Optimisation.CirclePacking:place from circle-packing-0.1.0.4, D

Percentage Accurate: 92.9% → 97.8%

Time: 9.5s

Alternatives: 14

Speedup: 1.0×

• 24.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (+ x (/ (* y (- z x)) t)))

C

double code(double x, double y, double z, double t) {
	return x + ((y * (z - x)) / t);
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + ((y * (z - x)) / t)
end function

Java

public static double code(double x, double y, double z, double t) {
	return x + ((y * (z - x)) / t);
}

Python

def code(x, y, z, t):
	return x + ((y * (z - x)) / t)

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(y * Float64(z - x)) / t))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((y * (z - x)) / t);
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(y * N[(z - x), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]

Initial Program: 92.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (+ x (/ (* y (- z x)) t)))

C

double code(double x, double y, double z, double t) {
	return x + ((y * (z - x)) / t);
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + ((y * (z - x)) / t)
end function

Java

public static double code(double x, double y, double z, double t) {
	return x + ((y * (z - x)) / t);
}

Python

def code(x, y, z, t):
	return x + ((y * (z - x)) / t)

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(y * Float64(z - x)) / t))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((y * (z - x)) / t);
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(y * N[(z - x), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]

Alternative 1: 97.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -15000000:\\ \;\;\;\;x + \frac{y}{\frac{t}{z - x}}\\ \mathbf{else}:\\ \;\;\;\;x + \left(z - x\right) \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -15000000.0) (+ x (/ y (/ t (- z x)))) (+ x (* (- z x) (/ y t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -15000000.0) {
		tmp = x + (y / (t / (z - x)));
	} else {
		tmp = x + ((z - x) * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (y <= (-15000000.0d0)) then
        tmp = x + (y / (t / (z - x)))
    else
        tmp = x + ((z - x) * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -15000000.0) {
		tmp = x + (y / (t / (z - x)));
	} else {
		tmp = x + ((z - x) * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -15000000.0:
		tmp = x + (y / (t / (z - x)))
	else:
		tmp = x + ((z - x) * (y / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -15000000.0)
		tmp = Float64(x + Float64(y / Float64(t / Float64(z - x))));
	else
		tmp = Float64(x + Float64(Float64(z - x) * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -15000000.0)
		tmp = x + (y / (t / (z - x)));
	else
		tmp = x + ((z - x) * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -15000000.0], N[(x + N[(y / N[(t / N[(z - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(z - x), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.5e7

   1. Initial program 79.3%

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

      1. associate-/l* 99.9%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{t}{z - x}}} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{t}{z - x}}} \]

   if -1.5e7 < y

   1. Initial program 92.9%

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

      1. associate-*l/ 98.1%

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

   3. Simplified 98.1%

      \[\leadsto \color{blue}{x + \frac{y}{t} \cdot \left(z - x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -15000000:\\ \;\;\;\;x + \frac{y}{\frac{t}{z - x}}\\ \mathbf{else}:\\ \;\;\;\;x + \left(z - x\right) \cdot \frac{y}{t}\\ \end{array} \]

Alternative 2: 49.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z}{\frac{t}{y}}\\ t_2 := x \cdot \frac{y}{-t}\\ \mathbf{if}\;z \leq -2.2 \cdot 10^{+141}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq -1.05 \cdot 10^{+101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -5.8 \cdot 10^{+15}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.8 \cdot 10^{-59}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -8.5 \cdot 10^{-179}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq -1.05 \cdot 10^{-285}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.1 \cdot 10^{-272}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{-85}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ z (/ t y))) (t_2 (* x (/ y (- t)))))
   (if (<= z -2.2e+141)
     (* y (/ z t))
     (if (<= z -1.05e+101)
       x
       (if (<= z -5.8e+15)
         t_1
         (if (<= z -1.8e-59)
           x
           (if (<= z -8.5e-179)
             t_2
             (if (<= z -1.05e-285)
               x
               (if (<= z 4.1e-272) t_2 (if (<= z 1.6e-85) x t_1))))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z / (t / y);
	double t_2 = x * (y / -t);
	double tmp;
	if (z <= -2.2e+141) {
		tmp = y * (z / t);
	} else if (z <= -1.05e+101) {
		tmp = x;
	} else if (z <= -5.8e+15) {
		tmp = t_1;
	} else if (z <= -1.8e-59) {
		tmp = x;
	} else if (z <= -8.5e-179) {
		tmp = t_2;
	} else if (z <= -1.05e-285) {
		tmp = x;
	} else if (z <= 4.1e-272) {
		tmp = t_2;
	} else if (z <= 1.6e-85) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = z / (t / y)
    t_2 = x * (y / -t)
    if (z <= (-2.2d+141)) then
        tmp = y * (z / t)
    else if (z <= (-1.05d+101)) then
        tmp = x
    else if (z <= (-5.8d+15)) then
        tmp = t_1
    else if (z <= (-1.8d-59)) then
        tmp = x
    else if (z <= (-8.5d-179)) then
        tmp = t_2
    else if (z <= (-1.05d-285)) then
        tmp = x
    else if (z <= 4.1d-272) then
        tmp = t_2
    else if (z <= 1.6d-85) then
        tmp = x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = z / (t / y);
	double t_2 = x * (y / -t);
	double tmp;
	if (z <= -2.2e+141) {
		tmp = y * (z / t);
	} else if (z <= -1.05e+101) {
		tmp = x;
	} else if (z <= -5.8e+15) {
		tmp = t_1;
	} else if (z <= -1.8e-59) {
		tmp = x;
	} else if (z <= -8.5e-179) {
		tmp = t_2;
	} else if (z <= -1.05e-285) {
		tmp = x;
	} else if (z <= 4.1e-272) {
		tmp = t_2;
	} else if (z <= 1.6e-85) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z / (t / y)
	t_2 = x * (y / -t)
	tmp = 0
	if z <= -2.2e+141:
		tmp = y * (z / t)
	elif z <= -1.05e+101:
		tmp = x
	elif z <= -5.8e+15:
		tmp = t_1
	elif z <= -1.8e-59:
		tmp = x
	elif z <= -8.5e-179:
		tmp = t_2
	elif z <= -1.05e-285:
		tmp = x
	elif z <= 4.1e-272:
		tmp = t_2
	elif z <= 1.6e-85:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z / Float64(t / y))
	t_2 = Float64(x * Float64(y / Float64(-t)))
	tmp = 0.0
	if (z <= -2.2e+141)
		tmp = Float64(y * Float64(z / t));
	elseif (z <= -1.05e+101)
		tmp = x;
	elseif (z <= -5.8e+15)
		tmp = t_1;
	elseif (z <= -1.8e-59)
		tmp = x;
	elseif (z <= -8.5e-179)
		tmp = t_2;
	elseif (z <= -1.05e-285)
		tmp = x;
	elseif (z <= 4.1e-272)
		tmp = t_2;
	elseif (z <= 1.6e-85)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z / (t / y);
	t_2 = x * (y / -t);
	tmp = 0.0;
	if (z <= -2.2e+141)
		tmp = y * (z / t);
	elseif (z <= -1.05e+101)
		tmp = x;
	elseif (z <= -5.8e+15)
		tmp = t_1;
	elseif (z <= -1.8e-59)
		tmp = x;
	elseif (z <= -8.5e-179)
		tmp = t_2;
	elseif (z <= -1.05e-285)
		tmp = x;
	elseif (z <= 4.1e-272)
		tmp = t_2;
	elseif (z <= 1.6e-85)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(y / (-t)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.2e+141], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -1.05e+101], x, If[LessEqual[z, -5.8e+15], t$95$1, If[LessEqual[z, -1.8e-59], x, If[LessEqual[z, -8.5e-179], t$95$2, If[LessEqual[z, -1.05e-285], x, If[LessEqual[z, 4.1e-272], t$95$2, If[LessEqual[z, 1.6e-85], x, t$95$1]]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -2.2e141

   1. Initial program 92.5%

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

      1. +-commutative 92.5%

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

      2. associate-*r/ 90.0%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 90.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 90.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 72.3%

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

   5. Taylor expanded in z around inf 67.3%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]

   if -2.2e141 < z < -1.05e101 or -5.8e15 < z < -1.8e-59 or -8.49999999999999932e-179 < z < -1.04999999999999992e-285 or 4.0999999999999997e-272 < z < 1.60000000000000014e-85

   1. Initial program 88.4%

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

      1. +-commutative 88.4%

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

      2. associate-*r/ 96.1%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 96.1%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 96.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around 0 68.6%

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

   if -1.05e101 < z < -5.8e15 or 1.60000000000000014e-85 < z

   1. Initial program 88.6%

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

      1. +-commutative 88.6%

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

      2. associate-*r/ 91.0%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 91.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 91.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 68.3%

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

   5. Taylor expanded in z around inf 64.7%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]
   6. Step-by-step derivation

      1. *-commutative 64.7%

         \[\leadsto \color{blue}{\frac{z}{t} \cdot y} \]

      2. associate-/r/ 69.3%

         \[\leadsto \color{blue}{\frac{z}{\frac{t}{y}}} \]

   7. Applied egg-rr 69.3%

      \[\leadsto \color{blue}{\frac{z}{\frac{t}{y}}} \]

   if -1.8e-59 < z < -8.49999999999999932e-179 or -1.04999999999999992e-285 < z < 4.0999999999999997e-272

   1. Initial program 91.3%

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

      1. +-commutative 91.3%

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

      2. associate-*r/ 94.4%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 94.4%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 94.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 77.6%

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

   5. Taylor expanded in z around 0 77.6%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 77.6%

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

      2. sub-neg 77.6%

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

      3. div-sub 77.6%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 77.6%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   8. Taylor expanded in z around 0 58.6%

      \[\leadsto \color{blue}{-1 \cdot \frac{y \cdot x}{t}} \]
   9. Step-by-step derivation

      1. associate-*r/ 58.6%

         \[\leadsto \color{blue}{\frac{-1 \cdot \left(y \cdot x\right)}{t}} \]

      2. associate-*l/ 58.7%

         \[\leadsto \color{blue}{\frac{-1}{t} \cdot \left(y \cdot x\right)} \]

      3. metadata-eval 58.7%

         \[\leadsto \frac{\color{blue}{\frac{1}{-1}}}{t} \cdot \left(y \cdot x\right) \]

      4. associate-/r* 58.7%

         \[\leadsto \color{blue}{\frac{1}{-1 \cdot t}} \cdot \left(y \cdot x\right) \]

      5. neg-mul-1 58.7%

         \[\leadsto \frac{1}{\color{blue}{-t}} \cdot \left(y \cdot x\right) \]

      6. *-commutative 58.7%

         \[\leadsto \color{blue}{\left(y \cdot x\right) \cdot \frac{1}{-t}} \]

      7. *-commutative 58.7%

         \[\leadsto \color{blue}{\left(x \cdot y\right)} \cdot \frac{1}{-t} \]

      8. associate-*l* 66.8%

         \[\leadsto \color{blue}{x \cdot \left(y \cdot \frac{1}{-t}\right)} \]

      9. *-commutative 66.8%

         \[\leadsto x \cdot \color{blue}{\left(\frac{1}{-t} \cdot y\right)} \]

      10. associate-*l/ 66.9%

          \[\leadsto x \cdot \color{blue}{\frac{1 \cdot y}{-t}} \]

      11. *-lft-identity 66.9%

          \[\leadsto x \cdot \frac{\color{blue}{y}}{-t} \]

   10. Simplified 66.9%

       \[\leadsto \color{blue}{x \cdot \frac{y}{-t}} \]
3. Recombined 4 regimes into one program.

4. Final simplification 68.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.2 \cdot 10^{+141}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq -1.05 \cdot 10^{+101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -5.8 \cdot 10^{+15}:\\ \;\;\;\;\frac{z}{\frac{t}{y}}\\ \mathbf{elif}\;z \leq -1.8 \cdot 10^{-59}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -8.5 \cdot 10^{-179}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \mathbf{elif}\;z \leq -1.05 \cdot 10^{-285}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 4.1 \cdot 10^{-272}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \mathbf{elif}\;z \leq 1.6 \cdot 10^{-85}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{y}}\\ \end{array} \]

Alternative 3: 49.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{z}{\frac{t}{y}}\\ \mathbf{if}\;z \leq -2.4 \cdot 10^{+141}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq -8.4 \cdot 10^{+101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.3 \cdot 10^{+15}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -3.9 \cdot 10^{-61}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.05 \cdot 10^{-177}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \mathbf{elif}\;z \leq -6.6 \cdot 10^{-286}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{-273}:\\ \;\;\;\;y \cdot \frac{-x}{t}\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{-85}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ z (/ t y))))
   (if (<= z -2.4e+141)
     (* y (/ z t))
     (if (<= z -8.4e+101)
       x
       (if (<= z -2.3e+15)
         t_1
         (if (<= z -3.9e-61)
           x
           (if (<= z -2.05e-177)
             (* x (/ y (- t)))
             (if (<= z -6.6e-286)
               x
               (if (<= z 2.1e-273)
                 (* y (/ (- x) t))
                 (if (<= z 1.4e-85) x t_1))))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z / (t / y);
	double tmp;
	if (z <= -2.4e+141) {
		tmp = y * (z / t);
	} else if (z <= -8.4e+101) {
		tmp = x;
	} else if (z <= -2.3e+15) {
		tmp = t_1;
	} else if (z <= -3.9e-61) {
		tmp = x;
	} else if (z <= -2.05e-177) {
		tmp = x * (y / -t);
	} else if (z <= -6.6e-286) {
		tmp = x;
	} else if (z <= 2.1e-273) {
		tmp = y * (-x / t);
	} else if (z <= 1.4e-85) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = z / (t / y)
    if (z <= (-2.4d+141)) then
        tmp = y * (z / t)
    else if (z <= (-8.4d+101)) then
        tmp = x
    else if (z <= (-2.3d+15)) then
        tmp = t_1
    else if (z <= (-3.9d-61)) then
        tmp = x
    else if (z <= (-2.05d-177)) then
        tmp = x * (y / -t)
    else if (z <= (-6.6d-286)) then
        tmp = x
    else if (z <= 2.1d-273) then
        tmp = y * (-x / t)
    else if (z <= 1.4d-85) then
        tmp = x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = z / (t / y);
	double tmp;
	if (z <= -2.4e+141) {
		tmp = y * (z / t);
	} else if (z <= -8.4e+101) {
		tmp = x;
	} else if (z <= -2.3e+15) {
		tmp = t_1;
	} else if (z <= -3.9e-61) {
		tmp = x;
	} else if (z <= -2.05e-177) {
		tmp = x * (y / -t);
	} else if (z <= -6.6e-286) {
		tmp = x;
	} else if (z <= 2.1e-273) {
		tmp = y * (-x / t);
	} else if (z <= 1.4e-85) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z / (t / y)
	tmp = 0
	if z <= -2.4e+141:
		tmp = y * (z / t)
	elif z <= -8.4e+101:
		tmp = x
	elif z <= -2.3e+15:
		tmp = t_1
	elif z <= -3.9e-61:
		tmp = x
	elif z <= -2.05e-177:
		tmp = x * (y / -t)
	elif z <= -6.6e-286:
		tmp = x
	elif z <= 2.1e-273:
		tmp = y * (-x / t)
	elif z <= 1.4e-85:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z / Float64(t / y))
	tmp = 0.0
	if (z <= -2.4e+141)
		tmp = Float64(y * Float64(z / t));
	elseif (z <= -8.4e+101)
		tmp = x;
	elseif (z <= -2.3e+15)
		tmp = t_1;
	elseif (z <= -3.9e-61)
		tmp = x;
	elseif (z <= -2.05e-177)
		tmp = Float64(x * Float64(y / Float64(-t)));
	elseif (z <= -6.6e-286)
		tmp = x;
	elseif (z <= 2.1e-273)
		tmp = Float64(y * Float64(Float64(-x) / t));
	elseif (z <= 1.4e-85)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z / (t / y);
	tmp = 0.0;
	if (z <= -2.4e+141)
		tmp = y * (z / t);
	elseif (z <= -8.4e+101)
		tmp = x;
	elseif (z <= -2.3e+15)
		tmp = t_1;
	elseif (z <= -3.9e-61)
		tmp = x;
	elseif (z <= -2.05e-177)
		tmp = x * (y / -t);
	elseif (z <= -6.6e-286)
		tmp = x;
	elseif (z <= 2.1e-273)
		tmp = y * (-x / t);
	elseif (z <= 1.4e-85)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.4e+141], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -8.4e+101], x, If[LessEqual[z, -2.3e+15], t$95$1, If[LessEqual[z, -3.9e-61], x, If[LessEqual[z, -2.05e-177], N[(x * N[(y / (-t)), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -6.6e-286], x, If[LessEqual[z, 2.1e-273], N[(y * N[((-x) / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.4e-85], x, t$95$1]]]]]]]]]

Derivation

1. Split input into 5 regimes

2. if z < -2.39999999999999997e141

   1. Initial program 92.5%

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

      1. +-commutative 92.5%

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

      2. associate-*r/ 90.0%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 90.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 90.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 72.3%

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

   5. Taylor expanded in z around inf 67.3%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]

   if -2.39999999999999997e141 < z < -8.4000000000000001e101 or -2.3e15 < z < -3.90000000000000033e-61 or -2.05e-177 < z < -6.5999999999999997e-286 or 2.1000000000000002e-273 < z < 1.40000000000000008e-85

   1. Initial program 88.4%

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

      1. +-commutative 88.4%

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

      2. associate-*r/ 96.1%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 96.1%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 96.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around 0 68.6%

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

   if -8.4000000000000001e101 < z < -2.3e15 or 1.40000000000000008e-85 < z

   1. Initial program 88.6%

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

      1. +-commutative 88.6%

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

      2. associate-*r/ 91.0%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 91.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 91.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 68.3%

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

   5. Taylor expanded in z around inf 64.7%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]
   6. Step-by-step derivation

      1. *-commutative 64.7%

         \[\leadsto \color{blue}{\frac{z}{t} \cdot y} \]

      2. associate-/r/ 69.3%

         \[\leadsto \color{blue}{\frac{z}{\frac{t}{y}}} \]

   7. Applied egg-rr 69.3%

      \[\leadsto \color{blue}{\frac{z}{\frac{t}{y}}} \]

   if -3.90000000000000033e-61 < z < -2.05e-177

   1. Initial program 91.9%

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

      1. +-commutative 91.9%

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

      2. associate-*r/ 91.9%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 91.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 91.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 75.5%

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

   5. Taylor expanded in z around 0 75.5%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 75.5%

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

      2. sub-neg 75.5%

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

      3. div-sub 75.5%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 75.5%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   8. Taylor expanded in z around 0 56.0%

      \[\leadsto \color{blue}{-1 \cdot \frac{y \cdot x}{t}} \]
   9. Step-by-step derivation

      1. associate-*r/ 56.0%

         \[\leadsto \color{blue}{\frac{-1 \cdot \left(y \cdot x\right)}{t}} \]

      2. associate-*l/ 56.0%

         \[\leadsto \color{blue}{\frac{-1}{t} \cdot \left(y \cdot x\right)} \]

      3. metadata-eval 56.0%

         \[\leadsto \frac{\color{blue}{\frac{1}{-1}}}{t} \cdot \left(y \cdot x\right) \]

      4. associate-/r* 56.0%

         \[\leadsto \color{blue}{\frac{1}{-1 \cdot t}} \cdot \left(y \cdot x\right) \]

      5. neg-mul-1 56.0%

         \[\leadsto \frac{1}{\color{blue}{-t}} \cdot \left(y \cdot x\right) \]

      6. *-commutative 56.0%

         \[\leadsto \color{blue}{\left(y \cdot x\right) \cdot \frac{1}{-t}} \]

      7. *-commutative 56.0%

         \[\leadsto \color{blue}{\left(x \cdot y\right)} \cdot \frac{1}{-t} \]

      8. associate-*l* 63.7%

         \[\leadsto \color{blue}{x \cdot \left(y \cdot \frac{1}{-t}\right)} \]

      9. *-commutative 63.7%

         \[\leadsto x \cdot \color{blue}{\left(\frac{1}{-t} \cdot y\right)} \]

      10. associate-*l/ 63.8%

          \[\leadsto x \cdot \color{blue}{\frac{1 \cdot y}{-t}} \]

      11. *-lft-identity 63.8%

          \[\leadsto x \cdot \frac{\color{blue}{y}}{-t} \]

   10. Simplified 63.8%

       \[\leadsto \color{blue}{x \cdot \frac{y}{-t}} \]

   if -6.5999999999999997e-286 < z < 2.1000000000000002e-273

   1. Initial program 89.9%

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

      1. +-commutative 89.9%

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

      2. associate-*r/ 99.9%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 82.4%

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

   5. Taylor expanded in z around 0 74.1%

      \[\leadsto y \cdot \color{blue}{\left(-1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. associate-*r/ 74.1%

         \[\leadsto y \cdot \color{blue}{\frac{-1 \cdot x}{t}} \]

      2. neg-mul-1 74.1%

         \[\leadsto y \cdot \frac{\color{blue}{-x}}{t} \]

   7. Simplified 74.1%

      \[\leadsto y \cdot \color{blue}{\frac{-x}{t}} \]
3. Recombined 5 regimes into one program.

4. Final simplification 68.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.4 \cdot 10^{+141}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq -8.4 \cdot 10^{+101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.3 \cdot 10^{+15}:\\ \;\;\;\;\frac{z}{\frac{t}{y}}\\ \mathbf{elif}\;z \leq -3.9 \cdot 10^{-61}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.05 \cdot 10^{-177}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \mathbf{elif}\;z \leq -6.6 \cdot 10^{-286}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{-273}:\\ \;\;\;\;y \cdot \frac{-x}{t}\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{-85}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{y}}\\ \end{array} \]

Alternative 4: 80.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -3.15 \cdot 10^{-106}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq -1.46 \cdot 10^{-221} \lor \neg \left(t \leq 5.4 \cdot 10^{-308}\right) \land t \leq 6.6 \cdot 10^{-57}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= t -3.15e-106)
   (+ x (/ y (/ t z)))
   (if (or (<= t -1.46e-221) (and (not (<= t 5.4e-308)) (<= t 6.6e-57)))
     (* y (/ (- z x) t))
     (+ x (* z (/ y t))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -3.15e-106) {
		tmp = x + (y / (t / z));
	} else if ((t <= -1.46e-221) || (!(t <= 5.4e-308) && (t <= 6.6e-57))) {
		tmp = y * ((z - x) / t);
	} else {
		tmp = x + (z * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (t <= (-3.15d-106)) then
        tmp = x + (y / (t / z))
    else if ((t <= (-1.46d-221)) .or. (.not. (t <= 5.4d-308)) .and. (t <= 6.6d-57)) then
        tmp = y * ((z - x) / t)
    else
        tmp = x + (z * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -3.15e-106) {
		tmp = x + (y / (t / z));
	} else if ((t <= -1.46e-221) || (!(t <= 5.4e-308) && (t <= 6.6e-57))) {
		tmp = y * ((z - x) / t);
	} else {
		tmp = x + (z * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if t <= -3.15e-106:
		tmp = x + (y / (t / z))
	elif (t <= -1.46e-221) or (not (t <= 5.4e-308) and (t <= 6.6e-57)):
		tmp = y * ((z - x) / t)
	else:
		tmp = x + (z * (y / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (t <= -3.15e-106)
		tmp = Float64(x + Float64(y / Float64(t / z)));
	elseif ((t <= -1.46e-221) || (!(t <= 5.4e-308) && (t <= 6.6e-57)))
		tmp = Float64(y * Float64(Float64(z - x) / t));
	else
		tmp = Float64(x + Float64(z * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (t <= -3.15e-106)
		tmp = x + (y / (t / z));
	elseif ((t <= -1.46e-221) || (~((t <= 5.4e-308)) && (t <= 6.6e-57)))
		tmp = y * ((z - x) / t);
	else
		tmp = x + (z * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[t, -3.15e-106], N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[t, -1.46e-221], And[N[Not[LessEqual[t, 5.4e-308]], $MachinePrecision], LessEqual[t, 6.6e-57]]], N[(y * N[(N[(z - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(x + N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -3.1500000000000002e-106

   1. Initial program 86.7%

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

      1. associate-/l* 98.7%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{t}{z - x}}} \]

   3. Simplified 98.7%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{t}{z - x}}} \]

   4. Taylor expanded in z around inf 85.9%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{t}{z}}} \]

   if -3.1500000000000002e-106 < t < -1.46000000000000004e-221 or 5.4000000000000003e-308 < t < 6.5999999999999997e-57

   1. Initial program 98.3%

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

      1. +-commutative 98.3%

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

      2. associate-*r/ 94.6%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 94.6%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 94.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 85.4%

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

   5. Taylor expanded in z around 0 85.4%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 85.4%

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

      2. sub-neg 85.4%

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

      3. div-sub 86.7%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 86.7%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   if -1.46000000000000004e-221 < t < 5.4000000000000003e-308 or 6.5999999999999997e-57 < t

   1. Initial program 85.4%

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

      1. associate-*l/ 98.1%

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

   3. Simplified 98.1%

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

   4. Taylor expanded in z around inf 82.5%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. associate-*l/ 89.3%

         \[\leadsto x + \color{blue}{\frac{y}{t} \cdot z} \]

      2. *-commutative 89.3%

         \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   6. Simplified 89.3%

      \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 87.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -3.15 \cdot 10^{-106}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq -1.46 \cdot 10^{-221} \lor \neg \left(t \leq 5.4 \cdot 10^{-308}\right) \land t \leq 6.6 \cdot 10^{-57}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \end{array} \]

Alternative 5: 83.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + z \cdot \frac{y}{t}\\ t_2 := y \cdot \frac{z - x}{t}\\ \mathbf{if}\;y \leq -1.7 \cdot 10^{+53}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;y \leq -4.5 \cdot 10^{-93}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -1.4 \cdot 10^{-118}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \mathbf{elif}\;y \leq 2.1 \cdot 10^{+91}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ x (* z (/ y t)))) (t_2 (* y (/ (- z x) t))))
   (if (<= y -1.7e+53)
     t_2
     (if (<= y -4.5e-93)
       t_1
       (if (<= y -1.4e-118) (* x (/ y (- t))) (if (<= y 2.1e+91) t_1 t_2))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x + (z * (y / t));
	double t_2 = y * ((z - x) / t);
	double tmp;
	if (y <= -1.7e+53) {
		tmp = t_2;
	} else if (y <= -4.5e-93) {
		tmp = t_1;
	} else if (y <= -1.4e-118) {
		tmp = x * (y / -t);
	} else if (y <= 2.1e+91) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = x + (z * (y / t))
    t_2 = y * ((z - x) / t)
    if (y <= (-1.7d+53)) then
        tmp = t_2
    else if (y <= (-4.5d-93)) then
        tmp = t_1
    else if (y <= (-1.4d-118)) then
        tmp = x * (y / -t)
    else if (y <= 2.1d+91) 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 t_1 = x + (z * (y / t));
	double t_2 = y * ((z - x) / t);
	double tmp;
	if (y <= -1.7e+53) {
		tmp = t_2;
	} else if (y <= -4.5e-93) {
		tmp = t_1;
	} else if (y <= -1.4e-118) {
		tmp = x * (y / -t);
	} else if (y <= 2.1e+91) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x + (z * (y / t))
	t_2 = y * ((z - x) / t)
	tmp = 0
	if y <= -1.7e+53:
		tmp = t_2
	elif y <= -4.5e-93:
		tmp = t_1
	elif y <= -1.4e-118:
		tmp = x * (y / -t)
	elif y <= 2.1e+91:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x + Float64(z * Float64(y / t)))
	t_2 = Float64(y * Float64(Float64(z - x) / t))
	tmp = 0.0
	if (y <= -1.7e+53)
		tmp = t_2;
	elseif (y <= -4.5e-93)
		tmp = t_1;
	elseif (y <= -1.4e-118)
		tmp = Float64(x * Float64(y / Float64(-t)));
	elseif (y <= 2.1e+91)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x + (z * (y / t));
	t_2 = y * ((z - x) / t);
	tmp = 0.0;
	if (y <= -1.7e+53)
		tmp = t_2;
	elseif (y <= -4.5e-93)
		tmp = t_1;
	elseif (y <= -1.4e-118)
		tmp = x * (y / -t);
	elseif (y <= 2.1e+91)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x + N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y * N[(N[(z - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.7e+53], t$95$2, If[LessEqual[y, -4.5e-93], t$95$1, If[LessEqual[y, -1.4e-118], N[(x * N[(y / (-t)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.1e+91], t$95$1, t$95$2]]]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.69999999999999999e53 or 2.10000000000000008e91 < y

   1. Initial program 76.2%

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

      1. +-commutative 76.2%

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

      2. associate-*r/ 99.9%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 85.4%

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

   5. Taylor expanded in z around 0 85.4%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 85.4%

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

      2. sub-neg 85.4%

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

      3. div-sub 85.4%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 85.4%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   if -1.69999999999999999e53 < y < -4.5000000000000002e-93 or -1.4e-118 < y < 2.10000000000000008e91

   1. Initial program 97.4%

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

      1. associate-*l/ 98.2%

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

   3. Simplified 98.2%

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

   4. Taylor expanded in z around inf 89.0%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. associate-*l/ 88.0%

         \[\leadsto x + \color{blue}{\frac{y}{t} \cdot z} \]

      2. *-commutative 88.0%

         \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   6. Simplified 88.0%

      \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   if -4.5000000000000002e-93 < y < -1.4e-118

   1. Initial program 97.3%

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

      1. +-commutative 97.3%

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

      2. associate-*r/ 62.2%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 62.2%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 62.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 62.2%

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

   5. Taylor expanded in z around 0 62.2%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 62.2%

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

      2. sub-neg 62.2%

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

      3. div-sub 62.2%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 62.2%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   8. Taylor expanded in z around 0 97.3%

      \[\leadsto \color{blue}{-1 \cdot \frac{y \cdot x}{t}} \]
   9. Step-by-step derivation

      1. associate-*r/ 97.3%

         \[\leadsto \color{blue}{\frac{-1 \cdot \left(y \cdot x\right)}{t}} \]

      2. associate-*l/ 97.6%

         \[\leadsto \color{blue}{\frac{-1}{t} \cdot \left(y \cdot x\right)} \]

      3. metadata-eval 97.6%

         \[\leadsto \frac{\color{blue}{\frac{1}{-1}}}{t} \cdot \left(y \cdot x\right) \]

      4. associate-/r* 97.6%

         \[\leadsto \color{blue}{\frac{1}{-1 \cdot t}} \cdot \left(y \cdot x\right) \]

      5. neg-mul-1 97.6%

         \[\leadsto \frac{1}{\color{blue}{-t}} \cdot \left(y \cdot x\right) \]

      6. *-commutative 97.6%

         \[\leadsto \color{blue}{\left(y \cdot x\right) \cdot \frac{1}{-t}} \]

      7. *-commutative 97.6%

         \[\leadsto \color{blue}{\left(x \cdot y\right)} \cdot \frac{1}{-t} \]

      8. associate-*l* 100.0%

         \[\leadsto \color{blue}{x \cdot \left(y \cdot \frac{1}{-t}\right)} \]

      9. *-commutative 100.0%

         \[\leadsto x \cdot \color{blue}{\left(\frac{1}{-t} \cdot y\right)} \]

      10. associate-*l/ 100.0%

          \[\leadsto x \cdot \color{blue}{\frac{1 \cdot y}{-t}} \]

      11. *-lft-identity 100.0%

          \[\leadsto x \cdot \frac{\color{blue}{y}}{-t} \]

   10. Simplified 100.0%

       \[\leadsto \color{blue}{x \cdot \frac{y}{-t}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 87.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.7 \cdot 10^{+53}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \mathbf{elif}\;y \leq -4.5 \cdot 10^{-93}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \mathbf{elif}\;y \leq -1.4 \cdot 10^{-118}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \mathbf{elif}\;y \leq 2.1 \cdot 10^{+91}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \end{array} \]

Alternative 6: 80.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z - x}{t}\\ \mathbf{if}\;t \leq -2.25 \cdot 10^{-106}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq -1.6 \cdot 10^{-221}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;t \leq 4.4 \cdot 10^{-305}:\\ \;\;\;\;x + \frac{z}{\frac{t}{y}}\\ \mathbf{elif}\;t \leq 1.6 \cdot 10^{-57}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (/ (- z x) t))))
   (if (<= t -2.25e-106)
     (+ x (/ y (/ t z)))
     (if (<= t -1.6e-221)
       t_1
       (if (<= t 4.4e-305)
         (+ x (/ z (/ t y)))
         (if (<= t 1.6e-57) t_1 (+ x (* z (/ y t)))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * ((z - x) / t);
	double tmp;
	if (t <= -2.25e-106) {
		tmp = x + (y / (t / z));
	} else if (t <= -1.6e-221) {
		tmp = t_1;
	} else if (t <= 4.4e-305) {
		tmp = x + (z / (t / y));
	} else if (t <= 1.6e-57) {
		tmp = t_1;
	} else {
		tmp = x + (z * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * ((z - x) / t)
    if (t <= (-2.25d-106)) then
        tmp = x + (y / (t / z))
    else if (t <= (-1.6d-221)) then
        tmp = t_1
    else if (t <= 4.4d-305) then
        tmp = x + (z / (t / y))
    else if (t <= 1.6d-57) then
        tmp = t_1
    else
        tmp = x + (z * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * ((z - x) / t);
	double tmp;
	if (t <= -2.25e-106) {
		tmp = x + (y / (t / z));
	} else if (t <= -1.6e-221) {
		tmp = t_1;
	} else if (t <= 4.4e-305) {
		tmp = x + (z / (t / y));
	} else if (t <= 1.6e-57) {
		tmp = t_1;
	} else {
		tmp = x + (z * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * ((z - x) / t)
	tmp = 0
	if t <= -2.25e-106:
		tmp = x + (y / (t / z))
	elif t <= -1.6e-221:
		tmp = t_1
	elif t <= 4.4e-305:
		tmp = x + (z / (t / y))
	elif t <= 1.6e-57:
		tmp = t_1
	else:
		tmp = x + (z * (y / t))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(Float64(z - x) / t))
	tmp = 0.0
	if (t <= -2.25e-106)
		tmp = Float64(x + Float64(y / Float64(t / z)));
	elseif (t <= -1.6e-221)
		tmp = t_1;
	elseif (t <= 4.4e-305)
		tmp = Float64(x + Float64(z / Float64(t / y)));
	elseif (t <= 1.6e-57)
		tmp = t_1;
	else
		tmp = Float64(x + Float64(z * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * ((z - x) / t);
	tmp = 0.0;
	if (t <= -2.25e-106)
		tmp = x + (y / (t / z));
	elseif (t <= -1.6e-221)
		tmp = t_1;
	elseif (t <= 4.4e-305)
		tmp = x + (z / (t / y));
	elseif (t <= 1.6e-57)
		tmp = t_1;
	else
		tmp = x + (z * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(N[(z - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -2.25e-106], N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -1.6e-221], t$95$1, If[LessEqual[t, 4.4e-305], N[(x + N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.6e-57], t$95$1, N[(x + N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if t < -2.24999999999999978e-106

   1. Initial program 86.7%

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

      1. associate-/l* 98.7%

         \[\leadsto x + \color{blue}{\frac{y}{\frac{t}{z - x}}} \]

   3. Simplified 98.7%

      \[\leadsto \color{blue}{x + \frac{y}{\frac{t}{z - x}}} \]

   4. Taylor expanded in z around inf 85.9%

      \[\leadsto x + \frac{y}{\color{blue}{\frac{t}{z}}} \]

   if -2.24999999999999978e-106 < t < -1.60000000000000008e-221 or 4.39999999999999993e-305 < t < 1.6e-57

   1. Initial program 98.3%

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

      1. +-commutative 98.3%

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

      2. associate-*r/ 94.6%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 94.6%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 94.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 85.4%

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

   5. Taylor expanded in z around 0 85.4%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 85.4%

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

      2. sub-neg 85.4%

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

      3. div-sub 86.7%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 86.7%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   if -1.60000000000000008e-221 < t < 4.39999999999999993e-305

   1. Initial program 99.9%

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

      1. associate-*l/ 95.9%

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

   3. Simplified 95.9%

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

   4. Taylor expanded in z around inf 76.3%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. associate-*l/ 76.3%

         \[\leadsto x + \color{blue}{\frac{y}{t} \cdot z} \]

      2. *-commutative 76.3%

         \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   6. Simplified 76.3%

      \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]
   7. Step-by-step derivation

      1. clear-num 76.2%

         \[\leadsto x + z \cdot \color{blue}{\frac{1}{\frac{t}{y}}} \]

      2. div-inv 76.4%

         \[\leadsto x + \color{blue}{\frac{z}{\frac{t}{y}}} \]

   8. Applied egg-rr 76.4%

      \[\leadsto x + \color{blue}{\frac{z}{\frac{t}{y}}} \]

   if 1.6e-57 < t

   1. Initial program 80.6%

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

      1. associate-*l/ 98.9%

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

   3. Simplified 98.9%

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

   4. Taylor expanded in z around inf 84.5%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. associate-*l/ 93.6%

         \[\leadsto x + \color{blue}{\frac{y}{t} \cdot z} \]

      2. *-commutative 93.6%

         \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   6. Simplified 93.6%

      \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]
3. Recombined 4 regimes into one program.

4. Final simplification 87.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -2.25 \cdot 10^{-106}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq -1.6 \cdot 10^{-221}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \mathbf{elif}\;t \leq 4.4 \cdot 10^{-305}:\\ \;\;\;\;x + \frac{z}{\frac{t}{y}}\\ \mathbf{elif}\;t \leq 1.6 \cdot 10^{-57}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \mathbf{else}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \end{array} \]

Alternative 7: 83.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z - x}{t}\\ \mathbf{if}\;y \leq -8.4 \cdot 10^{+51}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -4.5 \cdot 10^{-93}:\\ \;\;\;\;x + \frac{z}{\frac{t}{y}}\\ \mathbf{elif}\;y \leq -1.4 \cdot 10^{-118}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+94}:\\ \;\;\;\;x + \frac{y \cdot z}{t}\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (/ (- z x) t))))
   (if (<= y -8.4e+51)
     t_1
     (if (<= y -4.5e-93)
       (+ x (/ z (/ t y)))
       (if (<= y -1.4e-118)
         (* x (/ y (- t)))
         (if (<= y 1.9e+94) (+ x (/ (* y z) t)) t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * ((z - x) / t);
	double tmp;
	if (y <= -8.4e+51) {
		tmp = t_1;
	} else if (y <= -4.5e-93) {
		tmp = x + (z / (t / y));
	} else if (y <= -1.4e-118) {
		tmp = x * (y / -t);
	} else if (y <= 1.9e+94) {
		tmp = x + ((y * z) / t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * ((z - x) / t)
    if (y <= (-8.4d+51)) then
        tmp = t_1
    else if (y <= (-4.5d-93)) then
        tmp = x + (z / (t / y))
    else if (y <= (-1.4d-118)) then
        tmp = x * (y / -t)
    else if (y <= 1.9d+94) then
        tmp = x + ((y * z) / t)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * ((z - x) / t);
	double tmp;
	if (y <= -8.4e+51) {
		tmp = t_1;
	} else if (y <= -4.5e-93) {
		tmp = x + (z / (t / y));
	} else if (y <= -1.4e-118) {
		tmp = x * (y / -t);
	} else if (y <= 1.9e+94) {
		tmp = x + ((y * z) / t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * ((z - x) / t)
	tmp = 0
	if y <= -8.4e+51:
		tmp = t_1
	elif y <= -4.5e-93:
		tmp = x + (z / (t / y))
	elif y <= -1.4e-118:
		tmp = x * (y / -t)
	elif y <= 1.9e+94:
		tmp = x + ((y * z) / t)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(Float64(z - x) / t))
	tmp = 0.0
	if (y <= -8.4e+51)
		tmp = t_1;
	elseif (y <= -4.5e-93)
		tmp = Float64(x + Float64(z / Float64(t / y)));
	elseif (y <= -1.4e-118)
		tmp = Float64(x * Float64(y / Float64(-t)));
	elseif (y <= 1.9e+94)
		tmp = Float64(x + Float64(Float64(y * z) / t));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * ((z - x) / t);
	tmp = 0.0;
	if (y <= -8.4e+51)
		tmp = t_1;
	elseif (y <= -4.5e-93)
		tmp = x + (z / (t / y));
	elseif (y <= -1.4e-118)
		tmp = x * (y / -t);
	elseif (y <= 1.9e+94)
		tmp = x + ((y * z) / t);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(N[(z - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -8.4e+51], t$95$1, If[LessEqual[y, -4.5e-93], N[(x + N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -1.4e-118], N[(x * N[(y / (-t)), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.9e+94], N[(x + N[(N[(y * z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 4 regimes

2. if y < -8.4000000000000005e51 or 1.8999999999999998e94 < y

   1. Initial program 76.2%

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

      1. +-commutative 76.2%

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

      2. associate-*r/ 99.9%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 99.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 85.4%

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

   5. Taylor expanded in z around 0 85.4%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 85.4%

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

      2. sub-neg 85.4%

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

      3. div-sub 85.4%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 85.4%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   if -8.4000000000000005e51 < y < -4.5000000000000002e-93

   1. Initial program 96.9%

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

      1. associate-*l/ 97.3%

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

   3. Simplified 97.3%

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

   4. Taylor expanded in z around inf 86.1%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. associate-*l/ 86.4%

         \[\leadsto x + \color{blue}{\frac{y}{t} \cdot z} \]

      2. *-commutative 86.4%

         \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   6. Simplified 86.4%

      \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]
   7. Step-by-step derivation

      1. clear-num 86.3%

         \[\leadsto x + z \cdot \color{blue}{\frac{1}{\frac{t}{y}}} \]

      2. div-inv 86.4%

         \[\leadsto x + \color{blue}{\frac{z}{\frac{t}{y}}} \]

   8. Applied egg-rr 86.4%

      \[\leadsto x + \color{blue}{\frac{z}{\frac{t}{y}}} \]

   if -4.5000000000000002e-93 < y < -1.4e-118

   1. Initial program 97.3%

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

      1. +-commutative 97.3%

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

      2. associate-*r/ 62.2%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 62.2%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 62.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 62.2%

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

   5. Taylor expanded in z around 0 62.2%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 62.2%

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

      2. sub-neg 62.2%

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

      3. div-sub 62.2%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 62.2%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   8. Taylor expanded in z around 0 97.3%

      \[\leadsto \color{blue}{-1 \cdot \frac{y \cdot x}{t}} \]
   9. Step-by-step derivation

      1. associate-*r/ 97.3%

         \[\leadsto \color{blue}{\frac{-1 \cdot \left(y \cdot x\right)}{t}} \]

      2. associate-*l/ 97.6%

         \[\leadsto \color{blue}{\frac{-1}{t} \cdot \left(y \cdot x\right)} \]

      3. metadata-eval 97.6%

         \[\leadsto \frac{\color{blue}{\frac{1}{-1}}}{t} \cdot \left(y \cdot x\right) \]

      4. associate-/r* 97.6%

         \[\leadsto \color{blue}{\frac{1}{-1 \cdot t}} \cdot \left(y \cdot x\right) \]

      5. neg-mul-1 97.6%

         \[\leadsto \frac{1}{\color{blue}{-t}} \cdot \left(y \cdot x\right) \]

      6. *-commutative 97.6%

         \[\leadsto \color{blue}{\left(y \cdot x\right) \cdot \frac{1}{-t}} \]

      7. *-commutative 97.6%

         \[\leadsto \color{blue}{\left(x \cdot y\right)} \cdot \frac{1}{-t} \]

      8. associate-*l* 100.0%

         \[\leadsto \color{blue}{x \cdot \left(y \cdot \frac{1}{-t}\right)} \]

      9. *-commutative 100.0%

         \[\leadsto x \cdot \color{blue}{\left(\frac{1}{-t} \cdot y\right)} \]

      10. associate-*l/ 100.0%

          \[\leadsto x \cdot \color{blue}{\frac{1 \cdot y}{-t}} \]

      11. *-lft-identity 100.0%

          \[\leadsto x \cdot \frac{\color{blue}{y}}{-t} \]

   10. Simplified 100.0%

       \[\leadsto \color{blue}{x \cdot \frac{y}{-t}} \]

   if -1.4e-118 < y < 1.8999999999999998e94

   1. Initial program 97.6%

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

      1. associate-*l/ 98.5%

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

   3. Simplified 98.5%

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

   4. Taylor expanded in z around inf 89.9%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
3. Recombined 4 regimes into one program.

4. Final simplification 87.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -8.4 \cdot 10^{+51}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \mathbf{elif}\;y \leq -4.5 \cdot 10^{-93}:\\ \;\;\;\;x + \frac{z}{\frac{t}{y}}\\ \mathbf{elif}\;y \leq -1.4 \cdot 10^{-118}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \mathbf{elif}\;y \leq 1.9 \cdot 10^{+94}:\\ \;\;\;\;x + \frac{y \cdot z}{t}\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \end{array} \]

Alternative 8: 50.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.2 \cdot 10^{+141} \lor \neg \left(z \leq -8.4 \cdot 10^{+101}\right) \land \left(z \leq -8 \cdot 10^{+14} \lor \neg \left(z \leq 1.4 \cdot 10^{-85}\right)\right):\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -2.2e+141)
         (and (not (<= z -8.4e+101)) (or (<= z -8e+14) (not (<= z 1.4e-85)))))
   (* y (/ z t))
   x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2.2e+141) || (!(z <= -8.4e+101) && ((z <= -8e+14) || !(z <= 1.4e-85)))) {
		tmp = y * (z / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-2.2d+141)) .or. (.not. (z <= (-8.4d+101))) .and. (z <= (-8d+14)) .or. (.not. (z <= 1.4d-85))) then
        tmp = y * (z / t)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -2.2e+141) || (!(z <= -8.4e+101) && ((z <= -8e+14) || !(z <= 1.4e-85)))) {
		tmp = y * (z / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -2.2e+141) or (not (z <= -8.4e+101) and ((z <= -8e+14) or not (z <= 1.4e-85))):
		tmp = y * (z / t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -2.2e+141) || (!(z <= -8.4e+101) && ((z <= -8e+14) || !(z <= 1.4e-85))))
		tmp = Float64(y * Float64(z / t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -2.2e+141) || (~((z <= -8.4e+101)) && ((z <= -8e+14) || ~((z <= 1.4e-85)))))
		tmp = y * (z / t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -2.2e+141], And[N[Not[LessEqual[z, -8.4e+101]], $MachinePrecision], Or[LessEqual[z, -8e+14], N[Not[LessEqual[z, 1.4e-85]], $MachinePrecision]]]], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -2.2e141 or -8.4000000000000001e101 < z < -8e14 or 1.40000000000000008e-85 < z

   1. Initial program 89.8%

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

      1. +-commutative 89.8%

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

      2. associate-*r/ 90.7%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 90.7%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 90.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 69.6%

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

   5. Taylor expanded in z around inf 65.5%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]

   if -2.2e141 < z < -8.4000000000000001e101 or -8e14 < z < 1.40000000000000008e-85

   1. Initial program 89.2%

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

      1. +-commutative 89.2%

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

      2. associate-*r/ 95.7%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 95.7%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 95.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around 0 56.2%

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

4. Final simplification 60.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.2 \cdot 10^{+141} \lor \neg \left(z \leq -8.4 \cdot 10^{+101}\right) \land \left(z \leq -8 \cdot 10^{+14} \lor \neg \left(z \leq 1.4 \cdot 10^{-85}\right)\right):\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 9: 51.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.2 \cdot 10^{+141}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq -8.5 \cdot 10^{+101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.4 \cdot 10^{+16} \lor \neg \left(z \leq 1.22 \cdot 10^{-85}\right):\\ \;\;\;\;\frac{z}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -2.2e+141)
   (* y (/ z t))
   (if (<= z -8.5e+101)
     x
     (if (or (<= z -2.4e+16) (not (<= z 1.22e-85))) (/ z (/ t y)) x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -2.2e+141) {
		tmp = y * (z / t);
	} else if (z <= -8.5e+101) {
		tmp = x;
	} else if ((z <= -2.4e+16) || !(z <= 1.22e-85)) {
		tmp = z / (t / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (z <= (-2.2d+141)) then
        tmp = y * (z / t)
    else if (z <= (-8.5d+101)) then
        tmp = x
    else if ((z <= (-2.4d+16)) .or. (.not. (z <= 1.22d-85))) then
        tmp = z / (t / y)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -2.2e+141) {
		tmp = y * (z / t);
	} else if (z <= -8.5e+101) {
		tmp = x;
	} else if ((z <= -2.4e+16) || !(z <= 1.22e-85)) {
		tmp = z / (t / y);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -2.2e+141:
		tmp = y * (z / t)
	elif z <= -8.5e+101:
		tmp = x
	elif (z <= -2.4e+16) or not (z <= 1.22e-85):
		tmp = z / (t / y)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -2.2e+141)
		tmp = Float64(y * Float64(z / t));
	elseif (z <= -8.5e+101)
		tmp = x;
	elseif ((z <= -2.4e+16) || !(z <= 1.22e-85))
		tmp = Float64(z / Float64(t / y));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -2.2e+141)
		tmp = y * (z / t);
	elseif (z <= -8.5e+101)
		tmp = x;
	elseif ((z <= -2.4e+16) || ~((z <= 1.22e-85)))
		tmp = z / (t / y);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -2.2e+141], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, -8.5e+101], x, If[Or[LessEqual[z, -2.4e+16], N[Not[LessEqual[z, 1.22e-85]], $MachinePrecision]], N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision], x]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.2e141

   1. Initial program 92.5%

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

      1. +-commutative 92.5%

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

      2. associate-*r/ 90.0%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 90.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 90.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 72.3%

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

   5. Taylor expanded in z around inf 67.3%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]

   if -2.2e141 < z < -8.5000000000000001e101 or -2.4e16 < z < 1.22000000000000006e-85

   1. Initial program 89.2%

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

      1. +-commutative 89.2%

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

      2. associate-*r/ 95.7%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 95.7%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 95.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around 0 56.2%

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

   if -8.5000000000000001e101 < z < -2.4e16 or 1.22000000000000006e-85 < z

   1. Initial program 88.6%

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

      1. +-commutative 88.6%

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

      2. associate-*r/ 91.0%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 91.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 91.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 68.3%

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

   5. Taylor expanded in z around inf 64.7%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]
   6. Step-by-step derivation

      1. *-commutative 64.7%

         \[\leadsto \color{blue}{\frac{z}{t} \cdot y} \]

      2. associate-/r/ 69.3%

         \[\leadsto \color{blue}{\frac{z}{\frac{t}{y}}} \]

   7. Applied egg-rr 69.3%

      \[\leadsto \color{blue}{\frac{z}{\frac{t}{y}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 62.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.2 \cdot 10^{+141}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq -8.5 \cdot 10^{+101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.4 \cdot 10^{+16} \lor \neg \left(z \leq 1.22 \cdot 10^{-85}\right):\\ \;\;\;\;\frac{z}{\frac{t}{y}}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 10: 50.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t}\\ \mathbf{if}\;z \leq -2.2 \cdot 10^{+141}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1.35 \cdot 10^{+100}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -3.9 \cdot 10^{+14}:\\ \;\;\;\;\frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{-85}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* y (/ z t))))
   (if (<= z -2.2e+141)
     t_1
     (if (<= z -1.35e+100)
       x
       (if (<= z -3.9e+14) (/ y (/ t z)) (if (<= z 1.4e-85) x t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y * (z / t);
	double tmp;
	if (z <= -2.2e+141) {
		tmp = t_1;
	} else if (z <= -1.35e+100) {
		tmp = x;
	} else if (z <= -3.9e+14) {
		tmp = y / (t / z);
	} else if (z <= 1.4e-85) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (z / t)
    if (z <= (-2.2d+141)) then
        tmp = t_1
    else if (z <= (-1.35d+100)) then
        tmp = x
    else if (z <= (-3.9d+14)) then
        tmp = y / (t / z)
    else if (z <= 1.4d-85) then
        tmp = x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = y * (z / t);
	double tmp;
	if (z <= -2.2e+141) {
		tmp = t_1;
	} else if (z <= -1.35e+100) {
		tmp = x;
	} else if (z <= -3.9e+14) {
		tmp = y / (t / z);
	} else if (z <= 1.4e-85) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y * (z / t)
	tmp = 0
	if z <= -2.2e+141:
		tmp = t_1
	elif z <= -1.35e+100:
		tmp = x
	elif z <= -3.9e+14:
		tmp = y / (t / z)
	elif z <= 1.4e-85:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y * Float64(z / t))
	tmp = 0.0
	if (z <= -2.2e+141)
		tmp = t_1;
	elseif (z <= -1.35e+100)
		tmp = x;
	elseif (z <= -3.9e+14)
		tmp = Float64(y / Float64(t / z));
	elseif (z <= 1.4e-85)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = y * (z / t);
	tmp = 0.0;
	if (z <= -2.2e+141)
		tmp = t_1;
	elseif (z <= -1.35e+100)
		tmp = x;
	elseif (z <= -3.9e+14)
		tmp = y / (t / z);
	elseif (z <= 1.4e-85)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.2e+141], t$95$1, If[LessEqual[z, -1.35e+100], x, If[LessEqual[z, -3.9e+14], N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.4e-85], x, t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.2e141 or 1.40000000000000008e-85 < z

   1. Initial program 88.3%

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

      1. +-commutative 88.3%

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

      2. associate-*r/ 92.0%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 92.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 92.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 69.6%

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

   5. Taylor expanded in z around inf 64.9%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]

   if -2.2e141 < z < -1.34999999999999999e100 or -3.9e14 < z < 1.40000000000000008e-85

   1. Initial program 89.2%

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

      1. +-commutative 89.2%

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

      2. associate-*r/ 95.7%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 95.7%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 95.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around 0 56.2%

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

   if -1.34999999999999999e100 < z < -3.9e14

   1. Initial program 99.6%

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

      1. +-commutative 99.6%

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

      2. associate-*r/ 81.9%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 81.9%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 81.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 69.5%

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

   5. Taylor expanded in z around inf 70.0%

      \[\leadsto y \cdot \color{blue}{\frac{z}{t}} \]
   6. Step-by-step derivation

      1. clear-num 69.9%

         \[\leadsto y \cdot \color{blue}{\frac{1}{\frac{t}{z}}} \]

      2. un-div-inv 72.3%

         \[\leadsto \color{blue}{\frac{y}{\frac{t}{z}}} \]

   7. Applied egg-rr 72.3%

      \[\leadsto \color{blue}{\frac{y}{\frac{t}{z}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 60.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2.2 \cdot 10^{+141}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq -1.35 \cdot 10^{+100}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -3.9 \cdot 10^{+14}:\\ \;\;\;\;\frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{-85}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \end{array} \]

Alternative 11: 69.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{-175} \lor \neg \left(y \leq 1.6 \cdot 10^{+58}\right):\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -8.5e-175) (not (<= y 1.6e+58))) (* y (/ (- z x) t)) x))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -8.5e-175) || !(y <= 1.6e+58)) {
		tmp = y * ((z - x) / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-8.5d-175)) .or. (.not. (y <= 1.6d+58))) then
        tmp = y * ((z - x) / t)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -8.5e-175) || !(y <= 1.6e+58)) {
		tmp = y * ((z - x) / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -8.5e-175) or not (y <= 1.6e+58):
		tmp = y * ((z - x) / t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -8.5e-175) || !(y <= 1.6e+58))
		tmp = Float64(y * Float64(Float64(z - x) / t));
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -8.5e-175) || ~((y <= 1.6e+58)))
		tmp = y * ((z - x) / t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -8.5e-175], N[Not[LessEqual[y, 1.6e+58]], $MachinePrecision]], N[(y * N[(N[(z - x), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -8.5000000000000005e-175 or 1.60000000000000008e58 < y

   1. Initial program 84.0%

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

      1. +-commutative 84.0%

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

      2. associate-*r/ 96.3%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 96.3%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 96.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around inf 75.0%

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

   5. Taylor expanded in z around 0 75.0%

      \[\leadsto y \cdot \color{blue}{\left(\frac{z}{t} + -1 \cdot \frac{x}{t}\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 75.0%

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

      2. sub-neg 75.0%

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

      3. div-sub 75.6%

         \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   7. Simplified 75.6%

      \[\leadsto y \cdot \color{blue}{\frac{z - x}{t}} \]

   if -8.5000000000000005e-175 < y < 1.60000000000000008e58

   1. Initial program 98.1%

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

      1. +-commutative 98.1%

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

      2. associate-*r/ 88.5%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 88.5%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 88.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in y around 0 68.3%

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

4. Final simplification 72.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -8.5 \cdot 10^{-175} \lor \neg \left(y \leq 1.6 \cdot 10^{+58}\right):\\ \;\;\;\;y \cdot \frac{z - x}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 12: 86.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.75 \cdot 10^{-53}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{-44}:\\ \;\;\;\;x - x \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z}{\frac{t}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -3.75e-53)
   (+ x (* z (/ y t)))
   (if (<= z 4.2e-44) (- x (* x (/ y t))) (+ x (/ z (/ t y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -3.75e-53) {
		tmp = x + (z * (y / t));
	} else if (z <= 4.2e-44) {
		tmp = x - (x * (y / t));
	} else {
		tmp = x + (z / (t / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (z <= (-3.75d-53)) then
        tmp = x + (z * (y / t))
    else if (z <= 4.2d-44) then
        tmp = x - (x * (y / t))
    else
        tmp = x + (z / (t / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -3.75e-53) {
		tmp = x + (z * (y / t));
	} else if (z <= 4.2e-44) {
		tmp = x - (x * (y / t));
	} else {
		tmp = x + (z / (t / y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -3.75e-53:
		tmp = x + (z * (y / t))
	elif z <= 4.2e-44:
		tmp = x - (x * (y / t))
	else:
		tmp = x + (z / (t / y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -3.75e-53)
		tmp = Float64(x + Float64(z * Float64(y / t)));
	elseif (z <= 4.2e-44)
		tmp = Float64(x - Float64(x * Float64(y / t)));
	else
		tmp = Float64(x + Float64(z / Float64(t / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -3.75e-53)
		tmp = x + (z * (y / t));
	elseif (z <= 4.2e-44)
		tmp = x - (x * (y / t));
	else
		tmp = x + (z / (t / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -3.75e-53], N[(x + N[(z * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.2e-44], N[(x - N[(x * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -3.75e-53

   1. Initial program 91.8%

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

      1. associate-*l/ 96.7%

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

   3. Simplified 96.7%

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

   4. Taylor expanded in z around inf 90.6%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. associate-*l/ 91.0%

         \[\leadsto x + \color{blue}{\frac{y}{t} \cdot z} \]

      2. *-commutative 91.0%

         \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   6. Simplified 91.0%

      \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   if -3.75e-53 < z < 4.20000000000000003e-44

   1. Initial program 89.1%

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

      1. +-commutative 89.1%

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

      2. associate-*r/ 95.0%

         \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

      3. fma-def 95.0%

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   3. Simplified 95.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

   4. Taylor expanded in z around 0 79.4%

      \[\leadsto \color{blue}{-1 \cdot \frac{y \cdot x}{t} + x} \]
   5. Step-by-step derivation

      1. +-commutative 79.4%

         \[\leadsto \color{blue}{x + -1 \cdot \frac{y \cdot x}{t}} \]

      2. mul-1-neg 79.4%

         \[\leadsto x + \color{blue}{\left(-\frac{y \cdot x}{t}\right)} \]

      3. unsub-neg 79.4%

         \[\leadsto \color{blue}{x - \frac{y \cdot x}{t}} \]

      4. *-commutative 79.4%

         \[\leadsto x - \frac{\color{blue}{x \cdot y}}{t} \]

      5. associate-*r/ 89.2%

         \[\leadsto x - \color{blue}{x \cdot \frac{y}{t}} \]

   6. Simplified 89.2%

      \[\leadsto \color{blue}{x - x \cdot \frac{y}{t}} \]

   if 4.20000000000000003e-44 < z

   1. Initial program 87.0%

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

      1. associate-*l/ 98.2%

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

   3. Simplified 98.2%

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

   4. Taylor expanded in z around inf 84.8%

      \[\leadsto x + \color{blue}{\frac{y \cdot z}{t}} \]
   5. Step-by-step derivation

      1. associate-*l/ 94.4%

         \[\leadsto x + \color{blue}{\frac{y}{t} \cdot z} \]

      2. *-commutative 94.4%

         \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]

   6. Simplified 94.4%

      \[\leadsto x + \color{blue}{z \cdot \frac{y}{t}} \]
   7. Step-by-step derivation

      1. clear-num 94.5%

         \[\leadsto x + z \cdot \color{blue}{\frac{1}{\frac{t}{y}}} \]

      2. div-inv 94.5%

         \[\leadsto x + \color{blue}{\frac{z}{\frac{t}{y}}} \]

   8. Applied egg-rr 94.5%

      \[\leadsto x + \color{blue}{\frac{z}{\frac{t}{y}}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 91.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.75 \cdot 10^{-53}:\\ \;\;\;\;x + z \cdot \frac{y}{t}\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{-44}:\\ \;\;\;\;x - x \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{z}{\frac{t}{y}}\\ \end{array} \]

Alternative 13: 97.6% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (+ x (* (- z x) (/ y t))))

C

double code(double x, double y, double z, double t) {
	return x + ((z - x) * (y / t));
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + ((z - x) * (y / t))
end function

Java

public static double code(double x, double y, double z, double t) {
	return x + ((z - x) * (y / t));
}

Python

def code(x, y, z, t):
	return x + ((z - x) * (y / t))

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(z - x) * Float64(y / t)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((z - x) * (y / t));
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(z - x), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 89.5%

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

   1. associate-*l/ 96.7%

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

3. Simplified 96.7%

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

4. Final simplification 96.7%

   \[\leadsto x + \left(z - x\right) \cdot \frac{y}{t} \]

Alternative 14: 39.5% accurate, 9.0× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 x)

C

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

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x
end function

Java

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

Python

def code(x, y, z, t):
	return x

Julia

function code(x, y, z, t)
	return x
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := x

Derivation

1. Initial program 89.5%

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

   1. +-commutative 89.5%

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

   2. associate-*r/ 93.3%

      \[\leadsto \color{blue}{y \cdot \frac{z - x}{t}} + x \]

   3. fma-def 93.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

3. Simplified 93.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - x}{t}, x\right)} \]

4. Taylor expanded in y around 0 40.8%

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

5. Final simplification 40.8%

   \[\leadsto x \]

Developer target: 90.8% accurate, 0.6× speedup

Math

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

FPCore

(FPCore (x y z t)
 :precision binary64
 (- x (+ (* x (/ y t)) (* (- z) (/ y t)))))

C

double code(double x, double y, double z, double t) {
	return x - ((x * (y / t)) + (-z * (y / t)));
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x - ((x * (y / t)) + (-z * (y / t)))
end function

Java

public static double code(double x, double y, double z, double t) {
	return x - ((x * (y / t)) + (-z * (y / t)));
}

Python

def code(x, y, z, t):
	return x - ((x * (y / t)) + (-z * (y / t)))

Julia

function code(x, y, z, t)
	return Float64(x - Float64(Float64(x * Float64(y / t)) + Float64(Float64(-z) * Float64(y / t))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x - ((x * (y / t)) + (-z * (y / t)));
end

Wolfram

code[x_, y_, z_, t_] := N[(x - N[(N[(x * N[(y / t), $MachinePrecision]), $MachinePrecision] + N[((-z) * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023257 
(FPCore (x y z t)
  :name "Optimisation.CirclePacking:place from circle-packing-0.1.0.4, D"
  :precision binary64

  :herbie-target
  (- x (+ (* x (/ y t)) (* (- z) (/ y t))))

  (+ x (/ (* y (- z x)) t)))