(z \cdot x\right)\right) \cdot \left(18 \cdot t\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 47.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \cdot c \leq -2.2 \cdot 10^{+186}:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;b \cdot c \leq -2.3 \cdot 10^{+14}:\\ \;\;\;\;x \cdot \left(i \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq -38000000000000:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;b \cdot c \leq 8.2 \cdot 10^{-209}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 5 \cdot 10^{+161}:\\ \;\;\;\;\left(y \cdot \left(x \cdot z\right)\right) \cdot \left(18 \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot c\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 79.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := \left(j \cdot 27\right) \cdot k\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+240}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;t\_1 \leq 10^{+145}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\right) - 4 \cdot \left(x \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(t \cdot a\right)\right) - t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (* (* j 27.0) k)))
   (if (<= t_1 -2e+240)
     (* j (* k -27.0))
     (if (<= t_1 1e+145)
       (-
        (+ (* b c) (* t (- (* 18.0 (* x (* y z))) (* a 4.0))))
        (* 4.0 (* x i)))
       (- (- (* b c) (* 4.0 (* t a))) t_1)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (j * 27.0) * k;
	double tmp;
	if (t_1 <= -2e+240) {
		tmp = j * (k * -27.0);
	} else if (t_1 <= 1e+145) {
		tmp = ((b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))) - (4.0 * (x * i));
	} else {
		tmp = ((b * c) - (4.0 * (t * a))) - t_1;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: tmp
    t_1 = (j * 27.0d0) * k
    if (t_1 <= (-2d+240)) then
        tmp = j * (k * (-27.0d0))
    else if (t_1 <= 1d+145) then
        tmp = ((b * c) + (t * ((18.0d0 * (x * (y * z))) - (a * 4.0d0)))) - (4.0d0 * (x * i))
    else
        tmp = ((b * c) - (4.0d0 * (t * a))) - t_1
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (j * 27.0) * k;
	double tmp;
	if (t_1 <= -2e+240) {
		tmp = j * (k * -27.0);
	} else if (t_1 <= 1e+145) {
		tmp = ((b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))) - (4.0 * (x * i));
	} else {
		tmp = ((b * c) - (4.0 * (t * a))) - t_1;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (j * 27.0) * k
	tmp = 0
	if t_1 <= -2e+240:
		tmp = j * (k * -27.0)
	elif t_1 <= 1e+145:
		tmp = ((b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))) - (4.0 * (x * i))
	else:
		tmp = ((b * c) - (4.0 * (t * a))) - t_1
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(j * 27.0) * k)
	tmp = 0.0
	if (t_1 <= -2e+240)
		tmp = Float64(j * Float64(k * -27.0));
	elseif (t_1 <= 1e+145)
		tmp = Float64(Float64(Float64(b * c) + Float64(t * Float64(Float64(18.0 * Float64(x * Float64(y * z))) - Float64(a * 4.0)))) - Float64(4.0 * Float64(x * i)));
	else
		tmp = Float64(Float64(Float64(b * c) - Float64(4.0 * Float64(t * a))) - t_1);
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (j * 27.0) * k;
	tmp = 0.0;
	if (t_1 <= -2e+240)
		tmp = j * (k * -27.0);
	elseif (t_1 <= 1e+145)
		tmp = ((b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))) - (4.0 * (x * i));
	else
		tmp = ((b * c) - (4.0 * (t * a))) - t_1;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(j * 27.0), $MachinePrecision] * k), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+240], N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 1e+145], N[(N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(18.0 * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(x * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 j 27) k) < -2.00000000000000003e240

   1. Initial program 63.6%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 63.5%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 63.5%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 63.5%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 68.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 68.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 68.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 72.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 72.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 72.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 72.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in j around inf 73.5%

      \[\leadsto \color{blue}{-27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. *-commutative 73.5%

         \[\leadsto \color{blue}{\left(j \cdot k\right) \cdot -27} \]

      2. associate-*r* 73.4%

         \[\leadsto \color{blue}{j \cdot \left(k \cdot -27\right)} \]

   9. Simplified 73.4%

      \[\leadsto \color{blue}{j \cdot \left(k \cdot -27\right)} \]

   if -2.00000000000000003e240 < (*.f64 (*.f64 j 27) k) < 9.9999999999999999e144

   1. Initial program 87.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 90.4%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in j around 0 86.3%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 4 \cdot \left(i \cdot x\right)} \]

   if 9.9999999999999999e144 < (*.f64 (*.f64 j 27) k)

   1. Initial program 84.2%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 81.5%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]
3. Recombined 3 regimes into one program.

4. Final simplification 84.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(j \cdot 27\right) \cdot k \leq -2 \cdot 10^{+240}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;\left(j \cdot 27\right) \cdot k \leq 10^{+145}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\right) - 4 \cdot \left(x \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(t \cdot a\right)\right) - \left(j \cdot 27\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 76.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := \left(j \cdot 27\right) \cdot k\\ t_2 := t \cdot \left(-18 \cdot \left(x \cdot \left(-y \cdot z\right)\right) - a \cdot 4\right) - t\_1\\ t_3 := b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \mathbf{if}\;t \leq -5.2 \cdot 10^{+155}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t \leq -8.8 \cdot 10^{+95}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t \leq -2.55 \cdot 10^{-64}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t \leq 2.5 \cdot 10^{+17}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(x \cdot i\right)\right) - t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (* (* j 27.0) k))
        (t_2 (- (* t (- (* -18.0 (* x (- (* y z)))) (* a 4.0))) t_1))
        (t_3 (+ (* b c) (* t (- (* 18.0 (* x (* y z))) (* a 4.0))))))
   (if (<= t -5.2e+155)
     t_3
     (if (<= t -8.8e+95)
       t_2
       (if (<= t -2.55e-64)
         t_3
         (if (<= t 2.5e+17) (- (- (* b c) (* 4.0 (* x i))) t_1) t_2))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (j * 27.0) * k;
	double t_2 = (t * ((-18.0 * (x * -(y * z))) - (a * 4.0))) - t_1;
	double t_3 = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	double tmp;
	if (t <= -5.2e+155) {
		tmp = t_3;
	} else if (t <= -8.8e+95) {
		tmp = t_2;
	} else if (t <= -2.55e-64) {
		tmp = t_3;
	} else if (t <= 2.5e+17) {
		tmp = ((b * c) - (4.0 * (x * i))) - t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = (j * 27.0d0) * k
    t_2 = (t * (((-18.0d0) * (x * -(y * z))) - (a * 4.0d0))) - t_1
    t_3 = (b * c) + (t * ((18.0d0 * (x * (y * z))) - (a * 4.0d0)))
    if (t <= (-5.2d+155)) then
        tmp = t_3
    else if (t <= (-8.8d+95)) then
        tmp = t_2
    else if (t <= (-2.55d-64)) then
        tmp = t_3
    else if (t <= 2.5d+17) then
        tmp = ((b * c) - (4.0d0 * (x * i))) - t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (j * 27.0) * k;
	double t_2 = (t * ((-18.0 * (x * -(y * z))) - (a * 4.0))) - t_1;
	double t_3 = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	double tmp;
	if (t <= -5.2e+155) {
		tmp = t_3;
	} else if (t <= -8.8e+95) {
		tmp = t_2;
	} else if (t <= -2.55e-64) {
		tmp = t_3;
	} else if (t <= 2.5e+17) {
		tmp = ((b * c) - (4.0 * (x * i))) - t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (j * 27.0) * k
	t_2 = (t * ((-18.0 * (x * -(y * z))) - (a * 4.0))) - t_1
	t_3 = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))
	tmp = 0
	if t <= -5.2e+155:
		tmp = t_3
	elif t <= -8.8e+95:
		tmp = t_2
	elif t <= -2.55e-64:
		tmp = t_3
	elif t <= 2.5e+17:
		tmp = ((b * c) - (4.0 * (x * i))) - t_1
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(j * 27.0) * k)
	t_2 = Float64(Float64(t * Float64(Float64(-18.0 * Float64(x * Float64(-Float64(y * z)))) - Float64(a * 4.0))) - t_1)
	t_3 = Float64(Float64(b * c) + Float64(t * Float64(Float64(18.0 * Float64(x * Float64(y * z))) - Float64(a * 4.0))))
	tmp = 0.0
	if (t <= -5.2e+155)
		tmp = t_3;
	elseif (t <= -8.8e+95)
		tmp = t_2;
	elseif (t <= -2.55e-64)
		tmp = t_3;
	elseif (t <= 2.5e+17)
		tmp = Float64(Float64(Float64(b * c) - Float64(4.0 * Float64(x * i))) - t_1);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (j * 27.0) * k;
	t_2 = (t * ((-18.0 * (x * -(y * z))) - (a * 4.0))) - t_1;
	t_3 = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	tmp = 0.0;
	if (t <= -5.2e+155)
		tmp = t_3;
	elseif (t <= -8.8e+95)
		tmp = t_2;
	elseif (t <= -2.55e-64)
		tmp = t_3;
	elseif (t <= 2.5e+17)
		tmp = ((b * c) - (4.0 * (x * i))) - t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(j * 27.0), $MachinePrecision] * k), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * N[(N[(-18.0 * N[(x * (-N[(y * z), $MachinePrecision])), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(18.0 * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.2e+155], t$95$3, If[LessEqual[t, -8.8e+95], t$95$2, If[LessEqual[t, -2.55e-64], t$95$3, If[LessEqual[t, 2.5e+17], N[(N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(x * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], t$95$2]]]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -5.2000000000000004e155 or -8.7999999999999996e95 < t < -2.54999999999999992e-64

   1. Initial program 86.6%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 88.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 88.0%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 88.0%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 89.4%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 89.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 89.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 89.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 89.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 89.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 89.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 84.2%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]

   8. Taylor expanded in j around 0 79.6%

      \[\leadsto \color{blue}{b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)} \]

   if -5.2000000000000004e155 < t < -8.7999999999999996e95 or 2.5e17 < t

   1. Initial program 81.6%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in t around -inf 81.2%

      \[\leadsto \color{blue}{-1 \cdot \left(t \cdot \left(-18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - -4 \cdot a\right)\right)} - \left(j \cdot 27\right) \cdot k \]
   4. Step-by-step derivation

      1. associate-*r* 81.2%

         \[\leadsto \color{blue}{\left(-1 \cdot t\right) \cdot \left(-18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - -4 \cdot a\right)} - \left(j \cdot 27\right) \cdot k \]

      2. neg-mul-1 81.2%

         \[\leadsto \color{blue}{\left(-t\right)} \cdot \left(-18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - -4 \cdot a\right) - \left(j \cdot 27\right) \cdot k \]

      3. cancel-sign-sub-inv 81.2%

         \[\leadsto \left(-t\right) \cdot \color{blue}{\left(-18 \cdot \left(x \cdot \left(y \cdot z\right)\right) + \left(--4\right) \cdot a\right)} - \left(j \cdot 27\right) \cdot k \]

      4. metadata-eval 81.2%

         \[\leadsto \left(-t\right) \cdot \left(-18 \cdot \left(x \cdot \left(y \cdot z\right)\right) + \color{blue}{4} \cdot a\right) - \left(j \cdot 27\right) \cdot k \]

      5. *-commutative 81.2%

         \[\leadsto \left(-t\right) \cdot \left(-18 \cdot \left(x \cdot \left(y \cdot z\right)\right) + \color{blue}{a \cdot 4}\right) - \left(j \cdot 27\right) \cdot k \]

      6. *-commutative 81.2%

         \[\leadsto \left(-t\right) \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot -18} + a \cdot 4\right) - \left(j \cdot 27\right) \cdot k \]

   5. Simplified 81.2%

      \[\leadsto \color{blue}{\left(-t\right) \cdot \left(\left(x \cdot \left(y \cdot z\right)\right) \cdot -18 + a \cdot 4\right)} - \left(j \cdot 27\right) \cdot k \]

   if -2.54999999999999992e-64 < t < 2.5e17

   1. Initial program 87.0%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 81.1%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(i \cdot x\right)\right)} - \left(j \cdot 27\right) \cdot k \]
3. Recombined 3 regimes into one program.

4. Final simplification 80.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -5.2 \cdot 10^{+155}:\\ \;\;\;\;b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \mathbf{elif}\;t \leq -8.8 \cdot 10^{+95}:\\ \;\;\;\;t \cdot \left(-18 \cdot \left(x \cdot \left(-y \cdot z\right)\right) - a \cdot 4\right) - \left(j \cdot 27\right) \cdot k\\ \mathbf{elif}\;t \leq -2.55 \cdot 10^{-64}:\\ \;\;\;\;b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \mathbf{elif}\;t \leq 2.5 \cdot 10^{+17}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(x \cdot i\right)\right) - \left(j \cdot 27\right) \cdot k\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(-18 \cdot \left(x \cdot \left(-y \cdot z\right)\right) - a \cdot 4\right) - \left(j \cdot 27\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 52.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := b \cdot c - 4 \cdot \left(t \cdot a\right)\\ t_2 := j \cdot \left(k \cdot -27\right)\\ \mathbf{if}\;b \cdot c \leq -1.45 \cdot 10^{+72}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \cdot c \leq -55000000000000:\\ \;\;\;\;t\_2 + x \cdot \left(i \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 2.7 \cdot 10^{+51}:\\ \;\;\;\;t\_2 + t \cdot \left(a \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 6.8 \cdot 10^{+154}:\\ \;\;\;\;18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (- (* b c) (* 4.0 (* t a)))) (t_2 (* j (* k -27.0))))
   (if (<= (* b c) -1.45e+72)
     t_1
     (if (<= (* b c) -55000000000000.0)
       (+ t_2 (* x (* i -4.0)))
       (if (<= (* b c) 2.7e+51)
         (+ t_2 (* t (* a -4.0)))
         (if (<= (* b c) 6.8e+154) (* 18.0 (* t (* x (* y z)))) t_1))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (b * c) - (4.0 * (t * a));
	double t_2 = j * (k * -27.0);
	double tmp;
	if ((b * c) <= -1.45e+72) {
		tmp = t_1;
	} else if ((b * c) <= -55000000000000.0) {
		tmp = t_2 + (x * (i * -4.0));
	} else if ((b * c) <= 2.7e+51) {
		tmp = t_2 + (t * (a * -4.0));
	} else if ((b * c) <= 6.8e+154) {
		tmp = 18.0 * (t * (x * (y * z)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (b * c) - (4.0d0 * (t * a))
    t_2 = j * (k * (-27.0d0))
    if ((b * c) <= (-1.45d+72)) then
        tmp = t_1
    else if ((b * c) <= (-55000000000000.0d0)) then
        tmp = t_2 + (x * (i * (-4.0d0)))
    else if ((b * c) <= 2.7d+51) then
        tmp = t_2 + (t * (a * (-4.0d0)))
    else if ((b * c) <= 6.8d+154) then
        tmp = 18.0d0 * (t * (x * (y * z)))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (b * c) - (4.0 * (t * a));
	double t_2 = j * (k * -27.0);
	double tmp;
	if ((b * c) <= -1.45e+72) {
		tmp = t_1;
	} else if ((b * c) <= -55000000000000.0) {
		tmp = t_2 + (x * (i * -4.0));
	} else if ((b * c) <= 2.7e+51) {
		tmp = t_2 + (t * (a * -4.0));
	} else if ((b * c) <= 6.8e+154) {
		tmp = 18.0 * (t * (x * (y * z)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (b * c) - (4.0 * (t * a))
	t_2 = j * (k * -27.0)
	tmp = 0
	if (b * c) <= -1.45e+72:
		tmp = t_1
	elif (b * c) <= -55000000000000.0:
		tmp = t_2 + (x * (i * -4.0))
	elif (b * c) <= 2.7e+51:
		tmp = t_2 + (t * (a * -4.0))
	elif (b * c) <= 6.8e+154:
		tmp = 18.0 * (t * (x * (y * z)))
	else:
		tmp = t_1
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(b * c) - Float64(4.0 * Float64(t * a)))
	t_2 = Float64(j * Float64(k * -27.0))
	tmp = 0.0
	if (Float64(b * c) <= -1.45e+72)
		tmp = t_1;
	elseif (Float64(b * c) <= -55000000000000.0)
		tmp = Float64(t_2 + Float64(x * Float64(i * -4.0)));
	elseif (Float64(b * c) <= 2.7e+51)
		tmp = Float64(t_2 + Float64(t * Float64(a * -4.0)));
	elseif (Float64(b * c) <= 6.8e+154)
		tmp = Float64(18.0 * Float64(t * Float64(x * Float64(y * z))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (b * c) - (4.0 * (t * a));
	t_2 = j * (k * -27.0);
	tmp = 0.0;
	if ((b * c) <= -1.45e+72)
		tmp = t_1;
	elseif ((b * c) <= -55000000000000.0)
		tmp = t_2 + (x * (i * -4.0));
	elseif ((b * c) <= 2.7e+51)
		tmp = t_2 + (t * (a * -4.0));
	elseif ((b * c) <= 6.8e+154)
		tmp = 18.0 * (t * (x * (y * z)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(b * c), $MachinePrecision], -1.45e+72], t$95$1, If[LessEqual[N[(b * c), $MachinePrecision], -55000000000000.0], N[(t$95$2 + N[(x * N[(i * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], 2.7e+51], N[(t$95$2 + N[(t * N[(a * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], 6.8e+154], N[(18.0 * N[(t * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 b c) < -1.45000000000000009e72 or 6.79999999999999948e154 < (*.f64 b c)

   1. Initial program 85.7%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 76.7%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]

   4. Taylor expanded in j around 0 73.6%

      \[\leadsto \color{blue}{b \cdot c - 4 \cdot \left(a \cdot t\right)} \]

   if -1.45000000000000009e72 < (*.f64 b c) < -5.5e13

   1. Initial program 82.8%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 82.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in i around inf 60.6%

      \[\leadsto \color{blue}{-4 \cdot \left(i \cdot x\right)} + j \cdot \left(k \cdot -27\right) \]
   6. Step-by-step derivation

      1. associate-*r* 60.6%

         \[\leadsto \color{blue}{\left(-4 \cdot i\right) \cdot x} + j \cdot \left(k \cdot -27\right) \]

      2. *-commutative 60.6%

         \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} + j \cdot \left(k \cdot -27\right) \]

   7. Simplified 60.6%

      \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} + j \cdot \left(k \cdot -27\right) \]

   if -5.5e13 < (*.f64 b c) < 2.69999999999999992e51

   1. Initial program 86.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 89.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 55.8%

      \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} + j \cdot \left(k \cdot -27\right) \]
   6. Step-by-step derivation

      1. *-commutative 55.8%

         \[\leadsto \color{blue}{\left(a \cdot t\right) \cdot -4} + j \cdot \left(k \cdot -27\right) \]

      2. *-commutative 55.8%

         \[\leadsto \color{blue}{\left(t \cdot a\right)} \cdot -4 + j \cdot \left(k \cdot -27\right) \]

      3. associate-*r* 55.8%

         \[\leadsto \color{blue}{t \cdot \left(a \cdot -4\right)} + j \cdot \left(k \cdot -27\right) \]

      4. *-commutative 55.8%

         \[\leadsto t \cdot \color{blue}{\left(-4 \cdot a\right)} + j \cdot \left(k \cdot -27\right) \]

   7. Simplified 55.8%

      \[\leadsto \color{blue}{t \cdot \left(-4 \cdot a\right)} + j \cdot \left(k \cdot -27\right) \]

   if 2.69999999999999992e51 < (*.f64 b c) < 6.79999999999999948e154

   1. Initial program 67.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 75.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 75.0%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 75.0%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 75.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 75.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 75.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 75.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 75.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 75.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 75.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in y around inf 75.4%

      \[\leadsto \color{blue}{18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 63.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \cdot c \leq -1.45 \cdot 10^{+72}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{elif}\;b \cdot c \leq -55000000000000:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + x \cdot \left(i \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 2.7 \cdot 10^{+51}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + t \cdot \left(a \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 6.8 \cdot 10^{+154}:\\ \;\;\;\;18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 70.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} \mathbf{if}\;x \leq -3.3 \cdot 10^{+109}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \mathbf{elif}\;x \leq -1.55 \cdot 10^{-7} \lor \neg \left(x \leq -9.5 \cdot 10^{-35}\right) \land x \leq 1.58 \cdot 10^{-18}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(t \cdot a\right)\right) - \left(j \cdot 27\right) \cdot k\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(y \cdot \left(z \cdot t\right)\right) - 4 \cdot i\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (if (<= x -3.3e+109)
   (* x (- (* 18.0 (* t (* y z))) (* 4.0 i)))
   (if (or (<= x -1.55e-7) (and (not (<= x -9.5e-35)) (<= x 1.58e-18)))
     (- (- (* b c) (* 4.0 (* t a))) (* (* j 27.0) k))
     (* x (- (* 18.0 (* y (* z t))) (* 4.0 i))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double tmp;
	if (x <= -3.3e+109) {
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	} else if ((x <= -1.55e-7) || (!(x <= -9.5e-35) && (x <= 1.58e-18))) {
		tmp = ((b * c) - (4.0 * (t * a))) - ((j * 27.0) * k);
	} else {
		tmp = x * ((18.0 * (y * (z * t))) - (4.0 * i));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: tmp
    if (x <= (-3.3d+109)) then
        tmp = x * ((18.0d0 * (t * (y * z))) - (4.0d0 * i))
    else if ((x <= (-1.55d-7)) .or. (.not. (x <= (-9.5d-35))) .and. (x <= 1.58d-18)) then
        tmp = ((b * c) - (4.0d0 * (t * a))) - ((j * 27.0d0) * k)
    else
        tmp = x * ((18.0d0 * (y * (z * t))) - (4.0d0 * i))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 tmp;
	if (x <= -3.3e+109) {
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	} else if ((x <= -1.55e-7) || (!(x <= -9.5e-35) && (x <= 1.58e-18))) {
		tmp = ((b * c) - (4.0 * (t * a))) - ((j * 27.0) * k);
	} else {
		tmp = x * ((18.0 * (y * (z * t))) - (4.0 * i));
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	tmp = 0
	if x <= -3.3e+109:
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i))
	elif (x <= -1.55e-7) or (not (x <= -9.5e-35) and (x <= 1.58e-18)):
		tmp = ((b * c) - (4.0 * (t * a))) - ((j * 27.0) * k)
	else:
		tmp = x * ((18.0 * (y * (z * t))) - (4.0 * i))
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0
	if (x <= -3.3e+109)
		tmp = Float64(x * Float64(Float64(18.0 * Float64(t * Float64(y * z))) - Float64(4.0 * i)));
	elseif ((x <= -1.55e-7) || (!(x <= -9.5e-35) && (x <= 1.58e-18)))
		tmp = Float64(Float64(Float64(b * c) - Float64(4.0 * Float64(t * a))) - Float64(Float64(j * 27.0) * k));
	else
		tmp = Float64(x * Float64(Float64(18.0 * Float64(y * Float64(z * t))) - Float64(4.0 * i)));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0;
	if (x <= -3.3e+109)
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	elseif ((x <= -1.55e-7) || (~((x <= -9.5e-35)) && (x <= 1.58e-18)))
		tmp = ((b * c) - (4.0 * (t * a))) - ((j * 27.0) * k);
	else
		tmp = x * ((18.0 * (y * (z * t))) - (4.0 * i));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := If[LessEqual[x, -3.3e+109], N[(x * N[(N[(18.0 * N[(t * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[x, -1.55e-7], And[N[Not[LessEqual[x, -9.5e-35]], $MachinePrecision], LessEqual[x, 1.58e-18]]], N[(N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(j * 27.0), $MachinePrecision] * k), $MachinePrecision]), $MachinePrecision], N[(x * N[(N[(18.0 * N[(y * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.2999999999999999e109

   1. Initial program 68.4%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 78.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 69.3%

      \[\leadsto \color{blue}{x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)} \]

   if -3.2999999999999999e109 < x < -1.55e-7 or -9.5000000000000003e-35 < x < 1.5800000000000001e-18

   1. Initial program 93.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 81.6%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]

   if -1.55e-7 < x < -9.5000000000000003e-35 or 1.5800000000000001e-18 < x

   1. Initial program 79.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 89.2%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 68.6%

      \[\leadsto \color{blue}{x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)} \]
   6. Step-by-step derivation

      1. pow1 68.6%

         \[\leadsto x \cdot \left(18 \cdot \color{blue}{{\left(t \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot i\right) \]

      2. *-commutative 68.6%

         \[\leadsto x \cdot \left(18 \cdot {\color{blue}{\left(\left(y \cdot z\right) \cdot t\right)}}^{1} - 4 \cdot i\right) \]

   7. Applied egg-rr 68.6%

      \[\leadsto x \cdot \left(18 \cdot \color{blue}{{\left(\left(y \cdot z\right) \cdot t\right)}^{1}} - 4 \cdot i\right) \]
   8. Step-by-step derivation

      1. unpow1 68.6%

         \[\leadsto x \cdot \left(18 \cdot \color{blue}{\left(\left(y \cdot z\right) \cdot t\right)} - 4 \cdot i\right) \]

      2. associate-*l* 70.8%

         \[\leadsto x \cdot \left(18 \cdot \color{blue}{\left(y \cdot \left(z \cdot t\right)\right)} - 4 \cdot i\right) \]

   9. Simplified 70.8%

      \[\leadsto x \cdot \left(18 \cdot \color{blue}{\left(y \cdot \left(z \cdot t\right)\right)} - 4 \cdot i\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 76.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.3 \cdot 10^{+109}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \mathbf{elif}\;x \leq -1.55 \cdot 10^{-7} \lor \neg \left(x \leq -9.5 \cdot 10^{-35}\right) \land x \leq 1.58 \cdot 10^{-18}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(t \cdot a\right)\right) - \left(j \cdot 27\right) \cdot k\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(y \cdot \left(z \cdot t\right)\right) - 4 \cdot i\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 86.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} \mathbf{if}\;z \leq 1.25 \cdot 10^{+98}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right)\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(\left(x \cdot y\right) \cdot \left(18 \cdot z\right) - a \cdot 4\right)\right) - 27 \cdot \left(j \cdot k\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (if (<= z 1.25e+98)
   (-
    (+ (* b c) (* t (- (* (* x 18.0) (* y z)) (* a 4.0))))
    (+ (* x (* 4.0 i)) (* j (* 27.0 k))))
   (-
    (+ (* b c) (* t (- (* (* x y) (* 18.0 z)) (* a 4.0))))
    (* 27.0 (* j k)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double tmp;
	if (z <= 1.25e+98) {
		tmp = ((b * c) + (t * (((x * 18.0) * (y * z)) - (a * 4.0)))) - ((x * (4.0 * i)) + (j * (27.0 * k)));
	} else {
		tmp = ((b * c) + (t * (((x * y) * (18.0 * z)) - (a * 4.0)))) - (27.0 * (j * k));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: tmp
    if (z <= 1.25d+98) then
        tmp = ((b * c) + (t * (((x * 18.0d0) * (y * z)) - (a * 4.0d0)))) - ((x * (4.0d0 * i)) + (j * (27.0d0 * k)))
    else
        tmp = ((b * c) + (t * (((x * y) * (18.0d0 * z)) - (a * 4.0d0)))) - (27.0d0 * (j * k))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 tmp;
	if (z <= 1.25e+98) {
		tmp = ((b * c) + (t * (((x * 18.0) * (y * z)) - (a * 4.0)))) - ((x * (4.0 * i)) + (j * (27.0 * k)));
	} else {
		tmp = ((b * c) + (t * (((x * y) * (18.0 * z)) - (a * 4.0)))) - (27.0 * (j * k));
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	tmp = 0
	if z <= 1.25e+98:
		tmp = ((b * c) + (t * (((x * 18.0) * (y * z)) - (a * 4.0)))) - ((x * (4.0 * i)) + (j * (27.0 * k)))
	else:
		tmp = ((b * c) + (t * (((x * y) * (18.0 * z)) - (a * 4.0)))) - (27.0 * (j * k))
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0
	if (z <= 1.25e+98)
		tmp = Float64(Float64(Float64(b * c) + Float64(t * Float64(Float64(Float64(x * 18.0) * Float64(y * z)) - Float64(a * 4.0)))) - Float64(Float64(x * Float64(4.0 * i)) + Float64(j * Float64(27.0 * k))));
	else
		tmp = Float64(Float64(Float64(b * c) + Float64(t * Float64(Float64(Float64(x * y) * Float64(18.0 * z)) - Float64(a * 4.0)))) - Float64(27.0 * Float64(j * k)));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0;
	if (z <= 1.25e+98)
		tmp = ((b * c) + (t * (((x * 18.0) * (y * z)) - (a * 4.0)))) - ((x * (4.0 * i)) + (j * (27.0 * k)));
	else
		tmp = ((b * c) + (t * (((x * y) * (18.0 * z)) - (a * 4.0)))) - (27.0 * (j * k));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := If[LessEqual[z, 1.25e+98], N[(N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(N[(x * 18.0), $MachinePrecision] * N[(y * z), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(x * N[(4.0 * i), $MachinePrecision]), $MachinePrecision] + N[(j * N[(27.0 * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(N[(x * y), $MachinePrecision] * N[(18.0 * z), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(27.0 * N[(j * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 1.25e98

   1. Initial program 86.4%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 89.1%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   if 1.25e98 < z

   1. Initial program 79.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 73.9%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 73.9%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 73.9%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 73.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 73.9%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 73.9%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 73.9%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 73.9%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 73.9%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 73.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 71.3%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. pow1 71.3%

         \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

      2. associate-*r* 71.3%

         \[\leadsto \left(b \cdot c + t \cdot \left({\color{blue}{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}}^{1} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   9. Applied egg-rr 71.3%

      \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
   10. Step-by-step derivation

       1. unpow1 71.3%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(18 \cdot x\right) \cdot \left(y \cdot z\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       2. associate-*r* 71.3%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{18 \cdot \left(x \cdot \left(y \cdot z\right)\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       3. *-commutative 71.3%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot 18} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       4. associate-*r* 81.7%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18 - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       5. associate-*l* 81.7%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   11. Simplified 81.7%

       \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 88.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq 1.25 \cdot 10^{+98}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right)\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(\left(x \cdot y\right) \cdot \left(18 \cdot z\right) - a \cdot 4\right)\right) - 27 \cdot \left(j \cdot k\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 58.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := b \cdot c - 4 \cdot \left(t \cdot a\right)\\ t_2 := x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \mathbf{if}\;x \leq -8 \cdot 10^{+89}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;x \leq -3.2 \cdot 10^{-72}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 2.25 \cdot 10^{-243}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + t \cdot \left(a \cdot -4\right)\\ \mathbf{elif}\;x \leq 4.1 \cdot 10^{-20}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (- (* b c) (* 4.0 (* t a))))
        (t_2 (* x (- (* 18.0 (* t (* y z))) (* 4.0 i)))))
   (if (<= x -8e+89)
     t_2
     (if (<= x -3.2e-72)
       t_1
       (if (<= x 2.25e-243)
         (+ (* j (* k -27.0)) (* t (* a -4.0)))
         (if (<= x 4.1e-20) t_1 t_2))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (b * c) - (4.0 * (t * a));
	double t_2 = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	double tmp;
	if (x <= -8e+89) {
		tmp = t_2;
	} else if (x <= -3.2e-72) {
		tmp = t_1;
	} else if (x <= 2.25e-243) {
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	} else if (x <= 4.1e-20) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (b * c) - (4.0d0 * (t * a))
    t_2 = x * ((18.0d0 * (t * (y * z))) - (4.0d0 * i))
    if (x <= (-8d+89)) then
        tmp = t_2
    else if (x <= (-3.2d-72)) then
        tmp = t_1
    else if (x <= 2.25d-243) then
        tmp = (j * (k * (-27.0d0))) + (t * (a * (-4.0d0)))
    else if (x <= 4.1d-20) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (b * c) - (4.0 * (t * a));
	double t_2 = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	double tmp;
	if (x <= -8e+89) {
		tmp = t_2;
	} else if (x <= -3.2e-72) {
		tmp = t_1;
	} else if (x <= 2.25e-243) {
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	} else if (x <= 4.1e-20) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (b * c) - (4.0 * (t * a))
	t_2 = x * ((18.0 * (t * (y * z))) - (4.0 * i))
	tmp = 0
	if x <= -8e+89:
		tmp = t_2
	elif x <= -3.2e-72:
		tmp = t_1
	elif x <= 2.25e-243:
		tmp = (j * (k * -27.0)) + (t * (a * -4.0))
	elif x <= 4.1e-20:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(b * c) - Float64(4.0 * Float64(t * a)))
	t_2 = Float64(x * Float64(Float64(18.0 * Float64(t * Float64(y * z))) - Float64(4.0 * i)))
	tmp = 0.0
	if (x <= -8e+89)
		tmp = t_2;
	elseif (x <= -3.2e-72)
		tmp = t_1;
	elseif (x <= 2.25e-243)
		tmp = Float64(Float64(j * Float64(k * -27.0)) + Float64(t * Float64(a * -4.0)));
	elseif (x <= 4.1e-20)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (b * c) - (4.0 * (t * a));
	t_2 = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	tmp = 0.0;
	if (x <= -8e+89)
		tmp = t_2;
	elseif (x <= -3.2e-72)
		tmp = t_1;
	elseif (x <= 2.25e-243)
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	elseif (x <= 4.1e-20)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(N[(18.0 * N[(t * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -8e+89], t$95$2, If[LessEqual[x, -3.2e-72], t$95$1, If[LessEqual[x, 2.25e-243], N[(N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision] + N[(t * N[(a * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 4.1e-20], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.99999999999999996e89 or 4.1000000000000001e-20 < x

   1. Initial program 77.8%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 85.9%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 67.0%

      \[\leadsto \color{blue}{x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)} \]

   if -7.99999999999999996e89 < x < -3.19999999999999999e-72 or 2.25000000000000009e-243 < x < 4.1000000000000001e-20

   1. Initial program 90.2%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 73.8%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]

   4. Taylor expanded in j around 0 65.4%

      \[\leadsto \color{blue}{b \cdot c - 4 \cdot \left(a \cdot t\right)} \]

   if -3.19999999999999999e-72 < x < 2.25000000000000009e-243

   1. Initial program 94.4%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 85.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 68.6%

      \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} + j \cdot \left(k \cdot -27\right) \]
   6. Step-by-step derivation

      1. *-commutative 68.6%

         \[\leadsto \color{blue}{\left(a \cdot t\right) \cdot -4} + j \cdot \left(k \cdot -27\right) \]

      2. *-commutative 68.6%

         \[\leadsto \color{blue}{\left(t \cdot a\right)} \cdot -4 + j \cdot \left(k \cdot -27\right) \]

      3. associate-*r* 68.6%

         \[\leadsto \color{blue}{t \cdot \left(a \cdot -4\right)} + j \cdot \left(k \cdot -27\right) \]

      4. *-commutative 68.6%

         \[\leadsto t \cdot \color{blue}{\left(-4 \cdot a\right)} + j \cdot \left(k \cdot -27\right) \]

   7. Simplified 68.6%

      \[\leadsto \color{blue}{t \cdot \left(-4 \cdot a\right)} + j \cdot \left(k \cdot -27\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 66.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -8 \cdot 10^{+89}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \mathbf{elif}\;x \leq -3.2 \cdot 10^{-72}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{elif}\;x \leq 2.25 \cdot 10^{-243}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + t \cdot \left(a \cdot -4\right)\\ \mathbf{elif}\;x \leq 4.1 \cdot 10^{-20}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 58.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{if}\;x \leq -7.5 \cdot 10^{+89}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \mathbf{elif}\;x \leq -4.2 \cdot 10^{-73}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 2.1 \cdot 10^{-243}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + t \cdot \left(a \cdot -4\right)\\ \mathbf{elif}\;x \leq 1.58 \cdot 10^{-18}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(y \cdot \left(z \cdot t\right)\right) - 4 \cdot i\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (- (* b c) (* 4.0 (* t a)))))
   (if (<= x -7.5e+89)
     (* x (- (* 18.0 (* t (* y z))) (* 4.0 i)))
     (if (<= x -4.2e-73)
       t_1
       (if (<= x 2.1e-243)
         (+ (* j (* k -27.0)) (* t (* a -4.0)))
         (if (<= x 1.58e-18)
           t_1
           (* x (- (* 18.0 (* y (* z t))) (* 4.0 i)))))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (b * c) - (4.0 * (t * a));
	double tmp;
	if (x <= -7.5e+89) {
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	} else if (x <= -4.2e-73) {
		tmp = t_1;
	} else if (x <= 2.1e-243) {
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	} else if (x <= 1.58e-18) {
		tmp = t_1;
	} else {
		tmp = x * ((18.0 * (y * (z * t))) - (4.0 * i));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: tmp
    t_1 = (b * c) - (4.0d0 * (t * a))
    if (x <= (-7.5d+89)) then
        tmp = x * ((18.0d0 * (t * (y * z))) - (4.0d0 * i))
    else if (x <= (-4.2d-73)) then
        tmp = t_1
    else if (x <= 2.1d-243) then
        tmp = (j * (k * (-27.0d0))) + (t * (a * (-4.0d0)))
    else if (x <= 1.58d-18) then
        tmp = t_1
    else
        tmp = x * ((18.0d0 * (y * (z * t))) - (4.0d0 * i))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (b * c) - (4.0 * (t * a));
	double tmp;
	if (x <= -7.5e+89) {
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	} else if (x <= -4.2e-73) {
		tmp = t_1;
	} else if (x <= 2.1e-243) {
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	} else if (x <= 1.58e-18) {
		tmp = t_1;
	} else {
		tmp = x * ((18.0 * (y * (z * t))) - (4.0 * i));
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (b * c) - (4.0 * (t * a))
	tmp = 0
	if x <= -7.5e+89:
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i))
	elif x <= -4.2e-73:
		tmp = t_1
	elif x <= 2.1e-243:
		tmp = (j * (k * -27.0)) + (t * (a * -4.0))
	elif x <= 1.58e-18:
		tmp = t_1
	else:
		tmp = x * ((18.0 * (y * (z * t))) - (4.0 * i))
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(b * c) - Float64(4.0 * Float64(t * a)))
	tmp = 0.0
	if (x <= -7.5e+89)
		tmp = Float64(x * Float64(Float64(18.0 * Float64(t * Float64(y * z))) - Float64(4.0 * i)));
	elseif (x <= -4.2e-73)
		tmp = t_1;
	elseif (x <= 2.1e-243)
		tmp = Float64(Float64(j * Float64(k * -27.0)) + Float64(t * Float64(a * -4.0)));
	elseif (x <= 1.58e-18)
		tmp = t_1;
	else
		tmp = Float64(x * Float64(Float64(18.0 * Float64(y * Float64(z * t))) - Float64(4.0 * i)));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (b * c) - (4.0 * (t * a));
	tmp = 0.0;
	if (x <= -7.5e+89)
		tmp = x * ((18.0 * (t * (y * z))) - (4.0 * i));
	elseif (x <= -4.2e-73)
		tmp = t_1;
	elseif (x <= 2.1e-243)
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	elseif (x <= 1.58e-18)
		tmp = t_1;
	else
		tmp = x * ((18.0 * (y * (z * t))) - (4.0 * i));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -7.5e+89], N[(x * N[(N[(18.0 * N[(t * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -4.2e-73], t$95$1, If[LessEqual[x, 2.1e-243], N[(N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision] + N[(t * N[(a * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.58e-18], t$95$1, N[(x * N[(N[(18.0 * N[(y * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -7.49999999999999947e89

   1. Initial program 71.8%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 80.4%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 66.3%

      \[\leadsto \color{blue}{x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)} \]

   if -7.49999999999999947e89 < x < -4.1999999999999997e-73 or 2.1000000000000001e-243 < x < 1.5800000000000001e-18

   1. Initial program 90.2%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 73.8%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]

   4. Taylor expanded in j around 0 65.4%

      \[\leadsto \color{blue}{b \cdot c - 4 \cdot \left(a \cdot t\right)} \]

   if -4.1999999999999997e-73 < x < 2.1000000000000001e-243

   1. Initial program 94.4%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 85.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 68.6%

      \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} + j \cdot \left(k \cdot -27\right) \]
   6. Step-by-step derivation

      1. *-commutative 68.6%

         \[\leadsto \color{blue}{\left(a \cdot t\right) \cdot -4} + j \cdot \left(k \cdot -27\right) \]

      2. *-commutative 68.6%

         \[\leadsto \color{blue}{\left(t \cdot a\right)} \cdot -4 + j \cdot \left(k \cdot -27\right) \]

      3. associate-*r* 68.6%

         \[\leadsto \color{blue}{t \cdot \left(a \cdot -4\right)} + j \cdot \left(k \cdot -27\right) \]

      4. *-commutative 68.6%

         \[\leadsto t \cdot \color{blue}{\left(-4 \cdot a\right)} + j \cdot \left(k \cdot -27\right) \]

   7. Simplified 68.6%

      \[\leadsto \color{blue}{t \cdot \left(-4 \cdot a\right)} + j \cdot \left(k \cdot -27\right) \]

   if 1.5800000000000001e-18 < x

   1. Initial program 81.5%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 89.3%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 67.4%

      \[\leadsto \color{blue}{x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)} \]
   6. Step-by-step derivation

      1. pow1 67.4%

         \[\leadsto x \cdot \left(18 \cdot \color{blue}{{\left(t \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot i\right) \]

      2. *-commutative 67.4%

         \[\leadsto x \cdot \left(18 \cdot {\color{blue}{\left(\left(y \cdot z\right) \cdot t\right)}}^{1} - 4 \cdot i\right) \]

   7. Applied egg-rr 67.4%

      \[\leadsto x \cdot \left(18 \cdot \color{blue}{{\left(\left(y \cdot z\right) \cdot t\right)}^{1}} - 4 \cdot i\right) \]
   8. Step-by-step derivation

      1. unpow1 67.4%

         \[\leadsto x \cdot \left(18 \cdot \color{blue}{\left(\left(y \cdot z\right) \cdot t\right)} - 4 \cdot i\right) \]

      2. associate-*l* 69.9%

         \[\leadsto x \cdot \left(18 \cdot \color{blue}{\left(y \cdot \left(z \cdot t\right)\right)} - 4 \cdot i\right) \]

   9. Simplified 69.9%

      \[\leadsto x \cdot \left(18 \cdot \color{blue}{\left(y \cdot \left(z \cdot t\right)\right)} - 4 \cdot i\right) \]
3. Recombined 4 regimes into one program.

4. Final simplification 67.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.5 \cdot 10^{+89}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right) - 4 \cdot i\right)\\ \mathbf{elif}\;x \leq -4.2 \cdot 10^{-73}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{elif}\;x \leq 2.1 \cdot 10^{-243}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + t \cdot \left(a \cdot -4\right)\\ \mathbf{elif}\;x \leq 1.58 \cdot 10^{-18}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(18 \cdot \left(y \cdot \left(z \cdot t\right)\right) - 4 \cdot i\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 75.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \mathbf{if}\;k \leq -9 \cdot 10^{+56}:\\ \;\;\;\;x \cdot \left(\left(18 \cdot z\right) \cdot \left(y \cdot t\right)\right) + j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;k \leq 7.8 \cdot 10^{+80}:\\ \;\;\;\;t\_1 - 4 \cdot \left(x \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1 - 27 \cdot \left(j \cdot k\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (+ (* b c) (* t (- (* 18.0 (* x (* y z))) (* a 4.0))))))
   (if (<= k -9e+56)
     (+ (* x (* (* 18.0 z) (* y t))) (* j (* k -27.0)))
     (if (<= k 7.8e+80) (- t_1 (* 4.0 (* x i))) (- t_1 (* 27.0 (* j k)))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	double tmp;
	if (k <= -9e+56) {
		tmp = (x * ((18.0 * z) * (y * t))) + (j * (k * -27.0));
	} else if (k <= 7.8e+80) {
		tmp = t_1 - (4.0 * (x * i));
	} else {
		tmp = t_1 - (27.0 * (j * k));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: tmp
    t_1 = (b * c) + (t * ((18.0d0 * (x * (y * z))) - (a * 4.0d0)))
    if (k <= (-9d+56)) then
        tmp = (x * ((18.0d0 * z) * (y * t))) + (j * (k * (-27.0d0)))
    else if (k <= 7.8d+80) then
        tmp = t_1 - (4.0d0 * (x * i))
    else
        tmp = t_1 - (27.0d0 * (j * k))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	double tmp;
	if (k <= -9e+56) {
		tmp = (x * ((18.0 * z) * (y * t))) + (j * (k * -27.0));
	} else if (k <= 7.8e+80) {
		tmp = t_1 - (4.0 * (x * i));
	} else {
		tmp = t_1 - (27.0 * (j * k));
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))
	tmp = 0
	if k <= -9e+56:
		tmp = (x * ((18.0 * z) * (y * t))) + (j * (k * -27.0))
	elif k <= 7.8e+80:
		tmp = t_1 - (4.0 * (x * i))
	else:
		tmp = t_1 - (27.0 * (j * k))
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(b * c) + Float64(t * Float64(Float64(18.0 * Float64(x * Float64(y * z))) - Float64(a * 4.0))))
	tmp = 0.0
	if (k <= -9e+56)
		tmp = Float64(Float64(x * Float64(Float64(18.0 * z) * Float64(y * t))) + Float64(j * Float64(k * -27.0)));
	elseif (k <= 7.8e+80)
		tmp = Float64(t_1 - Float64(4.0 * Float64(x * i)));
	else
		tmp = Float64(t_1 - Float64(27.0 * Float64(j * k)));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	tmp = 0.0;
	if (k <= -9e+56)
		tmp = (x * ((18.0 * z) * (y * t))) + (j * (k * -27.0));
	elseif (k <= 7.8e+80)
		tmp = t_1 - (4.0 * (x * i));
	else
		tmp = t_1 - (27.0 * (j * k));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(18.0 * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[k, -9e+56], N[(N[(x * N[(N[(18.0 * z), $MachinePrecision] * N[(y * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[k, 7.8e+80], N[(t$95$1 - N[(4.0 * N[(x * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t$95$1 - N[(27.0 * N[(j * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if k < -9.0000000000000006e56

   1. Initial program 83.3%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 88.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 61.0%

      \[\leadsto \color{blue}{18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)} + j \cdot \left(k \cdot -27\right) \]
   6. Step-by-step derivation

      1. *-commutative 61.0%

         \[\leadsto 18 \cdot \left(t \cdot \color{blue}{\left(\left(y \cdot z\right) \cdot x\right)}\right) + j \cdot \left(k \cdot -27\right) \]

      2. associate-*r* 61.1%

         \[\leadsto 18 \cdot \color{blue}{\left(\left(t \cdot \left(y \cdot z\right)\right) \cdot x\right)} + j \cdot \left(k \cdot -27\right) \]

      3. associate-*l* 61.0%

         \[\leadsto \color{blue}{\left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right)\right) \cdot x} + j \cdot \left(k \cdot -27\right) \]

      4. *-commutative 61.0%

         \[\leadsto \color{blue}{x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right)\right)} + j \cdot \left(k \cdot -27\right) \]

      5. *-commutative 61.0%

         \[\leadsto x \cdot \color{blue}{\left(\left(t \cdot \left(y \cdot z\right)\right) \cdot 18\right)} + j \cdot \left(k \cdot -27\right) \]

      6. associate-*r* 64.7%

         \[\leadsto x \cdot \left(\color{blue}{\left(\left(t \cdot y\right) \cdot z\right)} \cdot 18\right) + j \cdot \left(k \cdot -27\right) \]

      7. associate-*l* 64.7%

         \[\leadsto x \cdot \color{blue}{\left(\left(t \cdot y\right) \cdot \left(z \cdot 18\right)\right)} + j \cdot \left(k \cdot -27\right) \]

      8. *-commutative 64.7%

         \[\leadsto x \cdot \left(\color{blue}{\left(y \cdot t\right)} \cdot \left(z \cdot 18\right)\right) + j \cdot \left(k \cdot -27\right) \]

   7. Simplified 64.7%

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot t\right) \cdot \left(z \cdot 18\right)\right)} + j \cdot \left(k \cdot -27\right) \]

   if -9.0000000000000006e56 < k < 7.79999999999999998e80

   1. Initial program 89.5%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 89.6%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in j around 0 86.2%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 4 \cdot \left(i \cdot x\right)} \]

   if 7.79999999999999998e80 < k

   1. Initial program 74.6%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 78.5%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 78.5%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 78.5%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 80.4%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 80.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 80.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 82.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 82.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 82.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 82.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 80.4%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 80.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;k \leq -9 \cdot 10^{+56}:\\ \;\;\;\;x \cdot \left(\left(18 \cdot z\right) \cdot \left(y \cdot t\right)\right) + j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;k \leq 7.8 \cdot 10^{+80}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\right) - 4 \cdot \left(x \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\right) - 27 \cdot \left(j \cdot k\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 76.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} \mathbf{if}\;k \leq -4.2 \cdot 10^{+58}:\\ \;\;\;\;x \cdot \left(\left(18 \cdot z\right) \cdot \left(y \cdot t\right)\right) + j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;k \leq 1.95 \cdot 10^{-25}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\right) - 4 \cdot \left(x \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(\left(x \cdot y\right) \cdot \left(18 \cdot z\right) - a \cdot 4\right)\right) - 27 \cdot \left(j \cdot k\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (if (<= k -4.2e+58)
   (+ (* x (* (* 18.0 z) (* y t))) (* j (* k -27.0)))
   (if (<= k 1.95e-25)
     (- (+ (* b c) (* t (- (* 18.0 (* x (* y z))) (* a 4.0)))) (* 4.0 (* x i)))
     (-
      (+ (* b c) (* t (- (* (* x y) (* 18.0 z)) (* a 4.0))))
      (* 27.0 (* j k))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double tmp;
	if (k <= -4.2e+58) {
		tmp = (x * ((18.0 * z) * (y * t))) + (j * (k * -27.0));
	} else if (k <= 1.95e-25) {
		tmp = ((b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))) - (4.0 * (x * i));
	} else {
		tmp = ((b * c) + (t * (((x * y) * (18.0 * z)) - (a * 4.0)))) - (27.0 * (j * k));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: tmp
    if (k <= (-4.2d+58)) then
        tmp = (x * ((18.0d0 * z) * (y * t))) + (j * (k * (-27.0d0)))
    else if (k <= 1.95d-25) then
        tmp = ((b * c) + (t * ((18.0d0 * (x * (y * z))) - (a * 4.0d0)))) - (4.0d0 * (x * i))
    else
        tmp = ((b * c) + (t * (((x * y) * (18.0d0 * z)) - (a * 4.0d0)))) - (27.0d0 * (j * k))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 tmp;
	if (k <= -4.2e+58) {
		tmp = (x * ((18.0 * z) * (y * t))) + (j * (k * -27.0));
	} else if (k <= 1.95e-25) {
		tmp = ((b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))) - (4.0 * (x * i));
	} else {
		tmp = ((b * c) + (t * (((x * y) * (18.0 * z)) - (a * 4.0)))) - (27.0 * (j * k));
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	tmp = 0
	if k <= -4.2e+58:
		tmp = (x * ((18.0 * z) * (y * t))) + (j * (k * -27.0))
	elif k <= 1.95e-25:
		tmp = ((b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))) - (4.0 * (x * i))
	else:
		tmp = ((b * c) + (t * (((x * y) * (18.0 * z)) - (a * 4.0)))) - (27.0 * (j * k))
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0
	if (k <= -4.2e+58)
		tmp = Float64(Float64(x * Float64(Float64(18.0 * z) * Float64(y * t))) + Float64(j * Float64(k * -27.0)));
	elseif (k <= 1.95e-25)
		tmp = Float64(Float64(Float64(b * c) + Float64(t * Float64(Float64(18.0 * Float64(x * Float64(y * z))) - Float64(a * 4.0)))) - Float64(4.0 * Float64(x * i)));
	else
		tmp = Float64(Float64(Float64(b * c) + Float64(t * Float64(Float64(Float64(x * y) * Float64(18.0 * z)) - Float64(a * 4.0)))) - Float64(27.0 * Float64(j * k)));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0;
	if (k <= -4.2e+58)
		tmp = (x * ((18.0 * z) * (y * t))) + (j * (k * -27.0));
	elseif (k <= 1.95e-25)
		tmp = ((b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))) - (4.0 * (x * i));
	else
		tmp = ((b * c) + (t * (((x * y) * (18.0 * z)) - (a * 4.0)))) - (27.0 * (j * k));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := If[LessEqual[k, -4.2e+58], N[(N[(x * N[(N[(18.0 * z), $MachinePrecision] * N[(y * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[k, 1.95e-25], N[(N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(18.0 * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(x * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(N[(x * y), $MachinePrecision] * N[(18.0 * z), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(27.0 * N[(j * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if k < -4.20000000000000024e58

   1. Initial program 83.3%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 88.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in y around inf 61.0%

      \[\leadsto \color{blue}{18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)} + j \cdot \left(k \cdot -27\right) \]
   6. Step-by-step derivation

      1. *-commutative 61.0%

         \[\leadsto 18 \cdot \left(t \cdot \color{blue}{\left(\left(y \cdot z\right) \cdot x\right)}\right) + j \cdot \left(k \cdot -27\right) \]

      2. associate-*r* 61.1%

         \[\leadsto 18 \cdot \color{blue}{\left(\left(t \cdot \left(y \cdot z\right)\right) \cdot x\right)} + j \cdot \left(k \cdot -27\right) \]

      3. associate-*l* 61.0%

         \[\leadsto \color{blue}{\left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right)\right) \cdot x} + j \cdot \left(k \cdot -27\right) \]

      4. *-commutative 61.0%

         \[\leadsto \color{blue}{x \cdot \left(18 \cdot \left(t \cdot \left(y \cdot z\right)\right)\right)} + j \cdot \left(k \cdot -27\right) \]

      5. *-commutative 61.0%

         \[\leadsto x \cdot \color{blue}{\left(\left(t \cdot \left(y \cdot z\right)\right) \cdot 18\right)} + j \cdot \left(k \cdot -27\right) \]

      6. associate-*r* 64.7%

         \[\leadsto x \cdot \left(\color{blue}{\left(\left(t \cdot y\right) \cdot z\right)} \cdot 18\right) + j \cdot \left(k \cdot -27\right) \]

      7. associate-*l* 64.7%

         \[\leadsto x \cdot \color{blue}{\left(\left(t \cdot y\right) \cdot \left(z \cdot 18\right)\right)} + j \cdot \left(k \cdot -27\right) \]

      8. *-commutative 64.7%

         \[\leadsto x \cdot \left(\color{blue}{\left(y \cdot t\right)} \cdot \left(z \cdot 18\right)\right) + j \cdot \left(k \cdot -27\right) \]

   7. Simplified 64.7%

      \[\leadsto \color{blue}{x \cdot \left(\left(y \cdot t\right) \cdot \left(z \cdot 18\right)\right)} + j \cdot \left(k \cdot -27\right) \]

   if -4.20000000000000024e58 < k < 1.95e-25

   1. Initial program 89.5%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 90.3%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in j around 0 86.3%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 4 \cdot \left(i \cdot x\right)} \]

   if 1.95e-25 < k

   1. Initial program 78.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 80.3%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 80.3%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 80.3%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 81.7%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 81.7%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 81.7%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 83.1%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 83.1%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 83.1%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 83.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 79.0%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. pow1 79.0%

         \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

      2. associate-*r* 79.0%

         \[\leadsto \left(b \cdot c + t \cdot \left({\color{blue}{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}}^{1} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   9. Applied egg-rr 79.0%

      \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
   10. Step-by-step derivation

       1. unpow1 79.0%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(18 \cdot x\right) \cdot \left(y \cdot z\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       2. associate-*r* 79.0%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{18 \cdot \left(x \cdot \left(y \cdot z\right)\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       3. *-commutative 79.0%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot 18} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       4. associate-*r* 80.4%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18 - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       5. associate-*l* 80.4%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   11. Simplified 80.4%

       \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
3. Recombined 3 regimes into one program.

4. Final simplification 80.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;k \leq -4.2 \cdot 10^{+58}:\\ \;\;\;\;x \cdot \left(\left(18 \cdot z\right) \cdot \left(y \cdot t\right)\right) + j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;k \leq 1.95 \cdot 10^{-25}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\right) - 4 \cdot \left(x \cdot i\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c + t \cdot \left(\left(x \cdot y\right) \cdot \left(18 \cdot z\right) - a \cdot 4\right)\right) - 27 \cdot \left(j \cdot k\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 52.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{if}\;b \cdot c \leq -3.4 \cdot 10^{+43}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \cdot c \leq 7.6 \cdot 10^{+51}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + t \cdot \left(a \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 3.55 \cdot 10^{+152}:\\ \;\;\;\;18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (- (* b c) (* 4.0 (* t a)))))
   (if (<= (* b c) -3.4e+43)
     t_1
     (if (<= (* b c) 7.6e+51)
       (+ (* j (* k -27.0)) (* t (* a -4.0)))
       (if (<= (* b c) 3.55e+152) (* 18.0 (* t (* x (* y z)))) t_1)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (b * c) - (4.0 * (t * a));
	double tmp;
	if ((b * c) <= -3.4e+43) {
		tmp = t_1;
	} else if ((b * c) <= 7.6e+51) {
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	} else if ((b * c) <= 3.55e+152) {
		tmp = 18.0 * (t * (x * (y * z)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: tmp
    t_1 = (b * c) - (4.0d0 * (t * a))
    if ((b * c) <= (-3.4d+43)) then
        tmp = t_1
    else if ((b * c) <= 7.6d+51) then
        tmp = (j * (k * (-27.0d0))) + (t * (a * (-4.0d0)))
    else if ((b * c) <= 3.55d+152) then
        tmp = 18.0d0 * (t * (x * (y * z)))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (b * c) - (4.0 * (t * a));
	double tmp;
	if ((b * c) <= -3.4e+43) {
		tmp = t_1;
	} else if ((b * c) <= 7.6e+51) {
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	} else if ((b * c) <= 3.55e+152) {
		tmp = 18.0 * (t * (x * (y * z)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (b * c) - (4.0 * (t * a))
	tmp = 0
	if (b * c) <= -3.4e+43:
		tmp = t_1
	elif (b * c) <= 7.6e+51:
		tmp = (j * (k * -27.0)) + (t * (a * -4.0))
	elif (b * c) <= 3.55e+152:
		tmp = 18.0 * (t * (x * (y * z)))
	else:
		tmp = t_1
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(b * c) - Float64(4.0 * Float64(t * a)))
	tmp = 0.0
	if (Float64(b * c) <= -3.4e+43)
		tmp = t_1;
	elseif (Float64(b * c) <= 7.6e+51)
		tmp = Float64(Float64(j * Float64(k * -27.0)) + Float64(t * Float64(a * -4.0)));
	elseif (Float64(b * c) <= 3.55e+152)
		tmp = Float64(18.0 * Float64(t * Float64(x * Float64(y * z))));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (b * c) - (4.0 * (t * a));
	tmp = 0.0;
	if ((b * c) <= -3.4e+43)
		tmp = t_1;
	elseif ((b * c) <= 7.6e+51)
		tmp = (j * (k * -27.0)) + (t * (a * -4.0));
	elseif ((b * c) <= 3.55e+152)
		tmp = 18.0 * (t * (x * (y * z)));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(b * c), $MachinePrecision], -3.4e+43], t$95$1, If[LessEqual[N[(b * c), $MachinePrecision], 7.6e+51], N[(N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision] + N[(t * N[(a * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(b * c), $MachinePrecision], 3.55e+152], N[(18.0 * N[(t * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 b c) < -3.40000000000000012e43 or 3.55000000000000008e152 < (*.f64 b c)

   1. Initial program 85.0%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 73.8%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]

   4. Taylor expanded in j around 0 70.1%

      \[\leadsto \color{blue}{b \cdot c - 4 \cdot \left(a \cdot t\right)} \]

   if -3.40000000000000012e43 < (*.f64 b c) < 7.5999999999999994e51

   1. Initial program 87.0%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 89.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in a around inf 54.2%

      \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} + j \cdot \left(k \cdot -27\right) \]
   6. Step-by-step derivation

      1. *-commutative 54.2%

         \[\leadsto \color{blue}{\left(a \cdot t\right) \cdot -4} + j \cdot \left(k \cdot -27\right) \]

      2. *-commutative 54.2%

         \[\leadsto \color{blue}{\left(t \cdot a\right)} \cdot -4 + j \cdot \left(k \cdot -27\right) \]

      3. associate-*r* 54.2%

         \[\leadsto \color{blue}{t \cdot \left(a \cdot -4\right)} + j \cdot \left(k \cdot -27\right) \]

      4. *-commutative 54.2%

         \[\leadsto t \cdot \color{blue}{\left(-4 \cdot a\right)} + j \cdot \left(k \cdot -27\right) \]

   7. Simplified 54.2%

      \[\leadsto \color{blue}{t \cdot \left(-4 \cdot a\right)} + j \cdot \left(k \cdot -27\right) \]

   if 7.5999999999999994e51 < (*.f64 b c) < 3.55000000000000008e152

   1. Initial program 67.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 75.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 75.0%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 75.0%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 75.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 75.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 75.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 75.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 75.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 75.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 75.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in y around inf 75.4%

      \[\leadsto \color{blue}{18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 61.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \cdot c \leq -3.4 \cdot 10^{+43}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{elif}\;b \cdot c \leq 7.6 \cdot 10^{+51}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + t \cdot \left(a \cdot -4\right)\\ \mathbf{elif}\;b \cdot c \leq 3.55 \cdot 10^{+152}:\\ \;\;\;\;18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 72.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := \left(j \cdot 27\right) \cdot k\\ t_2 := t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \mathbf{if}\;t \leq -5.2 \cdot 10^{+72}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t \leq -4.8 \cdot 10^{-55}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(t \cdot a\right)\right) - t\_1\\ \mathbf{elif}\;t \leq 2.7 \cdot 10^{+41}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(x \cdot i\right)\right) - t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (* (* j 27.0) k))
        (t_2 (* t (- (* 18.0 (* x (* y z))) (* a 4.0)))))
   (if (<= t -5.2e+72)
     t_2
     (if (<= t -4.8e-55)
       (- (- (* b c) (* 4.0 (* t a))) t_1)
       (if (<= t 2.7e+41) (- (- (* b c) (* 4.0 (* x i))) t_1) t_2)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (j * 27.0) * k;
	double t_2 = t * ((18.0 * (x * (y * z))) - (a * 4.0));
	double tmp;
	if (t <= -5.2e+72) {
		tmp = t_2;
	} else if (t <= -4.8e-55) {
		tmp = ((b * c) - (4.0 * (t * a))) - t_1;
	} else if (t <= 2.7e+41) {
		tmp = ((b * c) - (4.0 * (x * i))) - t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (j * 27.0d0) * k
    t_2 = t * ((18.0d0 * (x * (y * z))) - (a * 4.0d0))
    if (t <= (-5.2d+72)) then
        tmp = t_2
    else if (t <= (-4.8d-55)) then
        tmp = ((b * c) - (4.0d0 * (t * a))) - t_1
    else if (t <= 2.7d+41) then
        tmp = ((b * c) - (4.0d0 * (x * i))) - t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (j * 27.0) * k;
	double t_2 = t * ((18.0 * (x * (y * z))) - (a * 4.0));
	double tmp;
	if (t <= -5.2e+72) {
		tmp = t_2;
	} else if (t <= -4.8e-55) {
		tmp = ((b * c) - (4.0 * (t * a))) - t_1;
	} else if (t <= 2.7e+41) {
		tmp = ((b * c) - (4.0 * (x * i))) - t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (j * 27.0) * k
	t_2 = t * ((18.0 * (x * (y * z))) - (a * 4.0))
	tmp = 0
	if t <= -5.2e+72:
		tmp = t_2
	elif t <= -4.8e-55:
		tmp = ((b * c) - (4.0 * (t * a))) - t_1
	elif t <= 2.7e+41:
		tmp = ((b * c) - (4.0 * (x * i))) - t_1
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(j * 27.0) * k)
	t_2 = Float64(t * Float64(Float64(18.0 * Float64(x * Float64(y * z))) - Float64(a * 4.0)))
	tmp = 0.0
	if (t <= -5.2e+72)
		tmp = t_2;
	elseif (t <= -4.8e-55)
		tmp = Float64(Float64(Float64(b * c) - Float64(4.0 * Float64(t * a))) - t_1);
	elseif (t <= 2.7e+41)
		tmp = Float64(Float64(Float64(b * c) - Float64(4.0 * Float64(x * i))) - t_1);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (j * 27.0) * k;
	t_2 = t * ((18.0 * (x * (y * z))) - (a * 4.0));
	tmp = 0.0;
	if (t <= -5.2e+72)
		tmp = t_2;
	elseif (t <= -4.8e-55)
		tmp = ((b * c) - (4.0 * (t * a))) - t_1;
	elseif (t <= 2.7e+41)
		tmp = ((b * c) - (4.0 * (x * i))) - t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(j * 27.0), $MachinePrecision] * k), $MachinePrecision]}, Block[{t$95$2 = N[(t * N[(N[(18.0 * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.2e+72], t$95$2, If[LessEqual[t, -4.8e-55], N[(N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], If[LessEqual[t, 2.7e+41], N[(N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(x * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if t < -5.19999999999999963e72 or 2.7e41 < t

   1. Initial program 83.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 88.4%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 71.8%

      \[\leadsto \color{blue}{t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)} \]

   if -5.19999999999999963e72 < t < -4.79999999999999983e-55

   1. Initial program 84.8%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 74.3%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]

   if -4.79999999999999983e-55 < t < 2.7e41

   1. Initial program 87.6%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 78.6%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(i \cdot x\right)\right)} - \left(j \cdot 27\right) \cdot k \]
3. Recombined 3 regimes into one program.

4. Final simplification 75.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -5.2 \cdot 10^{+72}:\\ \;\;\;\;t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \mathbf{elif}\;t \leq -4.8 \cdot 10^{-55}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(t \cdot a\right)\right) - \left(j \cdot 27\right) \cdot k\\ \mathbf{elif}\;t \leq 2.7 \cdot 10^{+41}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(x \cdot i\right)\right) - \left(j \cdot 27\right) \cdot k\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 45.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := b \cdot c + j \cdot \left(k \cdot -27\right)\\ t_2 := a \cdot \left(t \cdot -4\right)\\ \mathbf{if}\;a \leq -5.1 \cdot 10^{+119}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;a \leq -1.8 \cdot 10^{-46}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq -2.3 \cdot 10^{-55}:\\ \;\;\;\;t \cdot \left(\left(x \cdot y\right) \cdot \left(18 \cdot z\right)\right)\\ \mathbf{elif}\;a \leq 1.95 \cdot 10^{+40}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (+ (* b c) (* j (* k -27.0)))) (t_2 (* a (* t -4.0))))
   (if (<= a -5.1e+119)
     t_2
     (if (<= a -1.8e-46)
       t_1
       (if (<= a -2.3e-55)
         (* t (* (* x y) (* 18.0 z)))
         (if (<= a 1.95e+40) t_1 t_2))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (b * c) + (j * (k * -27.0));
	double t_2 = a * (t * -4.0);
	double tmp;
	if (a <= -5.1e+119) {
		tmp = t_2;
	} else if (a <= -1.8e-46) {
		tmp = t_1;
	} else if (a <= -2.3e-55) {
		tmp = t * ((x * y) * (18.0 * z));
	} else if (a <= 1.95e+40) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (b * c) + (j * (k * (-27.0d0)))
    t_2 = a * (t * (-4.0d0))
    if (a <= (-5.1d+119)) then
        tmp = t_2
    else if (a <= (-1.8d-46)) then
        tmp = t_1
    else if (a <= (-2.3d-55)) then
        tmp = t * ((x * y) * (18.0d0 * z))
    else if (a <= 1.95d+40) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (b * c) + (j * (k * -27.0));
	double t_2 = a * (t * -4.0);
	double tmp;
	if (a <= -5.1e+119) {
		tmp = t_2;
	} else if (a <= -1.8e-46) {
		tmp = t_1;
	} else if (a <= -2.3e-55) {
		tmp = t * ((x * y) * (18.0 * z));
	} else if (a <= 1.95e+40) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (b * c) + (j * (k * -27.0))
	t_2 = a * (t * -4.0)
	tmp = 0
	if a <= -5.1e+119:
		tmp = t_2
	elif a <= -1.8e-46:
		tmp = t_1
	elif a <= -2.3e-55:
		tmp = t * ((x * y) * (18.0 * z))
	elif a <= 1.95e+40:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(b * c) + Float64(j * Float64(k * -27.0)))
	t_2 = Float64(a * Float64(t * -4.0))
	tmp = 0.0
	if (a <= -5.1e+119)
		tmp = t_2;
	elseif (a <= -1.8e-46)
		tmp = t_1;
	elseif (a <= -2.3e-55)
		tmp = Float64(t * Float64(Float64(x * y) * Float64(18.0 * z)));
	elseif (a <= 1.95e+40)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (b * c) + (j * (k * -27.0));
	t_2 = a * (t * -4.0);
	tmp = 0.0;
	if (a <= -5.1e+119)
		tmp = t_2;
	elseif (a <= -1.8e-46)
		tmp = t_1;
	elseif (a <= -2.3e-55)
		tmp = t * ((x * y) * (18.0 * z));
	elseif (a <= 1.95e+40)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(b * c), $MachinePrecision] + N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(a * N[(t * -4.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -5.1e+119], t$95$2, If[LessEqual[a, -1.8e-46], t$95$1, If[LessEqual[a, -2.3e-55], N[(t * N[(N[(x * y), $MachinePrecision] * N[(18.0 * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.95e+40], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if a < -5.09999999999999984e119 or 1.95e40 < a

   1. Initial program 79.6%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 84.2%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 84.2%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 84.2%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 86.5%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 86.5%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 86.5%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 86.5%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 86.5%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 86.5%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 86.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 79.9%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. pow1 79.9%

         \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

      2. associate-*r* 79.9%

         \[\leadsto \left(b \cdot c + t \cdot \left({\color{blue}{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}}^{1} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   9. Applied egg-rr 79.9%

      \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
   10. Step-by-step derivation

       1. unpow1 79.9%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(18 \cdot x\right) \cdot \left(y \cdot z\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       2. associate-*r* 79.9%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{18 \cdot \left(x \cdot \left(y \cdot z\right)\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       3. *-commutative 79.9%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot 18} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       4. associate-*r* 82.1%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18 - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       5. associate-*l* 82.1%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   11. Simplified 82.1%

       \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   12. Taylor expanded in a around inf 57.6%

       \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} \]
   13. Step-by-step derivation

       1. associate-*r* 57.6%

          \[\leadsto \color{blue}{\left(-4 \cdot a\right) \cdot t} \]

       2. *-commutative 57.6%

          \[\leadsto \color{blue}{\left(a \cdot -4\right)} \cdot t \]

       3. associate-*l* 57.6%

          \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   14. Simplified 57.6%

       \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   if -5.09999999999999984e119 < a < -1.8e-46 or -2.30000000000000011e-55 < a < 1.95e40

   1. Initial program 87.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 89.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 54.6%

      \[\leadsto \color{blue}{b \cdot c} + j \cdot \left(k \cdot -27\right) \]

   if -1.8e-46 < a < -2.30000000000000011e-55

   1. Initial program 99.7%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 100.0%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 100.0%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 100.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 100.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 100.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 100.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 100.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 100.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 80.0%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]

   8. Taylor expanded in x around inf 80.7%

      \[\leadsto \color{blue}{18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)} \]
   9. Step-by-step derivation

      1. *-commutative 80.7%

         \[\leadsto \color{blue}{\left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right) \cdot 18} \]

      2. associate-*r* 80.7%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot \left(y \cdot z\right)\right) \cdot 18\right)} \]

      3. associate-*r* 99.7%

         \[\leadsto t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18\right) \]

      4. associate-*l* 99.7%

         \[\leadsto t \cdot \color{blue}{\left(\left(x \cdot y\right) \cdot \left(z \cdot 18\right)\right)} \]

   10. Simplified 99.7%

       \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot y\right) \cdot \left(z \cdot 18\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 56.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -5.1 \cdot 10^{+119}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \mathbf{elif}\;a \leq -1.8 \cdot 10^{-46}:\\ \;\;\;\;b \cdot c + j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;a \leq -2.3 \cdot 10^{-55}:\\ \;\;\;\;t \cdot \left(\left(x \cdot y\right) \cdot \left(18 \cdot z\right)\right)\\ \mathbf{elif}\;a \leq 1.95 \cdot 10^{+40}:\\ \;\;\;\;b \cdot c + j \cdot \left(k \cdot -27\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 16: 50.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := b \cdot c + j \cdot \left(k \cdot -27\right)\\ t_2 := b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{if}\;a \leq -1.42 \cdot 10^{+32}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;a \leq -1.8 \cdot 10^{-46}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq -3.7 \cdot 10^{-55}:\\ \;\;\;\;t \cdot \left(\left(x \cdot y\right) \cdot \left(18 \cdot z\right)\right)\\ \mathbf{elif}\;a \leq 3.6 \cdot 10^{+25}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (+ (* b c) (* j (* k -27.0)))) (t_2 (- (* b c) (* 4.0 (* t a)))))
   (if (<= a -1.42e+32)
     t_2
     (if (<= a -1.8e-46)
       t_1
       (if (<= a -3.7e-55)
         (* t (* (* x y) (* 18.0 z)))
         (if (<= a 3.6e+25) t_1 t_2))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = (b * c) + (j * (k * -27.0));
	double t_2 = (b * c) - (4.0 * (t * a));
	double tmp;
	if (a <= -1.42e+32) {
		tmp = t_2;
	} else if (a <= -1.8e-46) {
		tmp = t_1;
	} else if (a <= -3.7e-55) {
		tmp = t * ((x * y) * (18.0 * z));
	} else if (a <= 3.6e+25) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (b * c) + (j * (k * (-27.0d0)))
    t_2 = (b * c) - (4.0d0 * (t * a))
    if (a <= (-1.42d+32)) then
        tmp = t_2
    else if (a <= (-1.8d-46)) then
        tmp = t_1
    else if (a <= (-3.7d-55)) then
        tmp = t * ((x * y) * (18.0d0 * z))
    else if (a <= 3.6d+25) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = (b * c) + (j * (k * -27.0));
	double t_2 = (b * c) - (4.0 * (t * a));
	double tmp;
	if (a <= -1.42e+32) {
		tmp = t_2;
	} else if (a <= -1.8e-46) {
		tmp = t_1;
	} else if (a <= -3.7e-55) {
		tmp = t * ((x * y) * (18.0 * z));
	} else if (a <= 3.6e+25) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = (b * c) + (j * (k * -27.0))
	t_2 = (b * c) - (4.0 * (t * a))
	tmp = 0
	if a <= -1.42e+32:
		tmp = t_2
	elif a <= -1.8e-46:
		tmp = t_1
	elif a <= -3.7e-55:
		tmp = t * ((x * y) * (18.0 * z))
	elif a <= 3.6e+25:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(b * c) + Float64(j * Float64(k * -27.0)))
	t_2 = Float64(Float64(b * c) - Float64(4.0 * Float64(t * a)))
	tmp = 0.0
	if (a <= -1.42e+32)
		tmp = t_2;
	elseif (a <= -1.8e-46)
		tmp = t_1;
	elseif (a <= -3.7e-55)
		tmp = Float64(t * Float64(Float64(x * y) * Float64(18.0 * z)));
	elseif (a <= 3.6e+25)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = (b * c) + (j * (k * -27.0));
	t_2 = (b * c) - (4.0 * (t * a));
	tmp = 0.0;
	if (a <= -1.42e+32)
		tmp = t_2;
	elseif (a <= -1.8e-46)
		tmp = t_1;
	elseif (a <= -3.7e-55)
		tmp = t * ((x * y) * (18.0 * z));
	elseif (a <= 3.6e+25)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(b * c), $MachinePrecision] + N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.42e+32], t$95$2, If[LessEqual[a, -1.8e-46], t$95$1, If[LessEqual[a, -3.7e-55], N[(t * N[(N[(x * y), $MachinePrecision] * N[(18.0 * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 3.6e+25], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if a < -1.41999999999999992e32 or 3.60000000000000015e25 < a

   1. Initial program 81.5%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 66.4%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]

   4. Taylor expanded in j around 0 67.5%

      \[\leadsto \color{blue}{b \cdot c - 4 \cdot \left(a \cdot t\right)} \]

   if -1.41999999999999992e32 < a < -1.8e-46 or -3.69999999999999985e-55 < a < 3.60000000000000015e25

   1. Initial program 87.6%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 88.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 56.5%

      \[\leadsto \color{blue}{b \cdot c} + j \cdot \left(k \cdot -27\right) \]

   if -1.8e-46 < a < -3.69999999999999985e-55

   1. Initial program 99.7%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 100.0%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 100.0%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 100.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 100.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 100.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 100.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 100.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 100.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 80.0%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]

   8. Taylor expanded in x around inf 80.7%

      \[\leadsto \color{blue}{18 \cdot \left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)} \]
   9. Step-by-step derivation

      1. *-commutative 80.7%

         \[\leadsto \color{blue}{\left(t \cdot \left(x \cdot \left(y \cdot z\right)\right)\right) \cdot 18} \]

      2. associate-*r* 80.7%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot \left(y \cdot z\right)\right) \cdot 18\right)} \]

      3. associate-*r* 99.7%

         \[\leadsto t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18\right) \]

      4. associate-*l* 99.7%

         \[\leadsto t \cdot \color{blue}{\left(\left(x \cdot y\right) \cdot \left(z \cdot 18\right)\right)} \]

   10. Simplified 99.7%

       \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot y\right) \cdot \left(z \cdot 18\right)\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 62.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -1.42 \cdot 10^{+32}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{elif}\;a \leq -1.8 \cdot 10^{-46}:\\ \;\;\;\;b \cdot c + j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;a \leq -3.7 \cdot 10^{-55}:\\ \;\;\;\;t \cdot \left(\left(x \cdot y\right) \cdot \left(18 \cdot z\right)\right)\\ \mathbf{elif}\;a \leq 3.6 \cdot 10^{+25}:\\ \;\;\;\;b \cdot c + j \cdot \left(k \cdot -27\right)\\ \mathbf{else}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 17: 50.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := j \cdot \left(k \cdot -27\right)\\ \mathbf{if}\;a \leq -1.1 \cdot 10^{+31}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{elif}\;a \leq -9.5 \cdot 10^{-300}:\\ \;\;\;\;b \cdot c + t\_1\\ \mathbf{elif}\;a \leq 2.4 \cdot 10^{+40}:\\ \;\;\;\;t\_1 + x \cdot \left(i \cdot -4\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (* j (* k -27.0))))
   (if (<= a -1.1e+31)
     (- (* b c) (* 4.0 (* t a)))
     (if (<= a -9.5e-300)
       (+ (* b c) t_1)
       (if (<= a 2.4e+40)
         (+ t_1 (* x (* i -4.0)))
         (* t (- (* 18.0 (* x (* y z))) (* a 4.0))))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = j * (k * -27.0);
	double tmp;
	if (a <= -1.1e+31) {
		tmp = (b * c) - (4.0 * (t * a));
	} else if (a <= -9.5e-300) {
		tmp = (b * c) + t_1;
	} else if (a <= 2.4e+40) {
		tmp = t_1 + (x * (i * -4.0));
	} else {
		tmp = t * ((18.0 * (x * (y * z))) - (a * 4.0));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: tmp
    t_1 = j * (k * (-27.0d0))
    if (a <= (-1.1d+31)) then
        tmp = (b * c) - (4.0d0 * (t * a))
    else if (a <= (-9.5d-300)) then
        tmp = (b * c) + t_1
    else if (a <= 2.4d+40) then
        tmp = t_1 + (x * (i * (-4.0d0)))
    else
        tmp = t * ((18.0d0 * (x * (y * z))) - (a * 4.0d0))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = j * (k * -27.0);
	double tmp;
	if (a <= -1.1e+31) {
		tmp = (b * c) - (4.0 * (t * a));
	} else if (a <= -9.5e-300) {
		tmp = (b * c) + t_1;
	} else if (a <= 2.4e+40) {
		tmp = t_1 + (x * (i * -4.0));
	} else {
		tmp = t * ((18.0 * (x * (y * z))) - (a * 4.0));
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = j * (k * -27.0)
	tmp = 0
	if a <= -1.1e+31:
		tmp = (b * c) - (4.0 * (t * a))
	elif a <= -9.5e-300:
		tmp = (b * c) + t_1
	elif a <= 2.4e+40:
		tmp = t_1 + (x * (i * -4.0))
	else:
		tmp = t * ((18.0 * (x * (y * z))) - (a * 4.0))
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(j * Float64(k * -27.0))
	tmp = 0.0
	if (a <= -1.1e+31)
		tmp = Float64(Float64(b * c) - Float64(4.0 * Float64(t * a)));
	elseif (a <= -9.5e-300)
		tmp = Float64(Float64(b * c) + t_1);
	elseif (a <= 2.4e+40)
		tmp = Float64(t_1 + Float64(x * Float64(i * -4.0)));
	else
		tmp = Float64(t * Float64(Float64(18.0 * Float64(x * Float64(y * z))) - Float64(a * 4.0)));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = j * (k * -27.0);
	tmp = 0.0;
	if (a <= -1.1e+31)
		tmp = (b * c) - (4.0 * (t * a));
	elseif (a <= -9.5e-300)
		tmp = (b * c) + t_1;
	elseif (a <= 2.4e+40)
		tmp = t_1 + (x * (i * -4.0));
	else
		tmp = t * ((18.0 * (x * (y * z))) - (a * 4.0));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.1e+31], N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, -9.5e-300], N[(N[(b * c), $MachinePrecision] + t$95$1), $MachinePrecision], If[LessEqual[a, 2.4e+40], N[(t$95$1 + N[(x * N[(i * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(t * N[(N[(18.0 * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if a < -1.10000000000000005e31

   1. Initial program 82.7%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 72.7%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(a \cdot t\right)\right)} - \left(j \cdot 27\right) \cdot k \]

   4. Taylor expanded in j around 0 74.6%

      \[\leadsto \color{blue}{b \cdot c - 4 \cdot \left(a \cdot t\right)} \]

   if -1.10000000000000005e31 < a < -9.5000000000000007e-300

   1. Initial program 88.8%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 87.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in b around inf 60.2%

      \[\leadsto \color{blue}{b \cdot c} + j \cdot \left(k \cdot -27\right) \]

   if -9.5000000000000007e-300 < a < 2.4e40

   1. Initial program 85.2%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 90.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in i around inf 57.2%

      \[\leadsto \color{blue}{-4 \cdot \left(i \cdot x\right)} + j \cdot \left(k \cdot -27\right) \]
   6. Step-by-step derivation

      1. associate-*r* 57.2%

         \[\leadsto \color{blue}{\left(-4 \cdot i\right) \cdot x} + j \cdot \left(k \cdot -27\right) \]

      2. *-commutative 57.2%

         \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} + j \cdot \left(k \cdot -27\right) \]

   7. Simplified 57.2%

      \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} + j \cdot \left(k \cdot -27\right) \]

   if 2.4e40 < a

   1. Initial program 82.6%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 84.9%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 78.2%

      \[\leadsto \color{blue}{t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 65.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -1.1 \cdot 10^{+31}:\\ \;\;\;\;b \cdot c - 4 \cdot \left(t \cdot a\right)\\ \mathbf{elif}\;a \leq -9.5 \cdot 10^{-300}:\\ \;\;\;\;b \cdot c + j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;a \leq 2.4 \cdot 10^{+40}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right) + x \cdot \left(i \cdot -4\right)\\ \mathbf{else}:\\ \;\;\;\;t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 18: 75.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} \mathbf{if}\;t \leq -5.2 \cdot 10^{-66} \lor \neg \left(t \leq 5.1 \cdot 10^{+39}\right):\\ \;\;\;\;b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(x \cdot i\right)\right) - \left(j \cdot 27\right) \cdot k\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (if (or (<= t -5.2e-66) (not (<= t 5.1e+39)))
   (+ (* b c) (* t (- (* 18.0 (* x (* y z))) (* a 4.0))))
   (- (- (* b c) (* 4.0 (* x i))) (* (* j 27.0) k))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double tmp;
	if ((t <= -5.2e-66) || !(t <= 5.1e+39)) {
		tmp = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	} else {
		tmp = ((b * c) - (4.0 * (x * i))) - ((j * 27.0) * k);
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: tmp
    if ((t <= (-5.2d-66)) .or. (.not. (t <= 5.1d+39))) then
        tmp = (b * c) + (t * ((18.0d0 * (x * (y * z))) - (a * 4.0d0)))
    else
        tmp = ((b * c) - (4.0d0 * (x * i))) - ((j * 27.0d0) * k)
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 tmp;
	if ((t <= -5.2e-66) || !(t <= 5.1e+39)) {
		tmp = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	} else {
		tmp = ((b * c) - (4.0 * (x * i))) - ((j * 27.0) * k);
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	tmp = 0
	if (t <= -5.2e-66) or not (t <= 5.1e+39):
		tmp = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)))
	else:
		tmp = ((b * c) - (4.0 * (x * i))) - ((j * 27.0) * k)
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0
	if ((t <= -5.2e-66) || !(t <= 5.1e+39))
		tmp = Float64(Float64(b * c) + Float64(t * Float64(Float64(18.0 * Float64(x * Float64(y * z))) - Float64(a * 4.0))));
	else
		tmp = Float64(Float64(Float64(b * c) - Float64(4.0 * Float64(x * i))) - Float64(Float64(j * 27.0) * k));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0;
	if ((t <= -5.2e-66) || ~((t <= 5.1e+39)))
		tmp = (b * c) + (t * ((18.0 * (x * (y * z))) - (a * 4.0)));
	else
		tmp = ((b * c) - (4.0 * (x * i))) - ((j * 27.0) * k);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := If[Or[LessEqual[t, -5.2e-66], N[Not[LessEqual[t, 5.1e+39]], $MachinePrecision]], N[(N[(b * c), $MachinePrecision] + N[(t * N[(N[(18.0 * N[(x * N[(y * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(a * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(b * c), $MachinePrecision] - N[(4.0 * N[(x * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(j * 27.0), $MachinePrecision] * k), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < -5.1999999999999998e-66 or 5.0999999999999998e39 < t

   1. Initial program 83.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 87.9%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 87.9%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 87.9%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 90.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 90.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 90.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 90.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 90.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 90.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 90.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 84.9%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]

   8. Taylor expanded in j around 0 76.2%

      \[\leadsto \color{blue}{b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)} \]

   if -5.1999999999999998e-66 < t < 5.0999999999999998e39

   1. Initial program 87.2%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 80.5%

      \[\leadsto \color{blue}{\left(b \cdot c - 4 \cdot \left(i \cdot x\right)\right)} - \left(j \cdot 27\right) \cdot k \]
3. Recombined 2 regimes into one program.

4. Final simplification 78.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -5.2 \cdot 10^{-66} \lor \neg \left(t \leq 5.1 \cdot 10^{+39}\right):\\ \;\;\;\;b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - a \cdot 4\right)\\ \mathbf{else}:\\ \;\;\;\;\left(b \cdot c - 4 \cdot \left(x \cdot i\right)\right) - \left(j \cdot 27\right) \cdot k\\ \end{array} \]
5. Add Preprocessing

Alternative 19: 30.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} t_1 := a \cdot \left(t \cdot -4\right)\\ \mathbf{if}\;a \leq -3.1 \cdot 10^{+118}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq -4.5 \cdot 10^{-167}:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;a \leq 1.18 \cdot 10^{-225}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;a \leq 2.2 \cdot 10^{+40}:\\ \;\;\;\;x \cdot \left(i \cdot -4\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (* a (* t -4.0))))
   (if (<= a -3.1e+118)
     t_1
     (if (<= a -4.5e-167)
       (* b c)
       (if (<= a 1.18e-225)
         (* j (* k -27.0))
         (if (<= a 2.2e+40) (* x (* i -4.0)) t_1))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double t_1 = a * (t * -4.0);
	double tmp;
	if (a <= -3.1e+118) {
		tmp = t_1;
	} else if (a <= -4.5e-167) {
		tmp = b * c;
	} else if (a <= 1.18e-225) {
		tmp = j * (k * -27.0);
	} else if (a <= 2.2e+40) {
		tmp = x * (i * -4.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: tmp
    t_1 = a * (t * (-4.0d0))
    if (a <= (-3.1d+118)) then
        tmp = t_1
    else if (a <= (-4.5d-167)) then
        tmp = b * c
    else if (a <= 1.18d-225) then
        tmp = j * (k * (-27.0d0))
    else if (a <= 2.2d+40) then
        tmp = x * (i * (-4.0d0))
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 t_1 = a * (t * -4.0);
	double tmp;
	if (a <= -3.1e+118) {
		tmp = t_1;
	} else if (a <= -4.5e-167) {
		tmp = b * c;
	} else if (a <= 1.18e-225) {
		tmp = j * (k * -27.0);
	} else if (a <= 2.2e+40) {
		tmp = x * (i * -4.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = a * (t * -4.0)
	tmp = 0
	if a <= -3.1e+118:
		tmp = t_1
	elif a <= -4.5e-167:
		tmp = b * c
	elif a <= 1.18e-225:
		tmp = j * (k * -27.0)
	elif a <= 2.2e+40:
		tmp = x * (i * -4.0)
	else:
		tmp = t_1
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(a * Float64(t * -4.0))
	tmp = 0.0
	if (a <= -3.1e+118)
		tmp = t_1;
	elseif (a <= -4.5e-167)
		tmp = Float64(b * c);
	elseif (a <= 1.18e-225)
		tmp = Float64(j * Float64(k * -27.0));
	elseif (a <= 2.2e+40)
		tmp = Float64(x * Float64(i * -4.0));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = a * (t * -4.0);
	tmp = 0.0;
	if (a <= -3.1e+118)
		tmp = t_1;
	elseif (a <= -4.5e-167)
		tmp = b * c;
	elseif (a <= 1.18e-225)
		tmp = j * (k * -27.0);
	elseif (a <= 2.2e+40)
		tmp = x * (i * -4.0);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(a * N[(t * -4.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -3.1e+118], t$95$1, If[LessEqual[a, -4.5e-167], N[(b * c), $MachinePrecision], If[LessEqual[a, 1.18e-225], N[(j * N[(k * -27.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 2.2e+40], N[(x * N[(i * -4.0), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if a < -3.09999999999999986e118 or 2.1999999999999999e40 < a

   1. Initial program 80.4%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 84.0%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 84.0%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 84.0%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 86.3%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 86.3%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 86.3%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 86.3%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 86.3%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 86.3%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 86.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 80.8%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. pow1 80.8%

         \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

      2. associate-*r* 80.8%

         \[\leadsto \left(b \cdot c + t \cdot \left({\color{blue}{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}}^{1} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   9. Applied egg-rr 80.8%

      \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
   10. Step-by-step derivation

       1. unpow1 80.8%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(18 \cdot x\right) \cdot \left(y \cdot z\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       2. associate-*r* 80.8%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{18 \cdot \left(x \cdot \left(y \cdot z\right)\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       3. *-commutative 80.8%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot 18} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       4. associate-*r* 83.0%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18 - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       5. associate-*l* 82.9%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   11. Simplified 82.9%

       \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   12. Taylor expanded in a around inf 58.2%

       \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} \]
   13. Step-by-step derivation

       1. associate-*r* 58.2%

          \[\leadsto \color{blue}{\left(-4 \cdot a\right) \cdot t} \]

       2. *-commutative 58.2%

          \[\leadsto \color{blue}{\left(a \cdot -4\right)} \cdot t \]

       3. associate-*l* 58.2%

          \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   14. Simplified 58.2%

       \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   if -3.09999999999999986e118 < a < -4.5000000000000001e-167

   1. Initial program 89.4%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 89.5%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 89.5%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 89.5%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 91.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 91.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 91.0%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 92.5%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 92.5%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 92.5%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 92.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in b around inf 36.2%

      \[\leadsto \color{blue}{b \cdot c} \]

   if -4.5000000000000001e-167 < a < 1.18e-225

   1. Initial program 86.5%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 86.3%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 86.3%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 86.3%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 86.4%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 86.4%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 86.4%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 86.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in j around inf 42.7%

      \[\leadsto \color{blue}{-27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. *-commutative 42.7%

         \[\leadsto \color{blue}{\left(j \cdot k\right) \cdot -27} \]

      2. associate-*r* 42.7%

         \[\leadsto \color{blue}{j \cdot \left(k \cdot -27\right)} \]

   9. Simplified 42.7%

      \[\leadsto \color{blue}{j \cdot \left(k \cdot -27\right)} \]

   if 1.18e-225 < a < 2.1999999999999999e40

   1. Initial program 87.0%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 88.2%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 88.2%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 88.2%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 88.2%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 88.2%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 88.2%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 88.2%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 88.2%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 88.2%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 88.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around inf 37.3%

      \[\leadsto \color{blue}{-4 \cdot \left(i \cdot x\right)} \]
   8. Step-by-step derivation

      1. associate-*r* 37.3%

         \[\leadsto \color{blue}{\left(-4 \cdot i\right) \cdot x} \]

      2. *-commutative 37.3%

         \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} \]

   9. Simplified 37.3%

      \[\leadsto \color{blue}{x \cdot \left(-4 \cdot i\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 45.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -3.1 \cdot 10^{+118}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \mathbf{elif}\;a \leq -4.5 \cdot 10^{-167}:\\ \;\;\;\;b \cdot c\\ \mathbf{elif}\;a \leq 1.18 \cdot 10^{-225}:\\ \;\;\;\;j \cdot \left(k \cdot -27\right)\\ \mathbf{elif}\;a \leq 2.2 \cdot 10^{+40}:\\ \;\;\;\;x \cdot \left(i \cdot -4\right)\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 20: 36.4% accurate, 1.6× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} \mathbf{if}\;b \cdot c \leq -4.7 \cdot 10^{+43} \lor \neg \left(b \cdot c \leq 1.15 \cdot 10^{+190}\right):\\ \;\;\;\;b \cdot c\\ \mathbf{else}:\\ \;\;\;\;\left(j \cdot k\right) \cdot -27\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (if (or (<= (* b c) -4.7e+43) (not (<= (* b c) 1.15e+190)))
   (* b c)
   (* (* j k) -27.0)))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double tmp;
	if (((b * c) <= -4.7e+43) || !((b * c) <= 1.15e+190)) {
		tmp = b * c;
	} else {
		tmp = (j * k) * -27.0;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: tmp
    if (((b * c) <= (-4.7d+43)) .or. (.not. ((b * c) <= 1.15d+190))) then
        tmp = b * c
    else
        tmp = (j * k) * (-27.0d0)
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 tmp;
	if (((b * c) <= -4.7e+43) || !((b * c) <= 1.15e+190)) {
		tmp = b * c;
	} else {
		tmp = (j * k) * -27.0;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	tmp = 0
	if ((b * c) <= -4.7e+43) or not ((b * c) <= 1.15e+190):
		tmp = b * c
	else:
		tmp = (j * k) * -27.0
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0
	if ((Float64(b * c) <= -4.7e+43) || !(Float64(b * c) <= 1.15e+190))
		tmp = Float64(b * c);
	else
		tmp = Float64(Float64(j * k) * -27.0);
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0;
	if (((b * c) <= -4.7e+43) || ~(((b * c) <= 1.15e+190)))
		tmp = b * c;
	else
		tmp = (j * k) * -27.0;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := If[Or[LessEqual[N[(b * c), $MachinePrecision], -4.7e+43], N[Not[LessEqual[N[(b * c), $MachinePrecision], 1.15e+190]], $MachinePrecision]], N[(b * c), $MachinePrecision], N[(N[(j * k), $MachinePrecision] * -27.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 b c) < -4.69999999999999998e43 or 1.15e190 < (*.f64 b c)

   1. Initial program 83.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 85.1%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 85.1%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 85.1%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 86.2%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 86.2%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 86.2%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 86.2%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 86.2%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 86.2%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 86.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in b around inf 60.9%

      \[\leadsto \color{blue}{b \cdot c} \]

   if -4.69999999999999998e43 < (*.f64 b c) < 1.15e190

   1. Initial program 86.1%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 88.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t, \mathsf{fma}\left(x, 18 \cdot \left(y \cdot z\right), a \cdot -4\right), \mathsf{fma}\left(b, c, x \cdot \left(i \cdot -4\right)\right)\right) + j \cdot \left(k \cdot -27\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in j around inf 25.1%

      \[\leadsto \color{blue}{-27 \cdot \left(j \cdot k\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 38.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \cdot c \leq -4.7 \cdot 10^{+43} \lor \neg \left(b \cdot c \leq 1.15 \cdot 10^{+190}\right):\\ \;\;\;\;b \cdot c\\ \mathbf{else}:\\ \;\;\;\;\left(j \cdot k\right) \cdot -27\\ \end{array} \]
5. Add Preprocessing

Alternative 21: 35.4% accurate, 1.6× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ \begin{array}{l} \mathbf{if}\;b \cdot c \leq -1.36 \cdot 10^{+14} \lor \neg \left(b \cdot c \leq 3 \cdot 10^{+112}\right):\\ \;\;\;\;b \cdot c\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k)
 :precision binary64
 (if (or (<= (* b c) -1.36e+14) (not (<= (* b c) 3e+112)))
   (* b c)
   (* a (* t -4.0))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	double tmp;
	if (((b * c) <= -1.36e+14) || !((b * c) <= 3e+112)) {
		tmp = b * c;
	} else {
		tmp = a * (t * -4.0);
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: tmp
    if (((b * c) <= (-1.36d+14)) .or. (.not. ((b * c) <= 3d+112))) then
        tmp = b * c
    else
        tmp = a * (t * (-4.0d0))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
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 tmp;
	if (((b * c) <= -1.36e+14) || !((b * c) <= 3e+112)) {
		tmp = b * c;
	} else {
		tmp = a * (t * -4.0);
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	tmp = 0
	if ((b * c) <= -1.36e+14) or not ((b * c) <= 3e+112):
		tmp = b * c
	else:
		tmp = a * (t * -4.0)
	return tmp

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0
	if ((Float64(b * c) <= -1.36e+14) || !(Float64(b * c) <= 3e+112))
		tmp = Float64(b * c);
	else
		tmp = Float64(a * Float64(t * -4.0));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	tmp = 0.0;
	if (((b * c) <= -1.36e+14) || ~(((b * c) <= 3e+112)))
		tmp = b * c;
	else
		tmp = a * (t * -4.0);
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := If[Or[LessEqual[N[(b * c), $MachinePrecision], -1.36e+14], N[Not[LessEqual[N[(b * c), $MachinePrecision], 3e+112]], $MachinePrecision]], N[(b * c), $MachinePrecision], N[(a * N[(t * -4.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 b c) < -1.36e14 or 2.99999999999999979e112 < (*.f64 b c)

   1. Initial program 84.9%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 86.7%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 86.7%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 86.7%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 87.6%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 87.6%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 87.6%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 87.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 87.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 87.6%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 87.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in b around inf 54.5%

      \[\leadsto \color{blue}{b \cdot c} \]

   if -1.36e14 < (*.f64 b c) < 2.99999999999999979e112

   1. Initial program 85.5%

      \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

   3. Simplified 86.9%

      \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. associate--l+ 86.9%

         \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      2. *-commutative 86.9%

         \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      3. fma-define 88.3%

         \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

      4. *-commutative 88.3%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      5. fma-neg 88.3%

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

      6. fma-define 89.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

      7. *-commutative 89.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

      8. *-commutative 89.0%

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

   6. Applied egg-rr 89.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

   7. Taylor expanded in i around 0 76.2%

      \[\leadsto \color{blue}{\left(b \cdot c + t \cdot \left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right) - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right)} \]
   8. Step-by-step derivation

      1. pow1 76.2%

         \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(18 \cdot \left(x \cdot \left(y \cdot z\right)\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

      2. associate-*r* 76.2%

         \[\leadsto \left(b \cdot c + t \cdot \left({\color{blue}{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}}^{1} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   9. Applied egg-rr 76.2%

      \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{{\left(\left(18 \cdot x\right) \cdot \left(y \cdot z\right)\right)}^{1}} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]
   10. Step-by-step derivation

       1. unpow1 76.2%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(18 \cdot x\right) \cdot \left(y \cdot z\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       2. associate-*r* 76.2%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{18 \cdot \left(x \cdot \left(y \cdot z\right)\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       3. *-commutative 76.2%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot \left(y \cdot z\right)\right) \cdot 18} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       4. associate-*r* 77.6%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(\left(x \cdot y\right) \cdot z\right)} \cdot 18 - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

       5. associate-*l* 77.6%

          \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   11. Simplified 77.6%

       \[\leadsto \left(b \cdot c + t \cdot \left(\color{blue}{\left(x \cdot y\right) \cdot \left(z \cdot 18\right)} - 4 \cdot a\right)\right) - 27 \cdot \left(j \cdot k\right) \]

   12. Taylor expanded in a around inf 33.3%

       \[\leadsto \color{blue}{-4 \cdot \left(a \cdot t\right)} \]
   13. Step-by-step derivation

       1. associate-*r* 33.3%

          \[\leadsto \color{blue}{\left(-4 \cdot a\right) \cdot t} \]

       2. *-commutative 33.3%

          \[\leadsto \color{blue}{\left(a \cdot -4\right)} \cdot t \]

       3. associate-*l* 34.0%

          \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]

   14. Simplified 34.0%

       \[\leadsto \color{blue}{a \cdot \left(-4 \cdot t\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 43.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \cdot c \leq -1.36 \cdot 10^{+14} \lor \neg \left(b \cdot c \leq 3 \cdot 10^{+112}\right):\\ \;\;\;\;b \cdot c\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(t \cdot -4\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 22: 23.5% accurate, 10.3× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c, i, j, k] = \mathsf{sort}([x, y, z, t, a, b, c, i, j, k])\\ \\ b \cdot c \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c i j k) :precision binary64 (* b c))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k);
double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	return b * c;
}

Fortran

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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
    code = b * c
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c && c < i && i < j && j < k;
public static double code(double x, double y, double z, double t, double a, double b, double c, double i, double j, double k) {
	return b * c;
}

Python

[x, y, z, t, a, b, c, i, j, k] = sort([x, y, z, t, a, b, c, i, j, k])
def code(x, y, z, t, a, b, c, i, j, k):
	return b * c

Julia

x, y, z, t, a, b, c, i, j, k = sort([x, y, z, t, a, b, c, i, j, k])
function code(x, y, z, t, a, b, c, i, j, k)
	return Float64(b * c)
end

MATLAB

x, y, z, t, a, b, c, i, j, k = num2cell(sort([x, y, z, t, a, b, c, i, j, k])){:}
function tmp = code(x, y, z, t, a, b, c, i, j, k)
	tmp = b * c;
end

Wolfram

NOTE: x, y, z, t, a, b, c, i, j, and k should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := N[(b * c), $MachinePrecision]

Derivation

1. Initial program 85.3%

   \[\left(\left(\left(\left(\left(\left(x \cdot 18\right) \cdot y\right) \cdot z\right) \cdot t - \left(a \cdot 4\right) \cdot t\right) + b \cdot c\right) - \left(x \cdot 4\right) \cdot i\right) - \left(j \cdot 27\right) \cdot k \]

3. Simplified 86.8%

   \[\leadsto \color{blue}{\left(t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + b \cdot c\right) - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. associate--l+ 86.8%

      \[\leadsto \color{blue}{t \cdot \left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

   2. *-commutative 86.8%

      \[\leadsto \color{blue}{\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4\right) \cdot t} + \left(b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

   3. fma-define 88.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot 18\right) \cdot \left(y \cdot z\right) - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right)} \]

   4. *-commutative 88.0%

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(y \cdot z\right) \cdot \left(x \cdot 18\right)} - a \cdot 4, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

   5. fma-neg 88.0%

      \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right)}, t, b \cdot c - \left(x \cdot \left(4 \cdot i\right) + j \cdot \left(27 \cdot k\right)\right)\right) \]

   6. fma-define 88.4%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \color{blue}{\mathsf{fma}\left(x, 4 \cdot i, j \cdot \left(27 \cdot k\right)\right)}\right) \]

   7. *-commutative 88.4%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, \color{blue}{i \cdot 4}, j \cdot \left(27 \cdot k\right)\right)\right) \]

   8. *-commutative 88.4%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \color{blue}{\left(k \cdot 27\right)}\right)\right) \]

6. Applied egg-rr 88.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(y \cdot z, x \cdot 18, -a \cdot 4\right), t, b \cdot c - \mathsf{fma}\left(x, i \cdot 4, j \cdot \left(k \cdot 27\right)\right)\right)} \]

7. Taylor expanded in b around inf 25.9%

   \[\leadsto \color{blue}{b \cdot c} \]

8. Final simplification 25.9%

   \[\leadsto b \cdot c \]
9. Add Preprocessing

Developer target: 89.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a \cdot t + i \cdot x\right) \cdot 4\\ t_2 := \left(\left(18 \cdot t\right) \cdot \left(\left(x \cdot y\right) \cdot z\right) - t\_1\right) - \left(\left(k \cdot j\right) \cdot 27 - c \cdot b\right)\\ \mathbf{if}\;t < -1.6210815397541398 \cdot 10^{-69}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t < 165.68027943805222:\\ \;\;\;\;\left(\left(18 \cdot y\right) \cdot \left(x \cdot \left(z \cdot t\right)\right) - t\_1\right) + \left(c \cdot b - 27 \cdot \left(k \cdot j\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c i j k)
 :precision binary64
 (let* ((t_1 (* (+ (* a t) (* i x)) 4.0))
        (t_2
         (-
          (- (* (* 18.0 t) (* (* x y) z)) t_1)
          (- (* (* k j) 27.0) (* c b)))))
   (if (< t -1.6210815397541398e-69)
     t_2
     (if (< t 165.68027943805222)
       (+ (- (* (* 18.0 y) (* x (* z t))) t_1) (- (* c b) (* 27.0 (* k j))))
       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 t_1 = ((a * t) + (i * x)) * 4.0;
	double t_2 = (((18.0 * t) * ((x * y) * z)) - t_1) - (((k * j) * 27.0) - (c * b));
	double tmp;
	if (t < -1.6210815397541398e-69) {
		tmp = t_2;
	} else if (t < 165.68027943805222) {
		tmp = (((18.0 * y) * (x * (z * t))) - t_1) + ((c * b) - (27.0 * (k * j)));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c, i, j, k)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = ((a * t) + (i * x)) * 4.0d0
    t_2 = (((18.0d0 * t) * ((x * y) * z)) - t_1) - (((k * j) * 27.0d0) - (c * b))
    if (t < (-1.6210815397541398d-69)) then
        tmp = t_2
    else if (t < 165.68027943805222d0) then
        tmp = (((18.0d0 * y) * (x * (z * t))) - t_1) + ((c * b) - (27.0d0 * (k * j)))
    else
        tmp = 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 t_1 = ((a * t) + (i * x)) * 4.0;
	double t_2 = (((18.0 * t) * ((x * y) * z)) - t_1) - (((k * j) * 27.0) - (c * b));
	double tmp;
	if (t < -1.6210815397541398e-69) {
		tmp = t_2;
	} else if (t < 165.68027943805222) {
		tmp = (((18.0 * y) * (x * (z * t))) - t_1) + ((c * b) - (27.0 * (k * j)));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c, i, j, k):
	t_1 = ((a * t) + (i * x)) * 4.0
	t_2 = (((18.0 * t) * ((x * y) * z)) - t_1) - (((k * j) * 27.0) - (c * b))
	tmp = 0
	if t < -1.6210815397541398e-69:
		tmp = t_2
	elif t < 165.68027943805222:
		tmp = (((18.0 * y) * (x * (z * t))) - t_1) + ((c * b) - (27.0 * (k * j)))
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c, i, j, k)
	t_1 = Float64(Float64(Float64(a * t) + Float64(i * x)) * 4.0)
	t_2 = Float64(Float64(Float64(Float64(18.0 * t) * Float64(Float64(x * y) * z)) - t_1) - Float64(Float64(Float64(k * j) * 27.0) - Float64(c * b)))
	tmp = 0.0
	if (t < -1.6210815397541398e-69)
		tmp = t_2;
	elseif (t < 165.68027943805222)
		tmp = Float64(Float64(Float64(Float64(18.0 * y) * Float64(x * Float64(z * t))) - t_1) + Float64(Float64(c * b) - Float64(27.0 * Float64(k * j))));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c, i, j, k)
	t_1 = ((a * t) + (i * x)) * 4.0;
	t_2 = (((18.0 * t) * ((x * y) * z)) - t_1) - (((k * j) * 27.0) - (c * b));
	tmp = 0.0;
	if (t < -1.6210815397541398e-69)
		tmp = t_2;
	elseif (t < 165.68027943805222)
		tmp = (((18.0 * y) * (x * (z * t))) - t_1) + ((c * b) - (27.0 * (k * j)));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_, i_, j_, k_] := Block[{t$95$1 = N[(N[(N[(a * t), $MachinePrecision] + N[(i * x), $MachinePrecision]), $MachinePrecision] * 4.0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[(18.0 * t), $MachinePrecision] * N[(N[(x * y), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision] - N[(N[(N[(k * j), $MachinePrecision] * 27.0), $MachinePrecision] - N[(c * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[t, -1.6210815397541398e-69], t$95$2, If[Less[t, 165.68027943805222], N[(N[(N[(N[(18.0 * y), $MachinePrecision] * N[(x * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision] + N[(N[(c * b), $MachinePrecision] - N[(27.0 * N[(k * j), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]

Reproduce

herbie shell --seed 2024043 
(FPCore (x y z t a b c i j k)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, E"
  :precision binary64

  :alt
  (if (< t -1.6210815397541398e-69) (- (- (* (* 18.0 t) (* (* x y) z)) (* (+ (* a t) (* i x)) 4.0)) (- (* (* k j) 27.0) (* c b))) (if (< t 165.68027943805222) (+ (- (* (* 18.0 y) (* x (* z t))) (* (+ (* a t) (* i x)) 4.0)) (- (* c b) (* 27.0 (* k j)))) (- (- (* (* 18.0 t) (* (* x y) z)) (* (+ (* a t) (* i x)) 4.0)) (- (* (* k j) 27.0) (* c b)))))

  (- (- (+ (- (* (* (* (* x 18.0) y) z) t) (* (* a 4.0) t)) (* b c)) (* (* x 4.0) i)) (* (* j 27.0) k)))