Diagrams.Solve.Polynomial:quartForm from diagrams-solve-0.1, C

Percentage Accurate: 97.6% → 98.8%

Time: 10.1s

Alternatives: 14

Speedup: 1.0×

• 32.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (+ (- (+ (* x y) (/ (* z t) 16.0)) (/ (* a b) 4.0)) c))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = (((x * y) + ((z * t) / 16.0d0)) - ((a * b) / 4.0d0)) + c
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
}

Python

def code(x, y, z, t, a, b, c):
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c

Julia

function code(x, y, z, t, a, b, c)
	return Float64(Float64(Float64(Float64(x * y) + Float64(Float64(z * t) / 16.0)) - Float64(Float64(a * b) / 4.0)) + c)
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(N[(N[(x * y), $MachinePrecision] + N[(N[(z * t), $MachinePrecision] / 16.0), $MachinePrecision]), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] / 4.0), $MachinePrecision]), $MachinePrecision] + c), $MachinePrecision]

Initial Program: 97.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (+ (- (+ (* x y) (/ (* z t) 16.0)) (/ (* a b) 4.0)) c))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = (((x * y) + ((z * t) / 16.0d0)) - ((a * b) / 4.0d0)) + c
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
}

Python

def code(x, y, z, t, a, b, c):
	return (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c

Julia

function code(x, y, z, t, a, b, c)
	return Float64(Float64(Float64(Float64(x * y) + Float64(Float64(z * t) / 16.0)) - Float64(Float64(a * b) / 4.0)) + c)
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0)) + c;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(N[(N[(x * y), $MachinePrecision] + N[(N[(z * t), $MachinePrecision] / 16.0), $MachinePrecision]), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] / 4.0), $MachinePrecision]), $MachinePrecision] + c), $MachinePrecision]

Alternative 1: 98.8% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, y, \mathsf{fma}\left(z, \frac{t}{16}, \frac{a \cdot b}{-4}\right)\right) + c \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (+ (fma x y (fma z (/ t 16.0) (/ (* a b) -4.0))) c))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return fma(x, y, fma(z, (t / 16.0), ((a * b) / -4.0))) + c;
}

Julia

function code(x, y, z, t, a, b, c)
	return Float64(fma(x, y, fma(z, Float64(t / 16.0), Float64(Float64(a * b) / -4.0))) + c)
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(x * y + N[(z * N[(t / 16.0), $MachinePrecision] + N[(N[(a * b), $MachinePrecision] / -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + c), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
2. Step-by-step derivation

   1. associate--l+ 99.6%

      \[\leadsto \color{blue}{\left(x \cdot y + \left(\frac{z \cdot t}{16} - \frac{a \cdot b}{4}\right)\right)} + c \]

   2. fma-define 99.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, y, \frac{z \cdot t}{16} - \frac{a \cdot b}{4}\right)} + c \]

   3. associate-/l* 99.6%

      \[\leadsto \mathsf{fma}\left(x, y, \color{blue}{z \cdot \frac{t}{16}} - \frac{a \cdot b}{4}\right) + c \]

   4. fma-neg 100.0%

      \[\leadsto \mathsf{fma}\left(x, y, \color{blue}{\mathsf{fma}\left(z, \frac{t}{16}, -\frac{a \cdot b}{4}\right)}\right) + c \]

   5. distribute-neg-frac2 100.0%

      \[\leadsto \mathsf{fma}\left(x, y, \mathsf{fma}\left(z, \frac{t}{16}, \color{blue}{\frac{a \cdot b}{-4}}\right)\right) + c \]

   6. metadata-eval 100.0%

      \[\leadsto \mathsf{fma}\left(x, y, \mathsf{fma}\left(z, \frac{t}{16}, \frac{a \cdot b}{\color{blue}{-4}}\right)\right) + c \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, y, \mathsf{fma}\left(z, \frac{t}{16}, \frac{a \cdot b}{-4}\right)\right) + c} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 2: 43.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(a \cdot -0.25\right)\\ t_2 := z \cdot \left(t \cdot 0.0625\right)\\ \mathbf{if}\;x \cdot y \leq -1.9 \cdot 10^{+74}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \cdot y \leq -6.5 \cdot 10^{+30}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \cdot y \leq -2.8 \cdot 10^{-81}:\\ \;\;\;\;c\\ \mathbf{elif}\;x \cdot y \leq -4.6 \cdot 10^{-271}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \cdot y \leq 2.2 \cdot 10^{-193}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;x \cdot y \leq 6.9 \cdot 10^{-37}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \cdot y \leq 2.2 \cdot 10^{-14}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* b (* a -0.25))) (t_2 (* z (* t 0.0625))))
   (if (<= (* x y) -1.9e+74)
     (* x y)
     (if (<= (* x y) -6.5e+30)
       t_1
       (if (<= (* x y) -2.8e-81)
         c
         (if (<= (* x y) -4.6e-271)
           t_1
           (if (<= (* x y) 2.2e-193)
             t_2
             (if (<= (* x y) 6.9e-37)
               t_1
               (if (<= (* x y) 2.2e-14) t_2 (* x y))))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = b * (a * -0.25);
	double t_2 = z * (t * 0.0625);
	double tmp;
	if ((x * y) <= -1.9e+74) {
		tmp = x * y;
	} else if ((x * y) <= -6.5e+30) {
		tmp = t_1;
	} else if ((x * y) <= -2.8e-81) {
		tmp = c;
	} else if ((x * y) <= -4.6e-271) {
		tmp = t_1;
	} else if ((x * y) <= 2.2e-193) {
		tmp = t_2;
	} else if ((x * y) <= 6.9e-37) {
		tmp = t_1;
	} else if ((x * y) <= 2.2e-14) {
		tmp = t_2;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = b * (a * (-0.25d0))
    t_2 = z * (t * 0.0625d0)
    if ((x * y) <= (-1.9d+74)) then
        tmp = x * y
    else if ((x * y) <= (-6.5d+30)) then
        tmp = t_1
    else if ((x * y) <= (-2.8d-81)) then
        tmp = c
    else if ((x * y) <= (-4.6d-271)) then
        tmp = t_1
    else if ((x * y) <= 2.2d-193) then
        tmp = t_2
    else if ((x * y) <= 6.9d-37) then
        tmp = t_1
    else if ((x * y) <= 2.2d-14) then
        tmp = t_2
    else
        tmp = x * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = b * (a * -0.25);
	double t_2 = z * (t * 0.0625);
	double tmp;
	if ((x * y) <= -1.9e+74) {
		tmp = x * y;
	} else if ((x * y) <= -6.5e+30) {
		tmp = t_1;
	} else if ((x * y) <= -2.8e-81) {
		tmp = c;
	} else if ((x * y) <= -4.6e-271) {
		tmp = t_1;
	} else if ((x * y) <= 2.2e-193) {
		tmp = t_2;
	} else if ((x * y) <= 6.9e-37) {
		tmp = t_1;
	} else if ((x * y) <= 2.2e-14) {
		tmp = t_2;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = b * (a * -0.25)
	t_2 = z * (t * 0.0625)
	tmp = 0
	if (x * y) <= -1.9e+74:
		tmp = x * y
	elif (x * y) <= -6.5e+30:
		tmp = t_1
	elif (x * y) <= -2.8e-81:
		tmp = c
	elif (x * y) <= -4.6e-271:
		tmp = t_1
	elif (x * y) <= 2.2e-193:
		tmp = t_2
	elif (x * y) <= 6.9e-37:
		tmp = t_1
	elif (x * y) <= 2.2e-14:
		tmp = t_2
	else:
		tmp = x * y
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(b * Float64(a * -0.25))
	t_2 = Float64(z * Float64(t * 0.0625))
	tmp = 0.0
	if (Float64(x * y) <= -1.9e+74)
		tmp = Float64(x * y);
	elseif (Float64(x * y) <= -6.5e+30)
		tmp = t_1;
	elseif (Float64(x * y) <= -2.8e-81)
		tmp = c;
	elseif (Float64(x * y) <= -4.6e-271)
		tmp = t_1;
	elseif (Float64(x * y) <= 2.2e-193)
		tmp = t_2;
	elseif (Float64(x * y) <= 6.9e-37)
		tmp = t_1;
	elseif (Float64(x * y) <= 2.2e-14)
		tmp = t_2;
	else
		tmp = Float64(x * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = b * (a * -0.25);
	t_2 = z * (t * 0.0625);
	tmp = 0.0;
	if ((x * y) <= -1.9e+74)
		tmp = x * y;
	elseif ((x * y) <= -6.5e+30)
		tmp = t_1;
	elseif ((x * y) <= -2.8e-81)
		tmp = c;
	elseif ((x * y) <= -4.6e-271)
		tmp = t_1;
	elseif ((x * y) <= 2.2e-193)
		tmp = t_2;
	elseif ((x * y) <= 6.9e-37)
		tmp = t_1;
	elseif ((x * y) <= 2.2e-14)
		tmp = t_2;
	else
		tmp = x * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(z * N[(t * 0.0625), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x * y), $MachinePrecision], -1.9e+74], N[(x * y), $MachinePrecision], If[LessEqual[N[(x * y), $MachinePrecision], -6.5e+30], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], -2.8e-81], c, If[LessEqual[N[(x * y), $MachinePrecision], -4.6e-271], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], 2.2e-193], t$95$2, If[LessEqual[N[(x * y), $MachinePrecision], 6.9e-37], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], 2.2e-14], t$95$2, N[(x * y), $MachinePrecision]]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 x y) < -1.8999999999999999e74 or 2.2000000000000001e-14 < (*.f64 x y)

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 73.0%

      \[\leadsto \color{blue}{x \cdot y} + c \]

   4. Taylor expanded in x around inf 66.7%

      \[\leadsto \color{blue}{x \cdot y} \]

   if -1.8999999999999999e74 < (*.f64 x y) < -6.5e30 or -2.7999999999999999e-81 < (*.f64 x y) < -4.60000000000000017e-271 or 2.19999999999999977e-193 < (*.f64 x y) < 6.8999999999999999e-37

   1. Initial program 98.5%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 97.1%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in a around inf 53.1%

      \[\leadsto \color{blue}{-0.25 \cdot \left(a \cdot b\right)} \]
   5. Step-by-step derivation

      1. associate-*r* 53.1%

         \[\leadsto \color{blue}{\left(-0.25 \cdot a\right) \cdot b} \]

      2. *-commutative 53.1%

         \[\leadsto \color{blue}{b \cdot \left(-0.25 \cdot a\right)} \]

      3. *-commutative 53.1%

         \[\leadsto b \cdot \color{blue}{\left(a \cdot -0.25\right)} \]

   6. Simplified 53.1%

      \[\leadsto \color{blue}{b \cdot \left(a \cdot -0.25\right)} \]

   if -6.5e30 < (*.f64 x y) < -2.7999999999999999e-81

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf 36.9%

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

   if -4.60000000000000017e-271 < (*.f64 x y) < 2.19999999999999977e-193 or 6.8999999999999999e-37 < (*.f64 x y) < 2.2000000000000001e-14

   1. Initial program 99.9%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 99.9%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in t around inf 54.3%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right)} \]
   5. Step-by-step derivation

      1. associate-*r* 54.3%

         \[\leadsto \color{blue}{\left(0.0625 \cdot t\right) \cdot z} \]

      2. *-commutative 54.3%

         \[\leadsto \color{blue}{z \cdot \left(0.0625 \cdot t\right)} \]

   6. Simplified 54.3%

      \[\leadsto \color{blue}{z \cdot \left(0.0625 \cdot t\right)} \]
3. Recombined 4 regimes into one program.

4. Final simplification 57.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot y \leq -1.9 \cdot 10^{+74}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \cdot y \leq -6.5 \cdot 10^{+30}:\\ \;\;\;\;b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq -2.8 \cdot 10^{-81}:\\ \;\;\;\;c\\ \mathbf{elif}\;x \cdot y \leq -4.6 \cdot 10^{-271}:\\ \;\;\;\;b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq 2.2 \cdot 10^{-193}:\\ \;\;\;\;z \cdot \left(t \cdot 0.0625\right)\\ \mathbf{elif}\;x \cdot y \leq 6.9 \cdot 10^{-37}:\\ \;\;\;\;b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq 2.2 \cdot 10^{-14}:\\ \;\;\;\;z \cdot \left(t \cdot 0.0625\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 67.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 0.0625 \cdot \left(z \cdot t\right)\\ t_2 := \left(a \cdot b\right) \cdot 0.25\\ t_3 := t\_1 - t\_2\\ t_4 := x \cdot y - t\_2\\ \mathbf{if}\;x \cdot y \leq -5 \cdot 10^{+30}:\\ \;\;\;\;t\_4\\ \mathbf{elif}\;x \cdot y \leq -5 \cdot 10^{-82}:\\ \;\;\;\;c + t\_1\\ \mathbf{elif}\;x \cdot y \leq 10^{-190}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;x \cdot y \leq 4 \cdot 10^{-83}:\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq 10^{-19}:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* 0.0625 (* z t)))
        (t_2 (* (* a b) 0.25))
        (t_3 (- t_1 t_2))
        (t_4 (- (* x y) t_2)))
   (if (<= (* x y) -5e+30)
     t_4
     (if (<= (* x y) -5e-82)
       (+ c t_1)
       (if (<= (* x y) 1e-190)
         t_3
         (if (<= (* x y) 4e-83)
           (+ c (* b (* a -0.25)))
           (if (<= (* x y) 1e-19) t_3 t_4)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = 0.0625 * (z * t);
	double t_2 = (a * b) * 0.25;
	double t_3 = t_1 - t_2;
	double t_4 = (x * y) - t_2;
	double tmp;
	if ((x * y) <= -5e+30) {
		tmp = t_4;
	} else if ((x * y) <= -5e-82) {
		tmp = c + t_1;
	} else if ((x * y) <= 1e-190) {
		tmp = t_3;
	} else if ((x * y) <= 4e-83) {
		tmp = c + (b * (a * -0.25));
	} else if ((x * y) <= 1e-19) {
		tmp = t_3;
	} else {
		tmp = t_4;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: t_4
    real(8) :: tmp
    t_1 = 0.0625d0 * (z * t)
    t_2 = (a * b) * 0.25d0
    t_3 = t_1 - t_2
    t_4 = (x * y) - t_2
    if ((x * y) <= (-5d+30)) then
        tmp = t_4
    else if ((x * y) <= (-5d-82)) then
        tmp = c + t_1
    else if ((x * y) <= 1d-190) then
        tmp = t_3
    else if ((x * y) <= 4d-83) then
        tmp = c + (b * (a * (-0.25d0)))
    else if ((x * y) <= 1d-19) then
        tmp = t_3
    else
        tmp = t_4
    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 t_1 = 0.0625 * (z * t);
	double t_2 = (a * b) * 0.25;
	double t_3 = t_1 - t_2;
	double t_4 = (x * y) - t_2;
	double tmp;
	if ((x * y) <= -5e+30) {
		tmp = t_4;
	} else if ((x * y) <= -5e-82) {
		tmp = c + t_1;
	} else if ((x * y) <= 1e-190) {
		tmp = t_3;
	} else if ((x * y) <= 4e-83) {
		tmp = c + (b * (a * -0.25));
	} else if ((x * y) <= 1e-19) {
		tmp = t_3;
	} else {
		tmp = t_4;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = 0.0625 * (z * t)
	t_2 = (a * b) * 0.25
	t_3 = t_1 - t_2
	t_4 = (x * y) - t_2
	tmp = 0
	if (x * y) <= -5e+30:
		tmp = t_4
	elif (x * y) <= -5e-82:
		tmp = c + t_1
	elif (x * y) <= 1e-190:
		tmp = t_3
	elif (x * y) <= 4e-83:
		tmp = c + (b * (a * -0.25))
	elif (x * y) <= 1e-19:
		tmp = t_3
	else:
		tmp = t_4
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(0.0625 * Float64(z * t))
	t_2 = Float64(Float64(a * b) * 0.25)
	t_3 = Float64(t_1 - t_2)
	t_4 = Float64(Float64(x * y) - t_2)
	tmp = 0.0
	if (Float64(x * y) <= -5e+30)
		tmp = t_4;
	elseif (Float64(x * y) <= -5e-82)
		tmp = Float64(c + t_1);
	elseif (Float64(x * y) <= 1e-190)
		tmp = t_3;
	elseif (Float64(x * y) <= 4e-83)
		tmp = Float64(c + Float64(b * Float64(a * -0.25)));
	elseif (Float64(x * y) <= 1e-19)
		tmp = t_3;
	else
		tmp = t_4;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = 0.0625 * (z * t);
	t_2 = (a * b) * 0.25;
	t_3 = t_1 - t_2;
	t_4 = (x * y) - t_2;
	tmp = 0.0;
	if ((x * y) <= -5e+30)
		tmp = t_4;
	elseif ((x * y) <= -5e-82)
		tmp = c + t_1;
	elseif ((x * y) <= 1e-190)
		tmp = t_3;
	elseif ((x * y) <= 4e-83)
		tmp = c + (b * (a * -0.25));
	elseif ((x * y) <= 1e-19)
		tmp = t_3;
	else
		tmp = t_4;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a * b), $MachinePrecision] * 0.25), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$1 - t$95$2), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x * y), $MachinePrecision] - t$95$2), $MachinePrecision]}, If[LessEqual[N[(x * y), $MachinePrecision], -5e+30], t$95$4, If[LessEqual[N[(x * y), $MachinePrecision], -5e-82], N[(c + t$95$1), $MachinePrecision], If[LessEqual[N[(x * y), $MachinePrecision], 1e-190], t$95$3, If[LessEqual[N[(x * y), $MachinePrecision], 4e-83], N[(c + N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x * y), $MachinePrecision], 1e-19], t$95$3, t$95$4]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 x y) < -4.9999999999999998e30 or 9.9999999999999998e-20 < (*.f64 x y)

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 90.1%

      \[\leadsto \color{blue}{\left(c + x \cdot y\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in y around inf 87.7%

      \[\leadsto \color{blue}{y \cdot \left(x + \frac{c}{y}\right)} - 0.25 \cdot \left(a \cdot b\right) \]

   5. Taylor expanded in x around inf 82.8%

      \[\leadsto y \cdot \color{blue}{x} - 0.25 \cdot \left(a \cdot b\right) \]

   if -4.9999999999999998e30 < (*.f64 x y) < -4.9999999999999998e-82

   1. Initial program 95.7%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 66.7%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right)} + c \]

   if -4.9999999999999998e-82 < (*.f64 x y) < 1e-190 or 4.0000000000000001e-83 < (*.f64 x y) < 9.9999999999999998e-20

   1. Initial program 99.9%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 99.9%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in c around 0 79.9%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   if 1e-190 < (*.f64 x y) < 4.0000000000000001e-83

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 76.0%

      \[\leadsto \color{blue}{-0.25 \cdot \left(a \cdot b\right)} + c \]
   4. Step-by-step derivation

      1. associate-*r* 76.0%

         \[\leadsto \color{blue}{\left(-0.25 \cdot a\right) \cdot b} + c \]

      2. *-commutative 76.0%

         \[\leadsto \color{blue}{\left(a \cdot -0.25\right)} \cdot b + c \]

   5. Simplified 76.0%

      \[\leadsto \color{blue}{\left(a \cdot -0.25\right) \cdot b} + c \]
3. Recombined 4 regimes into one program.

4. Final simplification 79.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot y \leq -5 \cdot 10^{+30}:\\ \;\;\;\;x \cdot y - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{elif}\;x \cdot y \leq -5 \cdot 10^{-82}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{elif}\;x \cdot y \leq 10^{-190}:\\ \;\;\;\;0.0625 \cdot \left(z \cdot t\right) - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{elif}\;x \cdot y \leq 4 \cdot 10^{-83}:\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq 10^{-19}:\\ \;\;\;\;0.0625 \cdot \left(z \cdot t\right) - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{else}:\\ \;\;\;\;x \cdot y - \left(a \cdot b\right) \cdot 0.25\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 63.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c + x \cdot y\\ t_2 := c + 0.0625 \cdot \left(z \cdot t\right)\\ t_3 := b \cdot \left(a \cdot -0.25\right)\\ \mathbf{if}\;a \cdot b \leq -5 \cdot 10^{+178}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;a \cdot b \leq -5 \cdot 10^{-93}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \cdot b \leq -4 \cdot 10^{-191}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;a \cdot b \leq 2 \cdot 10^{-23}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \cdot b \leq 5 \cdot 10^{+81}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;c + t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (+ c (* x y)))
        (t_2 (+ c (* 0.0625 (* z t))))
        (t_3 (* b (* a -0.25))))
   (if (<= (* a b) -5e+178)
     t_3
     (if (<= (* a b) -5e-93)
       t_1
       (if (<= (* a b) -4e-191)
         t_2
         (if (<= (* a b) 2e-23) t_1 (if (<= (* a b) 5e+81) t_2 (+ c t_3))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = c + (x * y);
	double t_2 = c + (0.0625 * (z * t));
	double t_3 = b * (a * -0.25);
	double tmp;
	if ((a * b) <= -5e+178) {
		tmp = t_3;
	} else if ((a * b) <= -5e-93) {
		tmp = t_1;
	} else if ((a * b) <= -4e-191) {
		tmp = t_2;
	} else if ((a * b) <= 2e-23) {
		tmp = t_1;
	} else if ((a * b) <= 5e+81) {
		tmp = t_2;
	} else {
		tmp = c + t_3;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = c + (x * y)
    t_2 = c + (0.0625d0 * (z * t))
    t_3 = b * (a * (-0.25d0))
    if ((a * b) <= (-5d+178)) then
        tmp = t_3
    else if ((a * b) <= (-5d-93)) then
        tmp = t_1
    else if ((a * b) <= (-4d-191)) then
        tmp = t_2
    else if ((a * b) <= 2d-23) then
        tmp = t_1
    else if ((a * b) <= 5d+81) then
        tmp = t_2
    else
        tmp = c + t_3
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = c + (x * y);
	double t_2 = c + (0.0625 * (z * t));
	double t_3 = b * (a * -0.25);
	double tmp;
	if ((a * b) <= -5e+178) {
		tmp = t_3;
	} else if ((a * b) <= -5e-93) {
		tmp = t_1;
	} else if ((a * b) <= -4e-191) {
		tmp = t_2;
	} else if ((a * b) <= 2e-23) {
		tmp = t_1;
	} else if ((a * b) <= 5e+81) {
		tmp = t_2;
	} else {
		tmp = c + t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = c + (x * y)
	t_2 = c + (0.0625 * (z * t))
	t_3 = b * (a * -0.25)
	tmp = 0
	if (a * b) <= -5e+178:
		tmp = t_3
	elif (a * b) <= -5e-93:
		tmp = t_1
	elif (a * b) <= -4e-191:
		tmp = t_2
	elif (a * b) <= 2e-23:
		tmp = t_1
	elif (a * b) <= 5e+81:
		tmp = t_2
	else:
		tmp = c + t_3
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(c + Float64(x * y))
	t_2 = Float64(c + Float64(0.0625 * Float64(z * t)))
	t_3 = Float64(b * Float64(a * -0.25))
	tmp = 0.0
	if (Float64(a * b) <= -5e+178)
		tmp = t_3;
	elseif (Float64(a * b) <= -5e-93)
		tmp = t_1;
	elseif (Float64(a * b) <= -4e-191)
		tmp = t_2;
	elseif (Float64(a * b) <= 2e-23)
		tmp = t_1;
	elseif (Float64(a * b) <= 5e+81)
		tmp = t_2;
	else
		tmp = Float64(c + t_3);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = c + (x * y);
	t_2 = c + (0.0625 * (z * t));
	t_3 = b * (a * -0.25);
	tmp = 0.0;
	if ((a * b) <= -5e+178)
		tmp = t_3;
	elseif ((a * b) <= -5e-93)
		tmp = t_1;
	elseif ((a * b) <= -4e-191)
		tmp = t_2;
	elseif ((a * b) <= 2e-23)
		tmp = t_1;
	elseif ((a * b) <= 5e+81)
		tmp = t_2;
	else
		tmp = c + t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(c + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(a * b), $MachinePrecision], -5e+178], t$95$3, If[LessEqual[N[(a * b), $MachinePrecision], -5e-93], t$95$1, If[LessEqual[N[(a * b), $MachinePrecision], -4e-191], t$95$2, If[LessEqual[N[(a * b), $MachinePrecision], 2e-23], t$95$1, If[LessEqual[N[(a * b), $MachinePrecision], 5e+81], t$95$2, N[(c + t$95$3), $MachinePrecision]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 a b) < -4.9999999999999999e178

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 91.6%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in a around inf 85.9%

      \[\leadsto \color{blue}{-0.25 \cdot \left(a \cdot b\right)} \]
   5. Step-by-step derivation

      1. associate-*r* 85.9%

         \[\leadsto \color{blue}{\left(-0.25 \cdot a\right) \cdot b} \]

      2. *-commutative 85.9%

         \[\leadsto \color{blue}{b \cdot \left(-0.25 \cdot a\right)} \]

      3. *-commutative 85.9%

         \[\leadsto b \cdot \color{blue}{\left(a \cdot -0.25\right)} \]

   6. Simplified 85.9%

      \[\leadsto \color{blue}{b \cdot \left(a \cdot -0.25\right)} \]

   if -4.9999999999999999e178 < (*.f64 a b) < -4.99999999999999994e-93 or -4.0000000000000001e-191 < (*.f64 a b) < 1.99999999999999992e-23

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 68.4%

      \[\leadsto \color{blue}{x \cdot y} + c \]

   if -4.99999999999999994e-93 < (*.f64 a b) < -4.0000000000000001e-191 or 1.99999999999999992e-23 < (*.f64 a b) < 4.9999999999999998e81

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 76.0%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right)} + c \]

   if 4.9999999999999998e81 < (*.f64 a b)

   1. Initial program 97.8%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 75.2%

      \[\leadsto \color{blue}{-0.25 \cdot \left(a \cdot b\right)} + c \]
   4. Step-by-step derivation

      1. associate-*r* 75.2%

         \[\leadsto \color{blue}{\left(-0.25 \cdot a\right) \cdot b} + c \]

      2. *-commutative 75.2%

         \[\leadsto \color{blue}{\left(a \cdot -0.25\right)} \cdot b + c \]

   5. Simplified 75.2%

      \[\leadsto \color{blue}{\left(a \cdot -0.25\right) \cdot b} + c \]
3. Recombined 4 regimes into one program.

4. Final simplification 72.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot b \leq -5 \cdot 10^{+178}:\\ \;\;\;\;b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;a \cdot b \leq -5 \cdot 10^{-93}:\\ \;\;\;\;c + x \cdot y\\ \mathbf{elif}\;a \cdot b \leq -4 \cdot 10^{-191}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{elif}\;a \cdot b \leq 2 \cdot 10^{-23}:\\ \;\;\;\;c + x \cdot y\\ \mathbf{elif}\;a \cdot b \leq 5 \cdot 10^{+81}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 65.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c + 0.0625 \cdot \left(z \cdot t\right)\\ t_2 := x \cdot y - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{if}\;x \cdot y \leq -5 \cdot 10^{+30}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;x \cdot y \leq 4 \cdot 10^{-197}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \cdot y \leq 2 \cdot 10^{-41}:\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq 10^{-19}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (+ c (* 0.0625 (* z t)))) (t_2 (- (* x y) (* (* a b) 0.25))))
   (if (<= (* x y) -5e+30)
     t_2
     (if (<= (* x y) 4e-197)
       t_1
       (if (<= (* x y) 2e-41)
         (+ c (* b (* a -0.25)))
         (if (<= (* x y) 1e-19) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = c + (0.0625 * (z * t));
	double t_2 = (x * y) - ((a * b) * 0.25);
	double tmp;
	if ((x * y) <= -5e+30) {
		tmp = t_2;
	} else if ((x * y) <= 4e-197) {
		tmp = t_1;
	} else if ((x * y) <= 2e-41) {
		tmp = c + (b * (a * -0.25));
	} else if ((x * y) <= 1e-19) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = c + (0.0625d0 * (z * t))
    t_2 = (x * y) - ((a * b) * 0.25d0)
    if ((x * y) <= (-5d+30)) then
        tmp = t_2
    else if ((x * y) <= 4d-197) then
        tmp = t_1
    else if ((x * y) <= 2d-41) then
        tmp = c + (b * (a * (-0.25d0)))
    else if ((x * y) <= 1d-19) then
        tmp = t_1
    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 t_1 = c + (0.0625 * (z * t));
	double t_2 = (x * y) - ((a * b) * 0.25);
	double tmp;
	if ((x * y) <= -5e+30) {
		tmp = t_2;
	} else if ((x * y) <= 4e-197) {
		tmp = t_1;
	} else if ((x * y) <= 2e-41) {
		tmp = c + (b * (a * -0.25));
	} else if ((x * y) <= 1e-19) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = c + (0.0625 * (z * t))
	t_2 = (x * y) - ((a * b) * 0.25)
	tmp = 0
	if (x * y) <= -5e+30:
		tmp = t_2
	elif (x * y) <= 4e-197:
		tmp = t_1
	elif (x * y) <= 2e-41:
		tmp = c + (b * (a * -0.25))
	elif (x * y) <= 1e-19:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(c + Float64(0.0625 * Float64(z * t)))
	t_2 = Float64(Float64(x * y) - Float64(Float64(a * b) * 0.25))
	tmp = 0.0
	if (Float64(x * y) <= -5e+30)
		tmp = t_2;
	elseif (Float64(x * y) <= 4e-197)
		tmp = t_1;
	elseif (Float64(x * y) <= 2e-41)
		tmp = Float64(c + Float64(b * Float64(a * -0.25)));
	elseif (Float64(x * y) <= 1e-19)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = c + (0.0625 * (z * t));
	t_2 = (x * y) - ((a * b) * 0.25);
	tmp = 0.0;
	if ((x * y) <= -5e+30)
		tmp = t_2;
	elseif ((x * y) <= 4e-197)
		tmp = t_1;
	elseif ((x * y) <= 2e-41)
		tmp = c + (b * (a * -0.25));
	elseif ((x * y) <= 1e-19)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(c + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x * y), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] * 0.25), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x * y), $MachinePrecision], -5e+30], t$95$2, If[LessEqual[N[(x * y), $MachinePrecision], 4e-197], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], 2e-41], N[(c + N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x * y), $MachinePrecision], 1e-19], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 x y) < -4.9999999999999998e30 or 9.9999999999999998e-20 < (*.f64 x y)

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 90.1%

      \[\leadsto \color{blue}{\left(c + x \cdot y\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in y around inf 87.7%

      \[\leadsto \color{blue}{y \cdot \left(x + \frac{c}{y}\right)} - 0.25 \cdot \left(a \cdot b\right) \]

   5. Taylor expanded in x around inf 82.8%

      \[\leadsto y \cdot \color{blue}{x} - 0.25 \cdot \left(a \cdot b\right) \]

   if -4.9999999999999998e30 < (*.f64 x y) < 3.9999999999999999e-197 or 2.00000000000000001e-41 < (*.f64 x y) < 9.9999999999999998e-20

   1. Initial program 99.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 67.6%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right)} + c \]

   if 3.9999999999999999e-197 < (*.f64 x y) < 2.00000000000000001e-41

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 75.1%

      \[\leadsto \color{blue}{-0.25 \cdot \left(a \cdot b\right)} + c \]
   4. Step-by-step derivation

      1. associate-*r* 75.1%

         \[\leadsto \color{blue}{\left(-0.25 \cdot a\right) \cdot b} + c \]

      2. *-commutative 75.1%

         \[\leadsto \color{blue}{\left(a \cdot -0.25\right)} \cdot b + c \]

   5. Simplified 75.1%

      \[\leadsto \color{blue}{\left(a \cdot -0.25\right) \cdot b} + c \]
3. Recombined 3 regimes into one program.

4. Final simplification 75.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot y \leq -5 \cdot 10^{+30}:\\ \;\;\;\;x \cdot y - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{elif}\;x \cdot y \leq 4 \cdot 10^{-197}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{elif}\;x \cdot y \leq 2 \cdot 10^{-41}:\\ \;\;\;\;c + b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq 10^{-19}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot y - \left(a \cdot b\right) \cdot 0.25\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 63.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := c + x \cdot y\\ \mathbf{if}\;x \cdot y \leq -2 \cdot 10^{+74}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \cdot y \leq -8 \cdot 10^{+30}:\\ \;\;\;\;b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq -2 \cdot 10^{+30} \lor \neg \left(x \cdot y \leq 10^{-19}\right):\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (+ c (* x y))))
   (if (<= (* x y) -2e+74)
     t_1
     (if (<= (* x y) -8e+30)
       (* b (* a -0.25))
       (if (or (<= (* x y) -2e+30) (not (<= (* x y) 1e-19)))
         t_1
         (+ c (* 0.0625 (* z t))))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = c + (x * y);
	double tmp;
	if ((x * y) <= -2e+74) {
		tmp = t_1;
	} else if ((x * y) <= -8e+30) {
		tmp = b * (a * -0.25);
	} else if (((x * y) <= -2e+30) || !((x * y) <= 1e-19)) {
		tmp = t_1;
	} else {
		tmp = c + (0.0625 * (z * t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = c + (x * y)
    if ((x * y) <= (-2d+74)) then
        tmp = t_1
    else if ((x * y) <= (-8d+30)) then
        tmp = b * (a * (-0.25d0))
    else if (((x * y) <= (-2d+30)) .or. (.not. ((x * y) <= 1d-19))) then
        tmp = t_1
    else
        tmp = c + (0.0625d0 * (z * t))
    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 t_1 = c + (x * y);
	double tmp;
	if ((x * y) <= -2e+74) {
		tmp = t_1;
	} else if ((x * y) <= -8e+30) {
		tmp = b * (a * -0.25);
	} else if (((x * y) <= -2e+30) || !((x * y) <= 1e-19)) {
		tmp = t_1;
	} else {
		tmp = c + (0.0625 * (z * t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = c + (x * y)
	tmp = 0
	if (x * y) <= -2e+74:
		tmp = t_1
	elif (x * y) <= -8e+30:
		tmp = b * (a * -0.25)
	elif ((x * y) <= -2e+30) or not ((x * y) <= 1e-19):
		tmp = t_1
	else:
		tmp = c + (0.0625 * (z * t))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(c + Float64(x * y))
	tmp = 0.0
	if (Float64(x * y) <= -2e+74)
		tmp = t_1;
	elseif (Float64(x * y) <= -8e+30)
		tmp = Float64(b * Float64(a * -0.25));
	elseif ((Float64(x * y) <= -2e+30) || !(Float64(x * y) <= 1e-19))
		tmp = t_1;
	else
		tmp = Float64(c + Float64(0.0625 * Float64(z * t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = c + (x * y);
	tmp = 0.0;
	if ((x * y) <= -2e+74)
		tmp = t_1;
	elseif ((x * y) <= -8e+30)
		tmp = b * (a * -0.25);
	elseif (((x * y) <= -2e+30) || ~(((x * y) <= 1e-19)))
		tmp = t_1;
	else
		tmp = c + (0.0625 * (z * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x * y), $MachinePrecision], -2e+74], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], -8e+30], N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[N[(x * y), $MachinePrecision], -2e+30], N[Not[LessEqual[N[(x * y), $MachinePrecision], 1e-19]], $MachinePrecision]], t$95$1, N[(c + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 x y) < -1.9999999999999999e74 or -8.0000000000000002e30 < (*.f64 x y) < -2e30 or 9.9999999999999998e-20 < (*.f64 x y)

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 73.5%

      \[\leadsto \color{blue}{x \cdot y} + c \]

   if -1.9999999999999999e74 < (*.f64 x y) < -8.0000000000000002e30

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 100.0%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in a around inf 65.4%

      \[\leadsto \color{blue}{-0.25 \cdot \left(a \cdot b\right)} \]
   5. Step-by-step derivation

      1. associate-*r* 65.4%

         \[\leadsto \color{blue}{\left(-0.25 \cdot a\right) \cdot b} \]

      2. *-commutative 65.4%

         \[\leadsto \color{blue}{b \cdot \left(-0.25 \cdot a\right)} \]

      3. *-commutative 65.4%

         \[\leadsto b \cdot \color{blue}{\left(a \cdot -0.25\right)} \]

   6. Simplified 65.4%

      \[\leadsto \color{blue}{b \cdot \left(a \cdot -0.25\right)} \]

   if -2e30 < (*.f64 x y) < 9.9999999999999998e-20

   1. Initial program 99.2%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 63.9%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right)} + c \]
3. Recombined 3 regimes into one program.

4. Final simplification 68.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot y \leq -2 \cdot 10^{+74}:\\ \;\;\;\;c + x \cdot y\\ \mathbf{elif}\;x \cdot y \leq -8 \cdot 10^{+30}:\\ \;\;\;\;b \cdot \left(a \cdot -0.25\right)\\ \mathbf{elif}\;x \cdot y \leq -2 \cdot 10^{+30} \lor \neg \left(x \cdot y \leq 10^{-19}\right):\\ \;\;\;\;c + x \cdot y\\ \mathbf{else}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 43.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := b \cdot \left(a \cdot -0.25\right)\\ \mathbf{if}\;x \cdot y \leq -1.65 \cdot 10^{+74}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;x \cdot y \leq -6.5 \cdot 10^{+30}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \cdot y \leq -3.9 \cdot 10^{-79}:\\ \;\;\;\;c\\ \mathbf{elif}\;x \cdot y \leq 2.8 \cdot 10^{+39}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* b (* a -0.25))))
   (if (<= (* x y) -1.65e+74)
     (* x y)
     (if (<= (* x y) -6.5e+30)
       t_1
       (if (<= (* x y) -3.9e-79) c (if (<= (* x y) 2.8e+39) t_1 (* x y)))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = b * (a * -0.25);
	double tmp;
	if ((x * y) <= -1.65e+74) {
		tmp = x * y;
	} else if ((x * y) <= -6.5e+30) {
		tmp = t_1;
	} else if ((x * y) <= -3.9e-79) {
		tmp = c;
	} else if ((x * y) <= 2.8e+39) {
		tmp = t_1;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = b * (a * (-0.25d0))
    if ((x * y) <= (-1.65d+74)) then
        tmp = x * y
    else if ((x * y) <= (-6.5d+30)) then
        tmp = t_1
    else if ((x * y) <= (-3.9d-79)) then
        tmp = c
    else if ((x * y) <= 2.8d+39) then
        tmp = t_1
    else
        tmp = x * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = b * (a * -0.25);
	double tmp;
	if ((x * y) <= -1.65e+74) {
		tmp = x * y;
	} else if ((x * y) <= -6.5e+30) {
		tmp = t_1;
	} else if ((x * y) <= -3.9e-79) {
		tmp = c;
	} else if ((x * y) <= 2.8e+39) {
		tmp = t_1;
	} else {
		tmp = x * y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = b * (a * -0.25)
	tmp = 0
	if (x * y) <= -1.65e+74:
		tmp = x * y
	elif (x * y) <= -6.5e+30:
		tmp = t_1
	elif (x * y) <= -3.9e-79:
		tmp = c
	elif (x * y) <= 2.8e+39:
		tmp = t_1
	else:
		tmp = x * y
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(b * Float64(a * -0.25))
	tmp = 0.0
	if (Float64(x * y) <= -1.65e+74)
		tmp = Float64(x * y);
	elseif (Float64(x * y) <= -6.5e+30)
		tmp = t_1;
	elseif (Float64(x * y) <= -3.9e-79)
		tmp = c;
	elseif (Float64(x * y) <= 2.8e+39)
		tmp = t_1;
	else
		tmp = Float64(x * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = b * (a * -0.25);
	tmp = 0.0;
	if ((x * y) <= -1.65e+74)
		tmp = x * y;
	elseif ((x * y) <= -6.5e+30)
		tmp = t_1;
	elseif ((x * y) <= -3.9e-79)
		tmp = c;
	elseif ((x * y) <= 2.8e+39)
		tmp = t_1;
	else
		tmp = x * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x * y), $MachinePrecision], -1.65e+74], N[(x * y), $MachinePrecision], If[LessEqual[N[(x * y), $MachinePrecision], -6.5e+30], t$95$1, If[LessEqual[N[(x * y), $MachinePrecision], -3.9e-79], c, If[LessEqual[N[(x * y), $MachinePrecision], 2.8e+39], t$95$1, N[(x * y), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 x y) < -1.6500000000000001e74 or 2.80000000000000001e39 < (*.f64 x y)

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 76.7%

      \[\leadsto \color{blue}{x \cdot y} + c \]

   4. Taylor expanded in x around inf 69.9%

      \[\leadsto \color{blue}{x \cdot y} \]

   if -1.6500000000000001e74 < (*.f64 x y) < -6.5e30 or -3.90000000000000006e-79 < (*.f64 x y) < 2.80000000000000001e39

   1. Initial program 99.2%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 95.7%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in a around inf 41.0%

      \[\leadsto \color{blue}{-0.25 \cdot \left(a \cdot b\right)} \]
   5. Step-by-step derivation

      1. associate-*r* 41.0%

         \[\leadsto \color{blue}{\left(-0.25 \cdot a\right) \cdot b} \]

      2. *-commutative 41.0%

         \[\leadsto \color{blue}{b \cdot \left(-0.25 \cdot a\right)} \]

      3. *-commutative 41.0%

         \[\leadsto b \cdot \color{blue}{\left(a \cdot -0.25\right)} \]

   6. Simplified 41.0%

      \[\leadsto \color{blue}{b \cdot \left(a \cdot -0.25\right)} \]

   if -6.5e30 < (*.f64 x y) < -3.90000000000000006e-79

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf 36.9%

      \[\leadsto \color{blue}{c} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 88.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a \cdot b\right) \cdot 0.25\\ \mathbf{if}\;x \cdot y \leq -2 \cdot 10^{+74} \lor \neg \left(x \cdot y \leq 10^{-19}\right):\\ \;\;\;\;\left(c + x \cdot y\right) - t\_1\\ \mathbf{else}:\\ \;\;\;\;\left(c + 0.0625 \cdot \left(z \cdot t\right)\right) - t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* a b) 0.25)))
   (if (or (<= (* x y) -2e+74) (not (<= (* x y) 1e-19)))
     (- (+ c (* x y)) t_1)
     (- (+ c (* 0.0625 (* z t))) t_1))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (a * b) * 0.25;
	double tmp;
	if (((x * y) <= -2e+74) || !((x * y) <= 1e-19)) {
		tmp = (c + (x * y)) - t_1;
	} else {
		tmp = (c + (0.0625 * (z * t))) - t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: tmp
    t_1 = (a * b) * 0.25d0
    if (((x * y) <= (-2d+74)) .or. (.not. ((x * y) <= 1d-19))) then
        tmp = (c + (x * y)) - t_1
    else
        tmp = (c + (0.0625d0 * (z * t))) - t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (a * b) * 0.25;
	double tmp;
	if (((x * y) <= -2e+74) || !((x * y) <= 1e-19)) {
		tmp = (c + (x * y)) - t_1;
	} else {
		tmp = (c + (0.0625 * (z * t))) - t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (a * b) * 0.25
	tmp = 0
	if ((x * y) <= -2e+74) or not ((x * y) <= 1e-19):
		tmp = (c + (x * y)) - t_1
	else:
		tmp = (c + (0.0625 * (z * t))) - t_1
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(a * b) * 0.25)
	tmp = 0.0
	if ((Float64(x * y) <= -2e+74) || !(Float64(x * y) <= 1e-19))
		tmp = Float64(Float64(c + Float64(x * y)) - t_1);
	else
		tmp = Float64(Float64(c + Float64(0.0625 * Float64(z * t))) - t_1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (a * b) * 0.25;
	tmp = 0.0;
	if (((x * y) <= -2e+74) || ~(((x * y) <= 1e-19)))
		tmp = (c + (x * y)) - t_1;
	else
		tmp = (c + (0.0625 * (z * t))) - t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(a * b), $MachinePrecision] * 0.25), $MachinePrecision]}, If[Or[LessEqual[N[(x * y), $MachinePrecision], -2e+74], N[Not[LessEqual[N[(x * y), $MachinePrecision], 1e-19]], $MachinePrecision]], N[(N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], N[(N[(c + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x y) < -1.9999999999999999e74 or 9.9999999999999998e-20 < (*.f64 x y)

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 90.7%

      \[\leadsto \color{blue}{\left(c + x \cdot y\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   if -1.9999999999999999e74 < (*.f64 x y) < 9.9999999999999998e-20

   1. Initial program 99.3%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 95.4%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 93.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot y \leq -2 \cdot 10^{+74} \lor \neg \left(x \cdot y \leq 10^{-19}\right):\\ \;\;\;\;\left(c + x \cdot y\right) - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{else}:\\ \;\;\;\;\left(c + 0.0625 \cdot \left(z \cdot t\right)\right) - \left(a \cdot b\right) \cdot 0.25\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 77.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{+207}:\\ \;\;\;\;z \cdot \left(-0.25 \cdot \frac{a \cdot b}{z} + t \cdot 0.0625\right)\\ \mathbf{elif}\;z \leq 4.8 \cdot 10^{+20}:\\ \;\;\;\;\left(c + x \cdot y\right) - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{else}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (<= z -1.9e+207)
   (* z (+ (* -0.25 (/ (* a b) z)) (* t 0.0625)))
   (if (<= z 4.8e+20)
     (- (+ c (* x y)) (* (* a b) 0.25))
     (+ c (* 0.0625 (* z t))))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (z <= -1.9e+207) {
		tmp = z * ((-0.25 * ((a * b) / z)) + (t * 0.0625));
	} else if (z <= 4.8e+20) {
		tmp = (c + (x * y)) - ((a * b) * 0.25);
	} else {
		tmp = c + (0.0625 * (z * t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (z <= (-1.9d+207)) then
        tmp = z * (((-0.25d0) * ((a * b) / z)) + (t * 0.0625d0))
    else if (z <= 4.8d+20) then
        tmp = (c + (x * y)) - ((a * b) * 0.25d0)
    else
        tmp = c + (0.0625d0 * (z * t))
    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 tmp;
	if (z <= -1.9e+207) {
		tmp = z * ((-0.25 * ((a * b) / z)) + (t * 0.0625));
	} else if (z <= 4.8e+20) {
		tmp = (c + (x * y)) - ((a * b) * 0.25);
	} else {
		tmp = c + (0.0625 * (z * t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if z <= -1.9e+207:
		tmp = z * ((-0.25 * ((a * b) / z)) + (t * 0.0625))
	elif z <= 4.8e+20:
		tmp = (c + (x * y)) - ((a * b) * 0.25)
	else:
		tmp = c + (0.0625 * (z * t))
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (z <= -1.9e+207)
		tmp = Float64(z * Float64(Float64(-0.25 * Float64(Float64(a * b) / z)) + Float64(t * 0.0625)));
	elseif (z <= 4.8e+20)
		tmp = Float64(Float64(c + Float64(x * y)) - Float64(Float64(a * b) * 0.25));
	else
		tmp = Float64(c + Float64(0.0625 * Float64(z * t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (z <= -1.9e+207)
		tmp = z * ((-0.25 * ((a * b) / z)) + (t * 0.0625));
	elseif (z <= 4.8e+20)
		tmp = (c + (x * y)) - ((a * b) * 0.25);
	else
		tmp = c + (0.0625 * (z * t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[z, -1.9e+207], N[(z * N[(N[(-0.25 * N[(N[(a * b), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision] + N[(t * 0.0625), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.8e+20], N[(N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] * 0.25), $MachinePrecision]), $MachinePrecision], N[(c + N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.89999999999999993e207

   1. Initial program 93.3%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 81.0%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in c around 0 74.4%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   5. Taylor expanded in z around inf 81.0%

      \[\leadsto \color{blue}{z \cdot \left(-0.25 \cdot \frac{a \cdot b}{z} + 0.0625 \cdot t\right)} \]

   if -1.89999999999999993e207 < z < 4.8e20

   1. Initial program 99.9%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 84.8%

      \[\leadsto \color{blue}{\left(c + x \cdot y\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   if 4.8e20 < z

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 57.0%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right)} + c \]
3. Recombined 3 regimes into one program.

4. Final simplification 77.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.9 \cdot 10^{+207}:\\ \;\;\;\;z \cdot \left(-0.25 \cdot \frac{a \cdot b}{z} + t \cdot 0.0625\right)\\ \mathbf{elif}\;z \leq 4.8 \cdot 10^{+20}:\\ \;\;\;\;\left(c + x \cdot y\right) - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{else}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 76.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a \cdot b\right) \cdot 0.25\\ t_2 := 0.0625 \cdot \left(z \cdot t\right)\\ \mathbf{if}\;z \leq -1.7 \cdot 10^{+207}:\\ \;\;\;\;t\_2 - t\_1\\ \mathbf{elif}\;z \leq 8.2 \cdot 10^{+14}:\\ \;\;\;\;\left(c + x \cdot y\right) - t\_1\\ \mathbf{else}:\\ \;\;\;\;c + t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* a b) 0.25)) (t_2 (* 0.0625 (* z t))))
   (if (<= z -1.7e+207)
     (- t_2 t_1)
     (if (<= z 8.2e+14) (- (+ c (* x y)) t_1) (+ c t_2)))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (a * b) * 0.25;
	double t_2 = 0.0625 * (z * t);
	double tmp;
	if (z <= -1.7e+207) {
		tmp = t_2 - t_1;
	} else if (z <= 8.2e+14) {
		tmp = (c + (x * y)) - t_1;
	} else {
		tmp = c + t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (a * b) * 0.25d0
    t_2 = 0.0625d0 * (z * t)
    if (z <= (-1.7d+207)) then
        tmp = t_2 - t_1
    else if (z <= 8.2d+14) then
        tmp = (c + (x * y)) - t_1
    else
        tmp = c + 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 t_1 = (a * b) * 0.25;
	double t_2 = 0.0625 * (z * t);
	double tmp;
	if (z <= -1.7e+207) {
		tmp = t_2 - t_1;
	} else if (z <= 8.2e+14) {
		tmp = (c + (x * y)) - t_1;
	} else {
		tmp = c + t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	t_1 = (a * b) * 0.25
	t_2 = 0.0625 * (z * t)
	tmp = 0
	if z <= -1.7e+207:
		tmp = t_2 - t_1
	elif z <= 8.2e+14:
		tmp = (c + (x * y)) - t_1
	else:
		tmp = c + t_2
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(a * b) * 0.25)
	t_2 = Float64(0.0625 * Float64(z * t))
	tmp = 0.0
	if (z <= -1.7e+207)
		tmp = Float64(t_2 - t_1);
	elseif (z <= 8.2e+14)
		tmp = Float64(Float64(c + Float64(x * y)) - t_1);
	else
		tmp = Float64(c + t_2);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (a * b) * 0.25;
	t_2 = 0.0625 * (z * t);
	tmp = 0.0;
	if (z <= -1.7e+207)
		tmp = t_2 - t_1;
	elseif (z <= 8.2e+14)
		tmp = (c + (x * y)) - t_1;
	else
		tmp = c + t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(a * b), $MachinePrecision] * 0.25), $MachinePrecision]}, Block[{t$95$2 = N[(0.0625 * N[(z * t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.7e+207], N[(t$95$2 - t$95$1), $MachinePrecision], If[LessEqual[z, 8.2e+14], N[(N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision] - t$95$1), $MachinePrecision], N[(c + t$95$2), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.6999999999999999e207

   1. Initial program 93.3%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 81.0%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in c around 0 74.4%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   if -1.6999999999999999e207 < z < 8.2e14

   1. Initial program 99.9%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around 0 84.8%

      \[\leadsto \color{blue}{\left(c + x \cdot y\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   if 8.2e14 < z

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf 57.0%

      \[\leadsto \color{blue}{0.0625 \cdot \left(t \cdot z\right)} + c \]
3. Recombined 3 regimes into one program.

4. Final simplification 77.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.7 \cdot 10^{+207}:\\ \;\;\;\;0.0625 \cdot \left(z \cdot t\right) - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{elif}\;z \leq 8.2 \cdot 10^{+14}:\\ \;\;\;\;\left(c + x \cdot y\right) - \left(a \cdot b\right) \cdot 0.25\\ \mathbf{else}:\\ \;\;\;\;c + 0.0625 \cdot \left(z \cdot t\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 41.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot y \leq -5.8 \cdot 10^{+71} \lor \neg \left(x \cdot y \leq 4.8 \cdot 10^{-22}\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;c\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= (* x y) -5.8e+71) (not (<= (* x y) 4.8e-22))) (* x y) c))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (((x * y) <= -5.8e+71) || !((x * y) <= 4.8e-22)) {
		tmp = x * y;
	} else {
		tmp = c;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if (((x * y) <= (-5.8d+71)) .or. (.not. ((x * y) <= 4.8d-22))) then
        tmp = x * y
    else
        tmp = c
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (((x * y) <= -5.8e+71) || !((x * y) <= 4.8e-22)) {
		tmp = x * y;
	} else {
		tmp = c;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if ((x * y) <= -5.8e+71) or not ((x * y) <= 4.8e-22):
		tmp = x * y
	else:
		tmp = c
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((Float64(x * y) <= -5.8e+71) || !(Float64(x * y) <= 4.8e-22))
		tmp = Float64(x * y);
	else
		tmp = c;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (((x * y) <= -5.8e+71) || ~(((x * y) <= 4.8e-22)))
		tmp = x * y;
	else
		tmp = c;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[N[(x * y), $MachinePrecision], -5.8e+71], N[Not[LessEqual[N[(x * y), $MachinePrecision], 4.8e-22]], $MachinePrecision]], N[(x * y), $MachinePrecision], c]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x y) < -5.80000000000000014e71 or 4.80000000000000005e-22 < (*.f64 x y)

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 71.0%

      \[\leadsto \color{blue}{x \cdot y} + c \]

   4. Taylor expanded in x around inf 65.0%

      \[\leadsto \color{blue}{x \cdot y} \]

   if -5.80000000000000014e71 < (*.f64 x y) < 4.80000000000000005e-22

   1. Initial program 99.3%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in c around inf 26.7%

      \[\leadsto \color{blue}{c} \]
3. Recombined 2 regimes into one program.

4. Final simplification 43.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot y \leq -5.8 \cdot 10^{+71} \lor \neg \left(x \cdot y \leq 4.8 \cdot 10^{-22}\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;c\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 97.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ c + \left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (+ c (- (+ (* x y) (/ (* z t) 16.0)) (/ (* a b) 4.0))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return c + (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0));
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = c + (((x * y) + ((z * t) / 16.0d0)) - ((a * b) / 4.0d0))
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return c + (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0));
}

Python

def code(x, y, z, t, a, b, c):
	return c + (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0))

Julia

function code(x, y, z, t, a, b, c)
	return Float64(c + Float64(Float64(Float64(x * y) + Float64(Float64(z * t) / 16.0)) - Float64(Float64(a * b) / 4.0)))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = c + (((x * y) + ((z * t) / 16.0)) - ((a * b) / 4.0));
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(c + N[(N[(N[(x * y), $MachinePrecision] + N[(N[(z * t), $MachinePrecision] / 16.0), $MachinePrecision]), $MachinePrecision] - N[(N[(a * b), $MachinePrecision] / 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
2. Add Preprocessing

3. Final simplification 99.6%

   \[\leadsto c + \left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) \]
4. Add Preprocessing

Alternative 13: 52.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq -1.7 \cdot 10^{-35} \lor \neg \left(b \leq 8.6 \cdot 10^{+160}\right):\\ \;\;\;\;b \cdot \left(a \cdot -0.25\right)\\ \mathbf{else}:\\ \;\;\;\;c + x \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (if (or (<= b -1.7e-35) (not (<= b 8.6e+160)))
   (* b (* a -0.25))
   (+ c (* x y))))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((b <= -1.7e-35) || !(b <= 8.6e+160)) {
		tmp = b * (a * -0.25);
	} else {
		tmp = c + (x * y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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) :: tmp
    if ((b <= (-1.7d-35)) .or. (.not. (b <= 8.6d+160))) then
        tmp = b * (a * (-0.25d0))
    else
        tmp = c + (x * y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if ((b <= -1.7e-35) || !(b <= 8.6e+160)) {
		tmp = b * (a * -0.25);
	} else {
		tmp = c + (x * y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b, c):
	tmp = 0
	if (b <= -1.7e-35) or not (b <= 8.6e+160):
		tmp = b * (a * -0.25)
	else:
		tmp = c + (x * y)
	return tmp

Julia

function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if ((b <= -1.7e-35) || !(b <= 8.6e+160))
		tmp = Float64(b * Float64(a * -0.25));
	else
		tmp = Float64(c + Float64(x * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if ((b <= -1.7e-35) || ~((b <= 8.6e+160)))
		tmp = b * (a * -0.25);
	else
		tmp = c + (x * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := If[Or[LessEqual[b, -1.7e-35], N[Not[LessEqual[b, 8.6e+160]], $MachinePrecision]], N[(b * N[(a * -0.25), $MachinePrecision]), $MachinePrecision], N[(c + N[(x * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < -1.7000000000000001e-35 or 8.59999999999999978e160 < b

   1. Initial program 99.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 74.7%

      \[\leadsto \color{blue}{\left(c + 0.0625 \cdot \left(t \cdot z\right)\right) - 0.25 \cdot \left(a \cdot b\right)} \]

   4. Taylor expanded in a around inf 47.8%

      \[\leadsto \color{blue}{-0.25 \cdot \left(a \cdot b\right)} \]
   5. Step-by-step derivation

      1. associate-*r* 47.8%

         \[\leadsto \color{blue}{\left(-0.25 \cdot a\right) \cdot b} \]

      2. *-commutative 47.8%

         \[\leadsto \color{blue}{b \cdot \left(-0.25 \cdot a\right)} \]

      3. *-commutative 47.8%

         \[\leadsto b \cdot \color{blue}{\left(a \cdot -0.25\right)} \]

   6. Simplified 47.8%

      \[\leadsto \color{blue}{b \cdot \left(a \cdot -0.25\right)} \]

   if -1.7000000000000001e-35 < b < 8.59999999999999978e160

   1. Initial program 100.0%

      \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 55.4%

      \[\leadsto \color{blue}{x \cdot y} + c \]
3. Recombined 2 regimes into one program.

4. Final simplification 52.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;b \leq -1.7 \cdot 10^{-35} \lor \neg \left(b \leq 8.6 \cdot 10^{+160}\right):\\ \;\;\;\;b \cdot \left(a \cdot -0.25\right)\\ \mathbf{else}:\\ \;\;\;\;c + x \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 22.0% accurate, 17.0× speedup

Math

\[\begin{array}{l} \\ c \end{array} \]

FPCore

(FPCore (x y z t a b c) :precision binary64 c)

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return c;
}

Fortran

real(8) function code(x, y, z, t, a, b, c)
    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
    code = c
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return c;
}

Python

def code(x, y, z, t, a, b, c):
	return c

Julia

function code(x, y, z, t, a, b, c)
	return c
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = c;
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := c

Derivation

1. Initial program 99.6%

   \[\left(\left(x \cdot y + \frac{z \cdot t}{16}\right) - \frac{a \cdot b}{4}\right) + c \]
2. Add Preprocessing

3. Taylor expanded in c around inf 18.5%

   \[\leadsto \color{blue}{c} \]
4. Add Preprocessing

Reproduce

herbie shell --seed 2024108 
(FPCore (x y z t a b c)
  :name "Diagrams.Solve.Polynomial:quartForm  from diagrams-solve-0.1, C"
  :precision binary64
  (+ (- (+ (* x y) (/ (* z t) 16.0)) (/ (* a b) 4.0)) c))