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

Percentage Accurate: 92.4% → 97.8%

Time: 8.7s

Alternatives: 10

Speedup: 1.0×

• 25.4% 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: 92.4% 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, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 96.4%

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

3. Taylor expanded in z around 0 91.7%

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

   1. +-commutative 91.7%

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

   2. *-commutative 91.7%

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

   3. associate-*r/ 92.3%

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

   4. mul-1-neg 92.3%

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

   5. associate-/l* 91.8%

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

   6. distribute-lft-neg-in 91.8%

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

   7. distribute-rgt-in 98.1%

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

   8. sub-neg 98.1%

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

5. Simplified 98.1%

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

6. Final simplification 98.1%

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

Alternative 2: 52.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{+92}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -5.4 \cdot 10^{+74}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;x \leq -2.8 \cdot 10^{-68}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 7.9 \cdot 10^{-25}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \mathbf{elif}\;x \leq 8.1 \cdot 10^{+191}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -7.2e+92)
   x
   (if (<= x -5.4e+74)
     (* y (/ z t))
     (if (<= x -2.8e-68)
       x
       (if (<= x 7.9e-25)
         (* (/ y t) z)
         (if (<= x 8.1e+191) x (* x (/ y (- t)))))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -7.2e+92) {
		tmp = x;
	} else if (x <= -5.4e+74) {
		tmp = y * (z / t);
	} else if (x <= -2.8e-68) {
		tmp = x;
	} else if (x <= 7.9e-25) {
		tmp = (y / t) * z;
	} else if (x <= 8.1e+191) {
		tmp = x;
	} else {
		tmp = 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 (x <= (-7.2d+92)) then
        tmp = x
    else if (x <= (-5.4d+74)) then
        tmp = y * (z / t)
    else if (x <= (-2.8d-68)) then
        tmp = x
    else if (x <= 7.9d-25) then
        tmp = (y / t) * z
    else if (x <= 8.1d+191) then
        tmp = x
    else
        tmp = 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 (x <= -7.2e+92) {
		tmp = x;
	} else if (x <= -5.4e+74) {
		tmp = y * (z / t);
	} else if (x <= -2.8e-68) {
		tmp = x;
	} else if (x <= 7.9e-25) {
		tmp = (y / t) * z;
	} else if (x <= 8.1e+191) {
		tmp = x;
	} else {
		tmp = x * (y / -t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -7.2e+92:
		tmp = x
	elif x <= -5.4e+74:
		tmp = y * (z / t)
	elif x <= -2.8e-68:
		tmp = x
	elif x <= 7.9e-25:
		tmp = (y / t) * z
	elif x <= 8.1e+191:
		tmp = x
	else:
		tmp = x * (y / -t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -7.2e+92)
		tmp = x;
	elseif (x <= -5.4e+74)
		tmp = Float64(y * Float64(z / t));
	elseif (x <= -2.8e-68)
		tmp = x;
	elseif (x <= 7.9e-25)
		tmp = Float64(Float64(y / t) * z);
	elseif (x <= 8.1e+191)
		tmp = x;
	else
		tmp = Float64(x * Float64(y / Float64(-t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -7.2e+92)
		tmp = x;
	elseif (x <= -5.4e+74)
		tmp = y * (z / t);
	elseif (x <= -2.8e-68)
		tmp = x;
	elseif (x <= 7.9e-25)
		tmp = (y / t) * z;
	elseif (x <= 8.1e+191)
		tmp = x;
	else
		tmp = x * (y / -t);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -7.2e+92], x, If[LessEqual[x, -5.4e+74], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -2.8e-68], x, If[LessEqual[x, 7.9e-25], N[(N[(y / t), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[x, 8.1e+191], x, N[(x * N[(y / (-t)), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -7.2e92 or -5.3999999999999996e74 < x < -2.8000000000000001e-68 or 7.8999999999999997e-25 < x < 8.1000000000000002e191

   1. Initial program 96.7%

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

   3. Taylor expanded in y around 0 52.8%

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

   if -7.2e92 < x < -5.3999999999999996e74

   1. Initial program 100.0%

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

   3. Taylor expanded in y around -inf 100.0%

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

   4. Taylor expanded in z around inf 84.5%

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

      1. associate-/l* 84.5%

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

   6. Simplified 84.5%

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

   if -2.8000000000000001e-68 < x < 7.8999999999999997e-25

   1. Initial program 96.0%

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

   3. Taylor expanded in y around -inf 75.8%

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

   4. Taylor expanded in z around inf 67.2%

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

      1. add-log-exp 30.3%

         \[\leadsto \color{blue}{\log \left(e^{\frac{y \cdot z}{t}}\right)} \]

      2. *-un-lft-identity 30.3%

         \[\leadsto \log \color{blue}{\left(1 \cdot e^{\frac{y \cdot z}{t}}\right)} \]

      3. log-prod 30.3%

         \[\leadsto \color{blue}{\log 1 + \log \left(e^{\frac{y \cdot z}{t}}\right)} \]

      4. metadata-eval 30.3%

         \[\leadsto \color{blue}{0} + \log \left(e^{\frac{y \cdot z}{t}}\right) \]

      5. add-log-exp 67.2%

         \[\leadsto 0 + \color{blue}{\frac{y \cdot z}{t}} \]

      6. associate-/l* 65.7%

         \[\leadsto 0 + \color{blue}{y \cdot \frac{z}{t}} \]

   6. Applied egg-rr 65.7%

      \[\leadsto \color{blue}{0 + y \cdot \frac{z}{t}} \]
   7. Step-by-step derivation

      1. +-lft-identity 65.7%

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

      2. *-commutative 65.7%

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

      3. associate-*l/ 67.2%

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

      4. associate-*r/ 69.5%

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

   8. Simplified 69.5%

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

   if 8.1000000000000002e191 < x

   1. Initial program 95.7%

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

   3. Taylor expanded in y around -inf 67.5%

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

   4. Taylor expanded in z around 0 63.0%

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

      1. mul-1-neg 63.0%

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

      2. associate-/l* 63.0%

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

      3. distribute-rgt-neg-in 63.0%

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

      4. mul-1-neg 63.0%

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

      5. associate-*r/ 63.0%

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

      6. mul-1-neg 63.0%

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

   6. Simplified 63.0%

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

4. Final simplification 61.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{+92}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -5.4 \cdot 10^{+74}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;x \leq -2.8 \cdot 10^{-68}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 7.9 \cdot 10^{-25}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \mathbf{elif}\;x \leq 8.1 \cdot 10^{+191}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{y}{-t}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 52.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{+92}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -7.6 \cdot 10^{+74}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;x \leq -1.16 \cdot 10^{-68}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.25 \cdot 10^{-23}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \mathbf{elif}\;x \leq 1.18 \cdot 10^{+201}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot \left(-y\right)}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -1.55e+92)
   x
   (if (<= x -7.6e+74)
     (* y (/ z t))
     (if (<= x -1.16e-68)
       x
       (if (<= x 1.25e-23)
         (* (/ y t) z)
         (if (<= x 1.18e+201) x (/ (* x (- y)) t)))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -1.55e+92) {
		tmp = x;
	} else if (x <= -7.6e+74) {
		tmp = y * (z / t);
	} else if (x <= -1.16e-68) {
		tmp = x;
	} else if (x <= 1.25e-23) {
		tmp = (y / t) * z;
	} else if (x <= 1.18e+201) {
		tmp = x;
	} else {
		tmp = (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 (x <= (-1.55d+92)) then
        tmp = x
    else if (x <= (-7.6d+74)) then
        tmp = y * (z / t)
    else if (x <= (-1.16d-68)) then
        tmp = x
    else if (x <= 1.25d-23) then
        tmp = (y / t) * z
    else if (x <= 1.18d+201) then
        tmp = x
    else
        tmp = (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 (x <= -1.55e+92) {
		tmp = x;
	} else if (x <= -7.6e+74) {
		tmp = y * (z / t);
	} else if (x <= -1.16e-68) {
		tmp = x;
	} else if (x <= 1.25e-23) {
		tmp = (y / t) * z;
	} else if (x <= 1.18e+201) {
		tmp = x;
	} else {
		tmp = (x * -y) / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -1.55e+92:
		tmp = x
	elif x <= -7.6e+74:
		tmp = y * (z / t)
	elif x <= -1.16e-68:
		tmp = x
	elif x <= 1.25e-23:
		tmp = (y / t) * z
	elif x <= 1.18e+201:
		tmp = x
	else:
		tmp = (x * -y) / t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -1.55e+92)
		tmp = x;
	elseif (x <= -7.6e+74)
		tmp = Float64(y * Float64(z / t));
	elseif (x <= -1.16e-68)
		tmp = x;
	elseif (x <= 1.25e-23)
		tmp = Float64(Float64(y / t) * z);
	elseif (x <= 1.18e+201)
		tmp = x;
	else
		tmp = Float64(Float64(x * Float64(-y)) / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -1.55e+92)
		tmp = x;
	elseif (x <= -7.6e+74)
		tmp = y * (z / t);
	elseif (x <= -1.16e-68)
		tmp = x;
	elseif (x <= 1.25e-23)
		tmp = (y / t) * z;
	elseif (x <= 1.18e+201)
		tmp = x;
	else
		tmp = (x * -y) / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -1.55e+92], x, If[LessEqual[x, -7.6e+74], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, -1.16e-68], x, If[LessEqual[x, 1.25e-23], N[(N[(y / t), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[x, 1.18e+201], x, N[(N[(x * (-y)), $MachinePrecision] / t), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1.5500000000000001e92 or -7.5999999999999997e74 < x < -1.1599999999999999e-68 or 1.2500000000000001e-23 < x < 1.18e201

   1. Initial program 96.7%

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

   3. Taylor expanded in y around 0 52.8%

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

   if -1.5500000000000001e92 < x < -7.5999999999999997e74

   1. Initial program 100.0%

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

   3. Taylor expanded in y around -inf 100.0%

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

   4. Taylor expanded in z around inf 84.5%

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

      1. associate-/l* 84.5%

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

   6. Simplified 84.5%

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

   if -1.1599999999999999e-68 < x < 1.2500000000000001e-23

   1. Initial program 96.0%

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

   3. Taylor expanded in y around -inf 75.8%

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

   4. Taylor expanded in z around inf 67.2%

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

      1. add-log-exp 30.3%

         \[\leadsto \color{blue}{\log \left(e^{\frac{y \cdot z}{t}}\right)} \]

      2. *-un-lft-identity 30.3%

         \[\leadsto \log \color{blue}{\left(1 \cdot e^{\frac{y \cdot z}{t}}\right)} \]

      3. log-prod 30.3%

         \[\leadsto \color{blue}{\log 1 + \log \left(e^{\frac{y \cdot z}{t}}\right)} \]

      4. metadata-eval 30.3%

         \[\leadsto \color{blue}{0} + \log \left(e^{\frac{y \cdot z}{t}}\right) \]

      5. add-log-exp 67.2%

         \[\leadsto 0 + \color{blue}{\frac{y \cdot z}{t}} \]

      6. associate-/l* 65.7%

         \[\leadsto 0 + \color{blue}{y \cdot \frac{z}{t}} \]

   6. Applied egg-rr 65.7%

      \[\leadsto \color{blue}{0 + y \cdot \frac{z}{t}} \]
   7. Step-by-step derivation

      1. +-lft-identity 65.7%

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

      2. *-commutative 65.7%

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

      3. associate-*l/ 67.2%

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

      4. associate-*r/ 69.5%

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

   8. Simplified 69.5%

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

   if 1.18e201 < x

   1. Initial program 95.7%

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

   3. Taylor expanded in y around -inf 67.5%

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

   4. Taylor expanded in z around 0 63.0%

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

      1. mul-1-neg 63.0%

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

      2. distribute-lft-neg-out 63.0%

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

      3. *-commutative 63.0%

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

   6. Simplified 63.0%

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

4. Final simplification 61.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{+92}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -7.6 \cdot 10^{+74}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;x \leq -1.16 \cdot 10^{-68}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.25 \cdot 10^{-23}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \mathbf{elif}\;x \leq 1.18 \cdot 10^{+201}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot \left(-y\right)}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 75.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1.5 \cdot 10^{+138} \lor \neg \left(z \leq -6 \cdot 10^{+32} \lor \neg \left(z \leq -2.35 \cdot 10^{-36}\right) \land z \leq 7.4 \cdot 10^{+80}\right):\\ \;\;\;\;\frac{y}{t} \cdot \left(z - x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -1.5e+138)
         (not (or (<= z -6e+32) (and (not (<= z -2.35e-36)) (<= z 7.4e+80)))))
   (* (/ y t) (- z x))
   (* x (- 1.0 (/ y t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.5e+138) || !((z <= -6e+32) || (!(z <= -2.35e-36) && (z <= 7.4e+80)))) {
		tmp = (y / t) * (z - x);
	} else {
		tmp = x * (1.0 - (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 ((z <= (-1.5d+138)) .or. (.not. (z <= (-6d+32)) .or. (.not. (z <= (-2.35d-36))) .and. (z <= 7.4d+80))) then
        tmp = (y / t) * (z - x)
    else
        tmp = x * (1.0d0 - (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -1.5e+138) || !((z <= -6e+32) || (!(z <= -2.35e-36) && (z <= 7.4e+80)))) {
		tmp = (y / t) * (z - x);
	} else {
		tmp = x * (1.0 - (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -1.5e+138) or not ((z <= -6e+32) or (not (z <= -2.35e-36) and (z <= 7.4e+80))):
		tmp = (y / t) * (z - x)
	else:
		tmp = x * (1.0 - (y / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -1.5e+138) || !((z <= -6e+32) || (!(z <= -2.35e-36) && (z <= 7.4e+80))))
		tmp = Float64(Float64(y / t) * Float64(z - x));
	else
		tmp = Float64(x * Float64(1.0 - Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -1.5e+138) || ~(((z <= -6e+32) || (~((z <= -2.35e-36)) && (z <= 7.4e+80)))))
		tmp = (y / t) * (z - x);
	else
		tmp = x * (1.0 - (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -1.5e+138], N[Not[Or[LessEqual[z, -6e+32], And[N[Not[LessEqual[z, -2.35e-36]], $MachinePrecision], LessEqual[z, 7.4e+80]]]], $MachinePrecision]], N[(N[(y / t), $MachinePrecision] * N[(z - x), $MachinePrecision]), $MachinePrecision], N[(x * N[(1.0 - N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -1.50000000000000005e138 or -6e32 < z < -2.3500000000000001e-36 or 7.39999999999999992e80 < z

   1. Initial program 94.5%

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

   3. Taylor expanded in y around -inf 76.5%

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

   4. Taylor expanded in z around 0 68.0%

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

      1. +-commutative 86.0%

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

      2. *-commutative 86.0%

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

      3. associate-*r/ 89.8%

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

      4. mul-1-neg 89.8%

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

      5. associate-/l* 86.9%

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

      6. distribute-lft-neg-in 86.9%

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

      7. distribute-rgt-in 99.2%

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

      8. sub-neg 99.2%

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

   6. Simplified 80.3%

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

   if -1.50000000000000005e138 < z < -6e32 or -2.3500000000000001e-36 < z < 7.39999999999999992e80

   1. Initial program 97.7%

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

   3. Taylor expanded in x around inf 85.6%

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

      1. mul-1-neg 85.6%

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

      2. unsub-neg 85.6%

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

   5. Simplified 85.6%

      \[\leadsto \color{blue}{x \cdot \left(1 - \frac{y}{t}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 83.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.5 \cdot 10^{+138} \lor \neg \left(z \leq -6 \cdot 10^{+32} \lor \neg \left(z \leq -2.35 \cdot 10^{-36}\right) \land z \leq 7.4 \cdot 10^{+80}\right):\\ \;\;\;\;\frac{y}{t} \cdot \left(z - x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 84.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{if}\;x \leq -8.8 \cdot 10^{+95}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq -2.1 \cdot 10^{+74}:\\ \;\;\;\;\frac{y}{t} \cdot \left(z - x\right)\\ \mathbf{elif}\;x \leq -3.2 \cdot 10^{+39} \lor \neg \left(x \leq 7.4 \cdot 10^{+61}\right):\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* x (- 1.0 (/ y t)))))
   (if (<= x -8.8e+95)
     t_1
     (if (<= x -2.1e+74)
       (* (/ y t) (- z x))
       (if (or (<= x -3.2e+39) (not (<= x 7.4e+61)))
         t_1
         (+ x (* y (/ z t))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x * (1.0 - (y / t));
	double tmp;
	if (x <= -8.8e+95) {
		tmp = t_1;
	} else if (x <= -2.1e+74) {
		tmp = (y / t) * (z - x);
	} else if ((x <= -3.2e+39) || !(x <= 7.4e+61)) {
		tmp = t_1;
	} else {
		tmp = x + (y * (z / 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 = x * (1.0d0 - (y / t))
    if (x <= (-8.8d+95)) then
        tmp = t_1
    else if (x <= (-2.1d+74)) then
        tmp = (y / t) * (z - x)
    else if ((x <= (-3.2d+39)) .or. (.not. (x <= 7.4d+61))) then
        tmp = t_1
    else
        tmp = x + (y * (z / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x * (1.0 - (y / t));
	double tmp;
	if (x <= -8.8e+95) {
		tmp = t_1;
	} else if (x <= -2.1e+74) {
		tmp = (y / t) * (z - x);
	} else if ((x <= -3.2e+39) || !(x <= 7.4e+61)) {
		tmp = t_1;
	} else {
		tmp = x + (y * (z / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x * (1.0 - (y / t))
	tmp = 0
	if x <= -8.8e+95:
		tmp = t_1
	elif x <= -2.1e+74:
		tmp = (y / t) * (z - x)
	elif (x <= -3.2e+39) or not (x <= 7.4e+61):
		tmp = t_1
	else:
		tmp = x + (y * (z / t))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x * Float64(1.0 - Float64(y / t)))
	tmp = 0.0
	if (x <= -8.8e+95)
		tmp = t_1;
	elseif (x <= -2.1e+74)
		tmp = Float64(Float64(y / t) * Float64(z - x));
	elseif ((x <= -3.2e+39) || !(x <= 7.4e+61))
		tmp = t_1;
	else
		tmp = Float64(x + Float64(y * Float64(z / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x * (1.0 - (y / t));
	tmp = 0.0;
	if (x <= -8.8e+95)
		tmp = t_1;
	elseif (x <= -2.1e+74)
		tmp = (y / t) * (z - x);
	elseif ((x <= -3.2e+39) || ~((x <= 7.4e+61)))
		tmp = t_1;
	else
		tmp = x + (y * (z / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x * N[(1.0 - N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -8.8e+95], t$95$1, If[LessEqual[x, -2.1e+74], N[(N[(y / t), $MachinePrecision] * N[(z - x), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[x, -3.2e+39], N[Not[LessEqual[x, 7.4e+61]], $MachinePrecision]], t$95$1, N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -8.7999999999999996e95 or -2.0999999999999999e74 < x < -3.19999999999999993e39 or 7.40000000000000005e61 < x

   1. Initial program 95.8%

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

   3. Taylor expanded in x around inf 93.6%

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

      1. mul-1-neg 93.6%

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

      2. unsub-neg 93.6%

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

   5. Simplified 93.6%

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

   if -8.7999999999999996e95 < x < -2.0999999999999999e74

   1. Initial program 100.0%

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

   3. Taylor expanded in y around -inf 100.0%

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

   4. Taylor expanded in z around 0 100.0%

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

      1. +-commutative 100.0%

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

      2. *-commutative 100.0%

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

      3. associate-*r/ 100.0%

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

      4. mul-1-neg 100.0%

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

      5. associate-/l* 100.0%

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

      6. distribute-lft-neg-in 100.0%

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

      7. distribute-rgt-in 100.0%

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

      8. sub-neg 100.0%

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

   6. Simplified 100.0%

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

   if -3.19999999999999993e39 < x < 7.40000000000000005e61

   1. Initial program 96.6%

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

   3. Taylor expanded in z around inf 86.1%

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

      1. associate-/l* 55.4%

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

   5. Simplified 85.0%

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

4. Final simplification 88.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -8.8 \cdot 10^{+95}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{elif}\;x \leq -2.1 \cdot 10^{+74}:\\ \;\;\;\;\frac{y}{t} \cdot \left(z - x\right)\\ \mathbf{elif}\;x \leq -3.2 \cdot 10^{+39} \lor \neg \left(x \leq 7.4 \cdot 10^{+61}\right):\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 72.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{t} \cdot z\\ t_2 := x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{if}\;z \leq -1.56 \cdot 10^{+156}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{+26}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;z \leq -3.6 \cdot 10^{-36}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{+82}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (/ y t) z)) (t_2 (* x (- 1.0 (/ y t)))))
   (if (<= z -1.56e+156)
     t_1
     (if (<= z -1.15e+26)
       t_2
       (if (<= z -3.6e-36) (* y (/ z t)) (if (<= z 3.2e+82) t_2 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y / t) * z;
	double t_2 = x * (1.0 - (y / t));
	double tmp;
	if (z <= -1.56e+156) {
		tmp = t_1;
	} else if (z <= -1.15e+26) {
		tmp = t_2;
	} else if (z <= -3.6e-36) {
		tmp = y * (z / t);
	} else if (z <= 3.2e+82) {
		tmp = t_2;
	} 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 = (y / t) * z
    t_2 = x * (1.0d0 - (y / t))
    if (z <= (-1.56d+156)) then
        tmp = t_1
    else if (z <= (-1.15d+26)) then
        tmp = t_2
    else if (z <= (-3.6d-36)) then
        tmp = y * (z / t)
    else if (z <= 3.2d+82) then
        tmp = t_2
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (y / t) * z;
	double t_2 = x * (1.0 - (y / t));
	double tmp;
	if (z <= -1.56e+156) {
		tmp = t_1;
	} else if (z <= -1.15e+26) {
		tmp = t_2;
	} else if (z <= -3.6e-36) {
		tmp = y * (z / t);
	} else if (z <= 3.2e+82) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (y / t) * z
	t_2 = x * (1.0 - (y / t))
	tmp = 0
	if z <= -1.56e+156:
		tmp = t_1
	elif z <= -1.15e+26:
		tmp = t_2
	elif z <= -3.6e-36:
		tmp = y * (z / t)
	elif z <= 3.2e+82:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y / t) * z)
	t_2 = Float64(x * Float64(1.0 - Float64(y / t)))
	tmp = 0.0
	if (z <= -1.56e+156)
		tmp = t_1;
	elseif (z <= -1.15e+26)
		tmp = t_2;
	elseif (z <= -3.6e-36)
		tmp = Float64(y * Float64(z / t));
	elseif (z <= 3.2e+82)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (y / t) * z;
	t_2 = x * (1.0 - (y / t));
	tmp = 0.0;
	if (z <= -1.56e+156)
		tmp = t_1;
	elseif (z <= -1.15e+26)
		tmp = t_2;
	elseif (z <= -3.6e-36)
		tmp = y * (z / t);
	elseif (z <= 3.2e+82)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y / t), $MachinePrecision] * z), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(1.0 - N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.56e+156], t$95$1, If[LessEqual[z, -1.15e+26], t$95$2, If[LessEqual[z, -3.6e-36], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 3.2e+82], t$95$2, t$95$1]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.55999999999999992e156 or 3.19999999999999975e82 < z

   1. Initial program 94.2%

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

   3. Taylor expanded in y around -inf 75.9%

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

   4. Taylor expanded in z around inf 74.5%

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

      1. add-log-exp 43.2%

         \[\leadsto \color{blue}{\log \left(e^{\frac{y \cdot z}{t}}\right)} \]

      2. *-un-lft-identity 43.2%

         \[\leadsto \log \color{blue}{\left(1 \cdot e^{\frac{y \cdot z}{t}}\right)} \]

      3. log-prod 43.2%

         \[\leadsto \color{blue}{\log 1 + \log \left(e^{\frac{y \cdot z}{t}}\right)} \]

      4. metadata-eval 43.2%

         \[\leadsto \color{blue}{0} + \log \left(e^{\frac{y \cdot z}{t}}\right) \]

      5. add-log-exp 74.5%

         \[\leadsto 0 + \color{blue}{\frac{y \cdot z}{t}} \]

      6. associate-/l* 71.0%

         \[\leadsto 0 + \color{blue}{y \cdot \frac{z}{t}} \]

   6. Applied egg-rr 71.0%

      \[\leadsto \color{blue}{0 + y \cdot \frac{z}{t}} \]
   7. Step-by-step derivation

      1. +-lft-identity 71.0%

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

      2. *-commutative 71.0%

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

      3. associate-*l/ 74.5%

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

      4. associate-*r/ 78.2%

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

   8. Simplified 78.2%

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

   if -1.55999999999999992e156 < z < -1.15e26 or -3.60000000000000032e-36 < z < 3.19999999999999975e82

   1. Initial program 97.2%

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

   3. Taylor expanded in x around inf 83.9%

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

      1. mul-1-neg 83.9%

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

      2. unsub-neg 83.9%

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

   5. Simplified 83.9%

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

   if -1.15e26 < z < -3.60000000000000032e-36

   1. Initial program 99.7%

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

   3. Taylor expanded in y around -inf 86.6%

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

   4. Taylor expanded in z around inf 71.6%

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

      1. associate-/l* 71.7%

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

   6. Simplified 71.7%

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

4. Final simplification 81.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.56 \cdot 10^{+156}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \mathbf{elif}\;z \leq -1.15 \cdot 10^{+26}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{elif}\;z \leq -3.6 \cdot 10^{-36}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{+82}:\\ \;\;\;\;x \cdot \left(1 - \frac{y}{t}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 51.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.2 \cdot 10^{+91}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -7.6 \cdot 10^{+74} \lor \neg \left(x \leq -2.75 \cdot 10^{-71}\right) \land x \leq 3 \cdot 10^{-25}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -6.2e+91)
   x
   (if (or (<= x -7.6e+74) (and (not (<= x -2.75e-71)) (<= x 3e-25)))
     (* y (/ z t))
     x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -6.2e+91) {
		tmp = x;
	} else if ((x <= -7.6e+74) || (!(x <= -2.75e-71) && (x <= 3e-25))) {
		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 (x <= (-6.2d+91)) then
        tmp = x
    else if ((x <= (-7.6d+74)) .or. (.not. (x <= (-2.75d-71))) .and. (x <= 3d-25)) 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 (x <= -6.2e+91) {
		tmp = x;
	} else if ((x <= -7.6e+74) || (!(x <= -2.75e-71) && (x <= 3e-25))) {
		tmp = y * (z / t);
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -6.2e+91:
		tmp = x
	elif (x <= -7.6e+74) or (not (x <= -2.75e-71) and (x <= 3e-25)):
		tmp = y * (z / t)
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -6.2e+91)
		tmp = x;
	elseif ((x <= -7.6e+74) || (!(x <= -2.75e-71) && (x <= 3e-25)))
		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 (x <= -6.2e+91)
		tmp = x;
	elseif ((x <= -7.6e+74) || (~((x <= -2.75e-71)) && (x <= 3e-25)))
		tmp = y * (z / t);
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -6.2e+91], x, If[Or[LessEqual[x, -7.6e+74], And[N[Not[LessEqual[x, -2.75e-71]], $MachinePrecision], LessEqual[x, 3e-25]]], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], x]]

Derivation

1. Split input into 2 regimes

2. if x < -6.19999999999999995e91 or -7.5999999999999997e74 < x < -2.7499999999999999e-71 or 2.9999999999999998e-25 < x

   1. Initial program 96.5%

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

   3. Taylor expanded in y around 0 49.3%

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

   if -6.19999999999999995e91 < x < -7.5999999999999997e74 or -2.7499999999999999e-71 < x < 2.9999999999999998e-25

   1. Initial program 96.2%

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

   3. Taylor expanded in y around -inf 77.0%

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

   4. Taylor expanded in z around inf 68.1%

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

      1. associate-/l* 66.7%

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

   6. Simplified 66.7%

      \[\leadsto \color{blue}{y \cdot \frac{z}{t}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 57.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -6.2 \cdot 10^{+91}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -7.6 \cdot 10^{+74} \lor \neg \left(x \leq -2.75 \cdot 10^{-71}\right) \land x \leq 3 \cdot 10^{-25}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 51.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{y}{t} \cdot z\\ \mathbf{if}\;z \leq -1.3 \cdot 10^{+138}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq -2.55 \cdot 10^{+33}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.75 \cdot 10^{-66}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-41}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (/ y t) z)))
   (if (<= z -1.3e+138)
     t_1
     (if (<= z -2.55e+33)
       x
       (if (<= z -1.75e-66) (* y (/ z t)) (if (<= z 4.5e-41) x t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y / t) * z;
	double tmp;
	if (z <= -1.3e+138) {
		tmp = t_1;
	} else if (z <= -2.55e+33) {
		tmp = x;
	} else if (z <= -1.75e-66) {
		tmp = y * (z / t);
	} else if (z <= 4.5e-41) {
		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 / t) * z
    if (z <= (-1.3d+138)) then
        tmp = t_1
    else if (z <= (-2.55d+33)) then
        tmp = x
    else if (z <= (-1.75d-66)) then
        tmp = y * (z / t)
    else if (z <= 4.5d-41) 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 / t) * z;
	double tmp;
	if (z <= -1.3e+138) {
		tmp = t_1;
	} else if (z <= -2.55e+33) {
		tmp = x;
	} else if (z <= -1.75e-66) {
		tmp = y * (z / t);
	} else if (z <= 4.5e-41) {
		tmp = x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (y / t) * z
	tmp = 0
	if z <= -1.3e+138:
		tmp = t_1
	elif z <= -2.55e+33:
		tmp = x
	elif z <= -1.75e-66:
		tmp = y * (z / t)
	elif z <= 4.5e-41:
		tmp = x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y / t) * z)
	tmp = 0.0
	if (z <= -1.3e+138)
		tmp = t_1;
	elseif (z <= -2.55e+33)
		tmp = x;
	elseif (z <= -1.75e-66)
		tmp = Float64(y * Float64(z / t));
	elseif (z <= 4.5e-41)
		tmp = x;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (y / t) * z;
	tmp = 0.0;
	if (z <= -1.3e+138)
		tmp = t_1;
	elseif (z <= -2.55e+33)
		tmp = x;
	elseif (z <= -1.75e-66)
		tmp = y * (z / t);
	elseif (z <= 4.5e-41)
		tmp = x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y / t), $MachinePrecision] * z), $MachinePrecision]}, If[LessEqual[z, -1.3e+138], t$95$1, If[LessEqual[z, -2.55e+33], x, If[LessEqual[z, -1.75e-66], N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 4.5e-41], x, t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.3e138 or 4.5e-41 < z

   1. Initial program 94.8%

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

   3. Taylor expanded in y around -inf 73.9%

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

   4. Taylor expanded in z around inf 66.7%

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

      1. add-log-exp 38.6%

         \[\leadsto \color{blue}{\log \left(e^{\frac{y \cdot z}{t}}\right)} \]

      2. *-un-lft-identity 38.6%

         \[\leadsto \log \color{blue}{\left(1 \cdot e^{\frac{y \cdot z}{t}}\right)} \]

      3. log-prod 38.6%

         \[\leadsto \color{blue}{\log 1 + \log \left(e^{\frac{y \cdot z}{t}}\right)} \]

      4. metadata-eval 38.6%

         \[\leadsto \color{blue}{0} + \log \left(e^{\frac{y \cdot z}{t}}\right) \]

      5. add-log-exp 66.7%

         \[\leadsto 0 + \color{blue}{\frac{y \cdot z}{t}} \]

      6. associate-/l* 63.2%

         \[\leadsto 0 + \color{blue}{y \cdot \frac{z}{t}} \]

   6. Applied egg-rr 63.2%

      \[\leadsto \color{blue}{0 + y \cdot \frac{z}{t}} \]
   7. Step-by-step derivation

      1. +-lft-identity 63.2%

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

      2. *-commutative 63.2%

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

      3. associate-*l/ 66.7%

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

      4. associate-*r/ 69.5%

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

   8. Simplified 69.5%

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

   if -1.3e138 < z < -2.5499999999999999e33 or -1.75e-66 < z < 4.5e-41

   1. Initial program 97.2%

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

   3. Taylor expanded in y around 0 53.0%

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

   if -2.5499999999999999e33 < z < -1.75e-66

   1. Initial program 99.8%

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

   3. Taylor expanded in y around -inf 87.0%

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

   4. Taylor expanded in z around inf 60.6%

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

      1. associate-/l* 60.6%

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

   6. Simplified 60.6%

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

4. Final simplification 60.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1.3 \cdot 10^{+138}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \mathbf{elif}\;z \leq -2.55 \cdot 10^{+33}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -1.75 \cdot 10^{-66}:\\ \;\;\;\;y \cdot \frac{z}{t}\\ \mathbf{elif}\;z \leq 4.5 \cdot 10^{-41}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 84.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.8 \cdot 10^{-78}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{+16}:\\ \;\;\;\;\frac{y \cdot \left(z - x\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= t -2.8e-78)
   (+ x (/ y (/ t z)))
   (if (<= t 1.65e+16) (/ (* y (- z x)) t) (+ x (* y (/ z t))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -2.8e-78) {
		tmp = x + (y / (t / z));
	} else if (t <= 1.65e+16) {
		tmp = (y * (z - x)) / t;
	} else {
		tmp = x + (y * (z / 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 <= (-2.8d-78)) then
        tmp = x + (y / (t / z))
    else if (t <= 1.65d+16) then
        tmp = (y * (z - x)) / t
    else
        tmp = x + (y * (z / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -2.8e-78) {
		tmp = x + (y / (t / z));
	} else if (t <= 1.65e+16) {
		tmp = (y * (z - x)) / t;
	} else {
		tmp = x + (y * (z / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if t <= -2.8e-78:
		tmp = x + (y / (t / z))
	elif t <= 1.65e+16:
		tmp = (y * (z - x)) / t
	else:
		tmp = x + (y * (z / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (t <= -2.8e-78)
		tmp = Float64(x + Float64(y / Float64(t / z)));
	elseif (t <= 1.65e+16)
		tmp = Float64(Float64(y * Float64(z - x)) / t);
	else
		tmp = Float64(x + Float64(y * Float64(z / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (t <= -2.8e-78)
		tmp = x + (y / (t / z));
	elseif (t <= 1.65e+16)
		tmp = (y * (z - x)) / t;
	else
		tmp = x + (y * (z / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[t, -2.8e-78], N[(x + N[(y / N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.65e+16], N[(N[(y * N[(z - x), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision], N[(x + N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -2.80000000000000024e-78

   1. Initial program 93.8%

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

   3. Taylor expanded in z around inf 84.0%

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

      1. associate-/l* 34.7%

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

   5. Simplified 87.2%

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

      1. clear-num 87.2%

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

      2. un-div-inv 87.3%

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

   7. Applied egg-rr 87.3%

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

   if -2.80000000000000024e-78 < t < 1.65e16

   1. Initial program 99.9%

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

   3. Taylor expanded in y around -inf 84.6%

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

   if 1.65e16 < t

   1. Initial program 90.0%

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

   3. Taylor expanded in z around inf 87.9%

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

      1. associate-/l* 29.7%

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

   5. Simplified 91.5%

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

4. Final simplification 86.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;t \leq -2.8 \cdot 10^{-78}:\\ \;\;\;\;x + \frac{y}{\frac{t}{z}}\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{+16}:\\ \;\;\;\;\frac{y \cdot \left(z - x\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot \frac{z}{t}\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 38.1% 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 96.4%

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

3. Taylor expanded in y around 0 36.7%

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

4. Final simplification 36.7%

   \[\leadsto x \]
5. Add Preprocessing

Developer target: 91.2% 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 2024066 
(FPCore (x y z t)
  :name "Optimisation.CirclePacking:place from circle-packing-0.1.0.4, D"
  :precision binary64

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

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