Graphics.Rasterific.Svg.PathConverter:arcToSegments from rasterific-svg-0.2.3.1

Percentage Accurate: 66.3% → 96.5%

Time: 9.3s

Alternatives: 8

Speedup: 1.0×

• 48.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 66.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 96.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot \frac{x}{y}}{y} + \frac{z}{t} \cdot \frac{z}{t} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 67.4%

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

   1. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{x \cdot x}{y \cdot y}\right), \color{blue}{\left(\frac{z \cdot z}{t \cdot t}\right)}\right) \]

   2. associate-/r* N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{\frac{x \cdot x}{y}}{y}\right), \left(\frac{\color{blue}{z \cdot z}}{t \cdot t}\right)\right) \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(\frac{x \cdot x}{y}\right), y\right), \left(\frac{\color{blue}{z \cdot z}}{t \cdot t}\right)\right) \]

   4. associate-/l* N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(x \cdot \frac{x}{y}\right), y\right), \left(\frac{\color{blue}{z} \cdot z}{t \cdot t}\right)\right) \]

   5. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \left(\frac{x}{y}\right)\right), y\right), \left(\frac{\color{blue}{z} \cdot z}{t \cdot t}\right)\right) \]

   6. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \left(\frac{z \cdot z}{t \cdot t}\right)\right) \]

   7. associate-/l* N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \left(z \cdot \color{blue}{\frac{z}{t \cdot t}}\right)\right) \]

   8. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \color{blue}{\left(\frac{z}{t \cdot t}\right)}\right)\right) \]

   9. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \color{blue}{\left(t \cdot t\right)}\right)\right)\right) \]

   10. *-lowering-*.f64 88.7%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, \color{blue}{t}\right)\right)\right)\right) \]

3. Simplified 88.7%

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

   1. associate-*r/ N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \left(\frac{z \cdot z}{\color{blue}{t \cdot t}}\right)\right) \]

   2. times-frac N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \left(\frac{z}{t} \cdot \color{blue}{\frac{z}{t}}\right)\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(\left(\frac{z}{t}\right), \color{blue}{\left(\frac{z}{t}\right)}\right)\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(\mathsf{/.f64}\left(z, t\right), \left(\frac{\color{blue}{z}}{t}\right)\right)\right) \]

   5. /-lowering-/.f64 97.2%

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(\mathsf{/.f64}\left(z, t\right), \mathsf{/.f64}\left(z, \color{blue}{t}\right)\right)\right) \]

6. Applied egg-rr 97.2%

   \[\leadsto \frac{x \cdot \frac{x}{y}}{y} + \color{blue}{\frac{z}{t} \cdot \frac{z}{t}} \]
7. Add Preprocessing

Alternative 2: 94.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{z \cdot z}{t \cdot t} \leq 0:\\ \;\;\;\;\frac{\frac{x}{y}}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{\frac{z}{t}}} + \frac{x}{y \cdot \frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ (* z z) (* t t)) 0.0)
   (/ (/ x y) (/ y x))
   (+ (/ z (/ t (/ z t))) (/ x (* y (/ y x))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * z) / (t * t)) <= 0.0) {
		tmp = (x / y) / (y / x);
	} else {
		tmp = (z / (t / (z / t))) + (x / (y * (y / 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 * z) / (t * t)) <= 0.0d0) then
        tmp = (x / y) / (y / x)
    else
        tmp = (z / (t / (z / t))) + (x / (y * (y / x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * z) / (t * t)) <= 0.0) {
		tmp = (x / y) / (y / x);
	} else {
		tmp = (z / (t / (z / t))) + (x / (y * (y / x)));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((z * z) / (t * t)) <= 0.0:
		tmp = (x / y) / (y / x)
	else:
		tmp = (z / (t / (z / t))) + (x / (y * (y / x)))
	return tmp

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(N[(z * z), $MachinePrecision] / N[(t * t), $MachinePrecision]), $MachinePrecision], 0.0], N[(N[(x / y), $MachinePrecision] / N[(y / x), $MachinePrecision]), $MachinePrecision], N[(N[(z / N[(t / N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x / N[(y * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 z z) (*.f64 t t)) < 0.0

   1. Initial program 64.0%

      \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{{x}^{2}}{{y}^{2}}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto \frac{x \cdot x}{{\color{blue}{y}}^{2}} \]

      2. associate-/l* N/A

         \[\leadsto x \cdot \color{blue}{\frac{x}{{y}^{2}}} \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(\frac{x}{{y}^{2}}\right)}\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{x}{y \cdot \color{blue}{y}}\right)\right) \]

      5. associate-/r* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{\frac{x}{y}}{\color{blue}{y}}\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{y}\right)\right) \]

      7. /-lowering-/.f64 81.8%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), y\right)\right) \]

   5. Simplified 81.8%

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

      1. associate-*r/ N/A

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

      2. associate-*l/ N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{\left(\frac{y}{x}\right)}\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), \left(\frac{\color{blue}{y}}{x}\right)\right) \]

      7. /-lowering-/.f64 91.0%

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), \mathsf{/.f64}\left(y, \color{blue}{x}\right)\right) \]

   7. Applied egg-rr 91.0%

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

   if 0.0 < (/.f64 (*.f64 z z) (*.f64 t t))

   1. Initial program 69.3%

      \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. +-commutative N/A

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

      3. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(z \cdot \frac{z}{t \cdot t}\right), \color{blue}{\left(\frac{x \cdot x}{y \cdot y}\right)}\right) \]

      4. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\left(z \cdot \frac{1}{\frac{t \cdot t}{z}}\right), \left(\frac{x \cdot \color{blue}{x}}{y \cdot y}\right)\right) \]

      5. un-div-inv N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\frac{z}{\frac{t \cdot t}{z}}\right), \left(\frac{\color{blue}{x \cdot x}}{y \cdot y}\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \left(\frac{t \cdot t}{z}\right)\right), \left(\frac{\color{blue}{x \cdot x}}{y \cdot y}\right)\right) \]

      7. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \left(\frac{1}{\frac{z}{t \cdot t}}\right)\right), \left(\frac{x \cdot \color{blue}{x}}{y \cdot y}\right)\right) \]

      8. associate-/r* N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \left(\frac{1}{\frac{\frac{z}{t}}{t}}\right)\right), \left(\frac{x \cdot x}{y \cdot y}\right)\right) \]

      9. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \left(\frac{t}{\frac{z}{t}}\right)\right), \left(\frac{x \cdot \color{blue}{x}}{y \cdot y}\right)\right) \]

      10. /-lowering-/.f64 N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \left(\frac{z}{t}\right)\right)\right), \left(\frac{x \cdot \color{blue}{x}}{y \cdot y}\right)\right) \]

      11. /-lowering-/.f64 N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{x \cdot x}{y \cdot y}\right)\right) \]

      12. times-frac N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{x}{y} \cdot \color{blue}{\frac{x}{y}}\right)\right) \]

      13. clear-num N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{1}{\frac{y}{x}} \cdot \frac{\color{blue}{x}}{y}\right)\right) \]

      14. frac-times N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{1 \cdot x}{\color{blue}{\frac{y}{x} \cdot y}}\right)\right) \]

      15. *-lft-identity N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{x}{\color{blue}{\frac{y}{x}} \cdot y}\right)\right) \]

      16. /-lowering-/.f64 N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \mathsf{/.f64}\left(x, \color{blue}{\left(\frac{y}{x} \cdot y\right)}\right)\right) \]

      17. *-lowering-*.f64 N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \mathsf{/.f64}\left(x, \mathsf{*.f64}\left(\left(\frac{y}{x}\right), \color{blue}{y}\right)\right)\right) \]

      18. /-lowering-/.f64 96.7%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \mathsf{/.f64}\left(x, \mathsf{*.f64}\left(\mathsf{/.f64}\left(y, x\right), y\right)\right)\right) \]

   4. Applied egg-rr 96.7%

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

4. Final simplification 94.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z \cdot z}{t \cdot t} \leq 0:\\ \;\;\;\;\frac{\frac{x}{y}}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{\frac{z}{t}}} + \frac{x}{y \cdot \frac{y}{x}}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 87.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{z \cdot z}{t \cdot t} \leq 0:\\ \;\;\;\;\frac{\frac{x}{y}}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;z \cdot \frac{z}{t \cdot t} + \frac{x}{\frac{y}{\frac{x}{y}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ (* z z) (* t t)) 0.0)
   (/ (/ x y) (/ y x))
   (+ (* z (/ z (* t t))) (/ x (/ y (/ x y))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * z) / (t * t)) <= 0.0) {
		tmp = (x / y) / (y / x);
	} else {
		tmp = (z * (z / (t * t))) + (x / (y / (x / 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 * z) / (t * t)) <= 0.0d0) then
        tmp = (x / y) / (y / x)
    else
        tmp = (z * (z / (t * t))) + (x / (y / (x / y)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * z) / (t * t)) <= 0.0) {
		tmp = (x / y) / (y / x);
	} else {
		tmp = (z * (z / (t * t))) + (x / (y / (x / y)));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((z * z) / (t * t)) <= 0.0:
		tmp = (x / y) / (y / x)
	else:
		tmp = (z * (z / (t * t))) + (x / (y / (x / y)))
	return tmp

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(N[(z * z), $MachinePrecision] / N[(t * t), $MachinePrecision]), $MachinePrecision], 0.0], N[(N[(x / y), $MachinePrecision] / N[(y / x), $MachinePrecision]), $MachinePrecision], N[(N[(z * N[(z / N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x / N[(y / N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 z z) (*.f64 t t)) < 0.0

   1. Initial program 64.0%

      \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{{x}^{2}}{{y}^{2}}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto \frac{x \cdot x}{{\color{blue}{y}}^{2}} \]

      2. associate-/l* N/A

         \[\leadsto x \cdot \color{blue}{\frac{x}{{y}^{2}}} \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(\frac{x}{{y}^{2}}\right)}\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{x}{y \cdot \color{blue}{y}}\right)\right) \]

      5. associate-/r* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{\frac{x}{y}}{\color{blue}{y}}\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{y}\right)\right) \]

      7. /-lowering-/.f64 81.8%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), y\right)\right) \]

   5. Simplified 81.8%

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

      1. associate-*r/ N/A

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

      2. associate-*l/ N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{\left(\frac{y}{x}\right)}\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), \left(\frac{\color{blue}{y}}{x}\right)\right) \]

      7. /-lowering-/.f64 91.0%

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), \mathsf{/.f64}\left(y, \color{blue}{x}\right)\right) \]

   7. Applied egg-rr 91.0%

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

   if 0.0 < (/.f64 (*.f64 z z) (*.f64 t t))

   1. Initial program 69.3%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\frac{x \cdot x}{y \cdot y}\right), \color{blue}{\left(\frac{z \cdot z}{t \cdot t}\right)}\right) \]

      2. associate-/r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\frac{\frac{x \cdot x}{y}}{y}\right), \left(\frac{\color{blue}{z \cdot z}}{t \cdot t}\right)\right) \]

      3. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(\frac{x \cdot x}{y}\right), y\right), \left(\frac{\color{blue}{z \cdot z}}{t \cdot t}\right)\right) \]

      4. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(x \cdot \frac{x}{y}\right), y\right), \left(\frac{\color{blue}{z} \cdot z}{t \cdot t}\right)\right) \]

      5. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \left(\frac{x}{y}\right)\right), y\right), \left(\frac{\color{blue}{z} \cdot z}{t \cdot t}\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \left(\frac{z \cdot z}{t \cdot t}\right)\right) \]

      7. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \left(z \cdot \color{blue}{\frac{z}{t \cdot t}}\right)\right) \]

      8. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \color{blue}{\left(\frac{z}{t \cdot t}\right)}\right)\right) \]

      9. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \color{blue}{\left(t \cdot t\right)}\right)\right)\right) \]

      10. *-lowering-*.f64 89.0%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, \color{blue}{t}\right)\right)\right)\right) \]

   3. Simplified 89.0%

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

      1. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \frac{\frac{x}{y}}{y}\right), \mathsf{*.f64}\left(\color{blue}{z}, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      2. associate-/r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \frac{x}{y \cdot y}\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      3. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\left(x \cdot \frac{1}{\frac{y \cdot y}{x}}\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      4. un-div-inv N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\frac{x}{\frac{y \cdot y}{x}}\right), \mathsf{*.f64}\left(\color{blue}{z}, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      5. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(x, \left(\frac{y \cdot y}{x}\right)\right), \mathsf{*.f64}\left(\color{blue}{z}, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      6. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(x, \left(\frac{1}{\frac{x}{y \cdot y}}\right)\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      7. associate-/r* N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(x, \left(\frac{1}{\frac{\frac{x}{y}}{y}}\right)\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      8. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(x, \left(\frac{y}{\frac{x}{y}}\right)\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      9. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(x, \mathsf{/.f64}\left(y, \left(\frac{x}{y}\right)\right)\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

      10. /-lowering-/.f64 88.3%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(x, \mathsf{/.f64}\left(y, \mathsf{/.f64}\left(x, y\right)\right)\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, t\right)\right)\right)\right) \]

   6. Applied egg-rr 88.3%

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

4. Final simplification 89.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{z \cdot z}{t \cdot t} \leq 0:\\ \;\;\;\;\frac{\frac{x}{y}}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;z \cdot \frac{z}{t \cdot t} + \frac{x}{\frac{y}{\frac{x}{y}}}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 90.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 6.9 \cdot 10^{-218}:\\ \;\;\;\;\frac{x \cdot \frac{x}{y}}{y} + z \cdot \frac{z}{t \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{\frac{z}{t}}} + \frac{x}{y \cdot \frac{y}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y 6.9e-218)
   (+ (/ (* x (/ x y)) y) (* z (/ z (* t t))))
   (+ (/ z (/ t (/ z t))) (/ x (* y (/ y x))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= 6.9e-218) {
		tmp = ((x * (x / y)) / y) + (z * (z / (t * t)));
	} else {
		tmp = (z / (t / (z / t))) + (x / (y * (y / 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 <= 6.9d-218) then
        tmp = ((x * (x / y)) / y) + (z * (z / (t * t)))
    else
        tmp = (z / (t / (z / t))) + (x / (y * (y / x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= 6.9e-218) {
		tmp = ((x * (x / y)) / y) + (z * (z / (t * t)));
	} else {
		tmp = (z / (t / (z / t))) + (x / (y * (y / x)));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= 6.9e-218:
		tmp = ((x * (x / y)) / y) + (z * (z / (t * t)))
	else:
		tmp = (z / (t / (z / t))) + (x / (y * (y / x)))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= 6.9e-218)
		tmp = Float64(Float64(Float64(x * Float64(x / y)) / y) + Float64(z * Float64(z / Float64(t * t))));
	else
		tmp = Float64(Float64(z / Float64(t / Float64(z / t))) + Float64(x / Float64(y * Float64(y / x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= 6.9e-218)
		tmp = ((x * (x / y)) / y) + (z * (z / (t * t)));
	else
		tmp = (z / (t / (z / t))) + (x / (y * (y / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, 6.9e-218], N[(N[(N[(x * N[(x / y), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision] + N[(z * N[(z / N[(t * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z / N[(t / N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x / N[(y * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 6.90000000000000034e-218

   1. Initial program 67.2%

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

      1. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\frac{x \cdot x}{y \cdot y}\right), \color{blue}{\left(\frac{z \cdot z}{t \cdot t}\right)}\right) \]

      2. associate-/r* N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\frac{\frac{x \cdot x}{y}}{y}\right), \left(\frac{\color{blue}{z \cdot z}}{t \cdot t}\right)\right) \]

      3. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(\frac{x \cdot x}{y}\right), y\right), \left(\frac{\color{blue}{z \cdot z}}{t \cdot t}\right)\right) \]

      4. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(x \cdot \frac{x}{y}\right), y\right), \left(\frac{\color{blue}{z} \cdot z}{t \cdot t}\right)\right) \]

      5. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \left(\frac{x}{y}\right)\right), y\right), \left(\frac{\color{blue}{z} \cdot z}{t \cdot t}\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \left(\frac{z \cdot z}{t \cdot t}\right)\right) \]

      7. associate-/l* N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \left(z \cdot \color{blue}{\frac{z}{t \cdot t}}\right)\right) \]

      8. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \color{blue}{\left(\frac{z}{t \cdot t}\right)}\right)\right) \]

      9. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \color{blue}{\left(t \cdot t\right)}\right)\right)\right) \]

      10. *-lowering-*.f64 89.6%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(x, \mathsf{/.f64}\left(x, y\right)\right), y\right), \mathsf{*.f64}\left(z, \mathsf{/.f64}\left(z, \mathsf{*.f64}\left(t, \color{blue}{t}\right)\right)\right)\right) \]

   3. Simplified 89.6%

      \[\leadsto \color{blue}{\frac{x \cdot \frac{x}{y}}{y} + z \cdot \frac{z}{t \cdot t}} \]
   4. Add Preprocessing

   if 6.90000000000000034e-218 < y

   1. Initial program 67.8%

      \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. associate-*r/ N/A

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

      2. +-commutative N/A

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

      3. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\left(z \cdot \frac{z}{t \cdot t}\right), \color{blue}{\left(\frac{x \cdot x}{y \cdot y}\right)}\right) \]

      4. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\left(z \cdot \frac{1}{\frac{t \cdot t}{z}}\right), \left(\frac{x \cdot \color{blue}{x}}{y \cdot y}\right)\right) \]

      5. un-div-inv N/A

         \[\leadsto \mathsf{+.f64}\left(\left(\frac{z}{\frac{t \cdot t}{z}}\right), \left(\frac{\color{blue}{x \cdot x}}{y \cdot y}\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \left(\frac{t \cdot t}{z}\right)\right), \left(\frac{\color{blue}{x \cdot x}}{y \cdot y}\right)\right) \]

      7. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \left(\frac{1}{\frac{z}{t \cdot t}}\right)\right), \left(\frac{x \cdot \color{blue}{x}}{y \cdot y}\right)\right) \]

      8. associate-/r* N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \left(\frac{1}{\frac{\frac{z}{t}}{t}}\right)\right), \left(\frac{x \cdot x}{y \cdot y}\right)\right) \]

      9. clear-num N/A

         \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \left(\frac{t}{\frac{z}{t}}\right)\right), \left(\frac{x \cdot \color{blue}{x}}{y \cdot y}\right)\right) \]

      10. /-lowering-/.f64 N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \left(\frac{z}{t}\right)\right)\right), \left(\frac{x \cdot \color{blue}{x}}{y \cdot y}\right)\right) \]

      11. /-lowering-/.f64 N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{x \cdot x}{y \cdot y}\right)\right) \]

      12. times-frac N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{x}{y} \cdot \color{blue}{\frac{x}{y}}\right)\right) \]

      13. clear-num N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{1}{\frac{y}{x}} \cdot \frac{\color{blue}{x}}{y}\right)\right) \]

      14. frac-times N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{1 \cdot x}{\color{blue}{\frac{y}{x} \cdot y}}\right)\right) \]

      15. *-lft-identity N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \left(\frac{x}{\color{blue}{\frac{y}{x}} \cdot y}\right)\right) \]

      16. /-lowering-/.f64 N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \mathsf{/.f64}\left(x, \color{blue}{\left(\frac{y}{x} \cdot y\right)}\right)\right) \]

      17. *-lowering-*.f64 N/A

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \mathsf{/.f64}\left(x, \mathsf{*.f64}\left(\left(\frac{y}{x}\right), \color{blue}{y}\right)\right)\right) \]

      18. /-lowering-/.f64 94.5%

          \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, t\right)\right)\right), \mathsf{/.f64}\left(x, \mathsf{*.f64}\left(\mathsf{/.f64}\left(y, x\right), y\right)\right)\right) \]

   4. Applied egg-rr 94.5%

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

4. Final simplification 91.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 6.9 \cdot 10^{-218}:\\ \;\;\;\;\frac{x \cdot \frac{x}{y}}{y} + z \cdot \frac{z}{t \cdot t}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{\frac{z}{t}}} + \frac{x}{y \cdot \frac{y}{x}}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 80.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{z \cdot z}{t \cdot t} \leq 4 \cdot 10^{+61}:\\ \;\;\;\;\frac{\frac{x}{y}}{\frac{y}{x}}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{\frac{z}{t}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ (* z z) (* t t)) 4e+61) (/ (/ x y) (/ y x)) (/ z (/ t (/ z t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * z) / (t * t)) <= 4e+61) {
		tmp = (x / y) / (y / x);
	} else {
		tmp = z / (t / (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 (((z * z) / (t * t)) <= 4d+61) then
        tmp = (x / y) / (y / x)
    else
        tmp = z / (t / (z / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * z) / (t * t)) <= 4e+61) {
		tmp = (x / y) / (y / x);
	} else {
		tmp = z / (t / (z / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((z * z) / (t * t)) <= 4e+61:
		tmp = (x / y) / (y / x)
	else:
		tmp = z / (t / (z / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(Float64(z * z) / Float64(t * t)) <= 4e+61)
		tmp = Float64(Float64(x / y) / Float64(y / x));
	else
		tmp = Float64(z / Float64(t / Float64(z / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((z * z) / (t * t)) <= 4e+61)
		tmp = (x / y) / (y / x);
	else
		tmp = z / (t / (z / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(N[(z * z), $MachinePrecision] / N[(t * t), $MachinePrecision]), $MachinePrecision], 4e+61], N[(N[(x / y), $MachinePrecision] / N[(y / x), $MachinePrecision]), $MachinePrecision], N[(z / N[(t / N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 z z) (*.f64 t t)) < 3.9999999999999998e61

   1. Initial program 65.9%

      \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{{x}^{2}}{{y}^{2}}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto \frac{x \cdot x}{{\color{blue}{y}}^{2}} \]

      2. associate-/l* N/A

         \[\leadsto x \cdot \color{blue}{\frac{x}{{y}^{2}}} \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(\frac{x}{{y}^{2}}\right)}\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{x}{y \cdot \color{blue}{y}}\right)\right) \]

      5. associate-/r* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{\frac{x}{y}}{\color{blue}{y}}\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{y}\right)\right) \]

      7. /-lowering-/.f64 77.1%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), y\right)\right) \]

   5. Simplified 77.1%

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

      1. associate-*r/ N/A

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

      2. associate-*l/ N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{\left(\frac{y}{x}\right)}\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), \left(\frac{\color{blue}{y}}{x}\right)\right) \]

      7. /-lowering-/.f64 86.3%

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), \mathsf{/.f64}\left(y, \color{blue}{x}\right)\right) \]

   7. Applied egg-rr 86.3%

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

   if 3.9999999999999998e61 < (/.f64 (*.f64 z z) (*.f64 t t))

   1. Initial program 68.8%

      \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{{z}^{2}}{{t}^{2}}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto \frac{{z}^{2}}{t \cdot \color{blue}{t}} \]

      2. associate-/r* N/A

         \[\leadsto \frac{\frac{{z}^{2}}{t}}{\color{blue}{t}} \]

      3. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\left(\frac{{z}^{2}}{t}\right), \color{blue}{t}\right) \]

      4. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(\left({z}^{2}\right), t\right), t\right) \]

      5. unpow2 N/A

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(\left(z \cdot z\right), t\right), t\right) \]

      6. *-lowering-*.f64 75.7%

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(z, z\right), t\right), t\right) \]

   5. Simplified 75.7%

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

      1. associate-/l/ N/A

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

      2. frac-times N/A

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

      3. associate-/r/ N/A

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

      4. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(z, \color{blue}{\left(\frac{t}{\frac{z}{t}}\right)}\right) \]

      5. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \color{blue}{\left(\frac{z}{t}\right)}\right)\right) \]

      6. /-lowering-/.f64 84.2%

         \[\leadsto \mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, \color{blue}{t}\right)\right)\right) \]

   7. Applied egg-rr 84.2%

      \[\leadsto \color{blue}{\frac{z}{\frac{t}{\frac{z}{t}}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 80.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{z \cdot z}{t \cdot t} \leq 4 \cdot 10^{+61}:\\ \;\;\;\;\frac{x}{y} \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{\frac{z}{t}}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ (* z z) (* t t)) 4e+61) (* (/ x y) (/ x y)) (/ z (/ t (/ z t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * z) / (t * t)) <= 4e+61) {
		tmp = (x / y) * (x / y);
	} else {
		tmp = z / (t / (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 (((z * z) / (t * t)) <= 4d+61) then
        tmp = (x / y) * (x / y)
    else
        tmp = z / (t / (z / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (((z * z) / (t * t)) <= 4e+61) {
		tmp = (x / y) * (x / y);
	} else {
		tmp = z / (t / (z / t));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if ((z * z) / (t * t)) <= 4e+61:
		tmp = (x / y) * (x / y)
	else:
		tmp = z / (t / (z / t))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(Float64(z * z) / Float64(t * t)) <= 4e+61)
		tmp = Float64(Float64(x / y) * Float64(x / y));
	else
		tmp = Float64(z / Float64(t / Float64(z / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (((z * z) / (t * t)) <= 4e+61)
		tmp = (x / y) * (x / y);
	else
		tmp = z / (t / (z / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(N[(z * z), $MachinePrecision] / N[(t * t), $MachinePrecision]), $MachinePrecision], 4e+61], N[(N[(x / y), $MachinePrecision] * N[(x / y), $MachinePrecision]), $MachinePrecision], N[(z / N[(t / N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 z z) (*.f64 t t)) < 3.9999999999999998e61

   1. Initial program 65.9%

      \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{{x}^{2}}{{y}^{2}}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto \frac{x \cdot x}{{\color{blue}{y}}^{2}} \]

      2. associate-/l* N/A

         \[\leadsto x \cdot \color{blue}{\frac{x}{{y}^{2}}} \]

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(\frac{x}{{y}^{2}}\right)}\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{x}{y \cdot \color{blue}{y}}\right)\right) \]

      5. associate-/r* N/A

         \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{\frac{x}{y}}{\color{blue}{y}}\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{y}\right)\right) \]

      7. /-lowering-/.f64 77.1%

         \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), y\right)\right) \]

   5. Simplified 77.1%

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

      1. associate-*r/ N/A

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

      2. associate-*l/ N/A

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

      3. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(\left(\frac{x}{y}\right), \color{blue}{\left(\frac{x}{y}\right)}\right) \]

      4. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{*.f64}\left(\mathsf{/.f64}\left(x, y\right), \left(\frac{\color{blue}{x}}{y}\right)\right) \]

      5. /-lowering-/.f64 86.1%

         \[\leadsto \mathsf{*.f64}\left(\mathsf{/.f64}\left(x, y\right), \mathsf{/.f64}\left(x, \color{blue}{y}\right)\right) \]

   7. Applied egg-rr 86.1%

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

   if 3.9999999999999998e61 < (/.f64 (*.f64 z z) (*.f64 t t))

   1. Initial program 68.8%

      \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{{z}^{2}}{{t}^{2}}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

         \[\leadsto \frac{{z}^{2}}{t \cdot \color{blue}{t}} \]

      2. associate-/r* N/A

         \[\leadsto \frac{\frac{{z}^{2}}{t}}{\color{blue}{t}} \]

      3. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\left(\frac{{z}^{2}}{t}\right), \color{blue}{t}\right) \]

      4. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(\left({z}^{2}\right), t\right), t\right) \]

      5. unpow2 N/A

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(\left(z \cdot z\right), t\right), t\right) \]

      6. *-lowering-*.f64 75.7%

         \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(z, z\right), t\right), t\right) \]

   5. Simplified 75.7%

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

      1. associate-/l/ N/A

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

      2. frac-times N/A

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

      3. associate-/r/ N/A

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

      4. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(z, \color{blue}{\left(\frac{t}{\frac{z}{t}}\right)}\right) \]

      5. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \color{blue}{\left(\frac{z}{t}\right)}\right)\right) \]

      6. /-lowering-/.f64 84.2%

         \[\leadsto \mathsf{/.f64}\left(z, \mathsf{/.f64}\left(t, \mathsf{/.f64}\left(z, \color{blue}{t}\right)\right)\right) \]

   7. Applied egg-rr 84.2%

      \[\leadsto \color{blue}{\frac{z}{\frac{t}{\frac{z}{t}}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 59.1% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{x}{y} \cdot \frac{x}{y} \end{array} \]

FPCore

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

C

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

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) * (x / y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 67.4%

   \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{{x}^{2}}{{y}^{2}}} \]
4. Step-by-step derivation

   1. unpow2 N/A

      \[\leadsto \frac{x \cdot x}{{\color{blue}{y}}^{2}} \]

   2. associate-/l* N/A

      \[\leadsto x \cdot \color{blue}{\frac{x}{{y}^{2}}} \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(\frac{x}{{y}^{2}}\right)}\right) \]

   4. unpow2 N/A

      \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{x}{y \cdot \color{blue}{y}}\right)\right) \]

   5. associate-/r* N/A

      \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{\frac{x}{y}}{\color{blue}{y}}\right)\right) \]

   6. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{y}\right)\right) \]

   7. /-lowering-/.f64 57.9%

      \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), y\right)\right) \]

5. Simplified 57.9%

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

   1. associate-*r/ N/A

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

   2. associate-*l/ N/A

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

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\left(\frac{x}{y}\right), \color{blue}{\left(\frac{x}{y}\right)}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\mathsf{/.f64}\left(x, y\right), \left(\frac{\color{blue}{x}}{y}\right)\right) \]

   5. /-lowering-/.f64 61.1%

      \[\leadsto \mathsf{*.f64}\left(\mathsf{/.f64}\left(x, y\right), \mathsf{/.f64}\left(x, \color{blue}{y}\right)\right) \]

7. Applied egg-rr 61.1%

   \[\leadsto \color{blue}{\frac{x}{y} \cdot \frac{x}{y}} \]
8. Add Preprocessing

Alternative 8: 56.9% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ x \cdot \frac{\frac{x}{y}}{y} \end{array} \]

FPCore

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

C

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

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) / y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 67.4%

   \[\frac{x \cdot x}{y \cdot y} + \frac{z \cdot z}{t \cdot t} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{{x}^{2}}{{y}^{2}}} \]
4. Step-by-step derivation

   1. unpow2 N/A

      \[\leadsto \frac{x \cdot x}{{\color{blue}{y}}^{2}} \]

   2. associate-/l* N/A

      \[\leadsto x \cdot \color{blue}{\frac{x}{{y}^{2}}} \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{\left(\frac{x}{{y}^{2}}\right)}\right) \]

   4. unpow2 N/A

      \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{x}{y \cdot \color{blue}{y}}\right)\right) \]

   5. associate-/r* N/A

      \[\leadsto \mathsf{*.f64}\left(x, \left(\frac{\frac{x}{y}}{\color{blue}{y}}\right)\right) \]

   6. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\left(\frac{x}{y}\right), \color{blue}{y}\right)\right) \]

   7. /-lowering-/.f64 57.9%

      \[\leadsto \mathsf{*.f64}\left(x, \mathsf{/.f64}\left(\mathsf{/.f64}\left(x, y\right), y\right)\right) \]

5. Simplified 57.9%

   \[\leadsto \color{blue}{x \cdot \frac{\frac{x}{y}}{y}} \]
6. Add Preprocessing

Developer Target 1: 99.6% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ {\left(\frac{x}{y}\right)}^{2} + {\left(\frac{z}{t}\right)}^{2} \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (+ (pow (/ x y) 2.0) (pow (/ z t) 2.0)))

C

double code(double x, double y, double z, double t) {
	return pow((x / y), 2.0) + pow((z / t), 2.0);
}

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) ** 2.0d0) + ((z / t) ** 2.0d0)
end function

Java

public static double code(double x, double y, double z, double t) {
	return Math.pow((x / y), 2.0) + Math.pow((z / t), 2.0);
}

Python

def code(x, y, z, t):
	return math.pow((x / y), 2.0) + math.pow((z / t), 2.0)

Julia

function code(x, y, z, t)
	return Float64((Float64(x / y) ^ 2.0) + (Float64(z / t) ^ 2.0))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = ((x / y) ^ 2.0) + ((z / t) ^ 2.0);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[Power[N[(x / y), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(z / t), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024161 
(FPCore (x y z t)
  :name "Graphics.Rasterific.Svg.PathConverter:arcToSegments from rasterific-svg-0.2.3.1"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (pow (/ x y) 2) (pow (/ z t) 2)))

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