Diagrams.TwoD.Arc:arcBetween from diagrams-lib-1.3.0.3

Percentage Accurate: 49.8% → 80.8%

Time: 7.7s

Alternatives: 5

Speedup: 48.0×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(y \cdot 4\right) \cdot y\\ \frac{x \cdot x - t\_0}{x \cdot x + t\_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (* y 4.0) y))) (/ (- (* x x) t_0) (+ (* x x) t_0))))

C

double code(double x, double y) {
	double t_0 = (y * 4.0) * y;
	return ((x * x) - t_0) / ((x * x) + t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = (y * 4.0d0) * y
    code = ((x * x) - t_0) / ((x * x) + t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = (y * 4.0) * y;
	return ((x * x) - t_0) / ((x * x) + t_0);
}

Python

def code(x, y):
	t_0 = (y * 4.0) * y
	return ((x * x) - t_0) / ((x * x) + t_0)

Julia

function code(x, y)
	t_0 = Float64(Float64(y * 4.0) * y)
	return Float64(Float64(Float64(x * x) - t_0) / Float64(Float64(x * x) + t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = (y * 4.0) * y;
	tmp = ((x * x) - t_0) / ((x * x) + t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y * 4.0), $MachinePrecision] * y), $MachinePrecision]}, N[(N[(N[(x * x), $MachinePrecision] - t$95$0), $MachinePrecision] / N[(N[(x * x), $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]]

Initial Program: 49.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(y \cdot 4\right) \cdot y\\ \frac{x \cdot x - t\_0}{x \cdot x + t\_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (* y 4.0) y))) (/ (- (* x x) t_0) (+ (* x x) t_0))))

C

double code(double x, double y) {
	double t_0 = (y * 4.0) * y;
	return ((x * x) - t_0) / ((x * x) + t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = (y * 4.0d0) * y
    code = ((x * x) - t_0) / ((x * x) + t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = (y * 4.0) * y;
	return ((x * x) - t_0) / ((x * x) + t_0);
}

Python

def code(x, y):
	t_0 = (y * 4.0) * y
	return ((x * x) - t_0) / ((x * x) + t_0)

Julia

function code(x, y)
	t_0 = Float64(Float64(y * 4.0) * y)
	return Float64(Float64(Float64(x * x) - t_0) / Float64(Float64(x * x) + t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = (y * 4.0) * y;
	tmp = ((x * x) - t_0) / ((x * x) + t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y * 4.0), $MachinePrecision] * y), $MachinePrecision]}, N[(N[(N[(x * x), $MachinePrecision] - t$95$0), $MachinePrecision] / N[(N[(x * x), $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]]

Alternative 1: 80.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(y \cdot 4\right)\\ \mathbf{if}\;t\_0 \leq 4 \cdot 10^{-225}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y \cdot -8}{x}, \frac{y}{x}, 1\right)\\ \mathbf{elif}\;t\_0 \leq 10^{+237}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, x, \left(y \cdot y\right) \cdot -4\right)}{\mathsf{fma}\left(y, y \cdot 4, x \cdot x\right)}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot 0.5}{y}, -1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (* y 4.0))))
   (if (<= t_0 4e-225)
     (fma (/ (* y -8.0) x) (/ y x) 1.0)
     (if (<= t_0 1e+237)
       (/ (fma x x (* (* y y) -4.0)) (fma y (* y 4.0) (* x x)))
       (fma (/ x y) (/ (* x 0.5) y) -1.0)))))

C

double code(double x, double y) {
	double t_0 = y * (y * 4.0);
	double tmp;
	if (t_0 <= 4e-225) {
		tmp = fma(((y * -8.0) / x), (y / x), 1.0);
	} else if (t_0 <= 1e+237) {
		tmp = fma(x, x, ((y * y) * -4.0)) / fma(y, (y * 4.0), (x * x));
	} else {
		tmp = fma((x / y), ((x * 0.5) / y), -1.0);
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(y * Float64(y * 4.0))
	tmp = 0.0
	if (t_0 <= 4e-225)
		tmp = fma(Float64(Float64(y * -8.0) / x), Float64(y / x), 1.0);
	elseif (t_0 <= 1e+237)
		tmp = Float64(fma(x, x, Float64(Float64(y * y) * -4.0)) / fma(y, Float64(y * 4.0), Float64(x * x)));
	else
		tmp = fma(Float64(x / y), Float64(Float64(x * 0.5) / y), -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[(y * 4.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, 4e-225], N[(N[(N[(y * -8.0), $MachinePrecision] / x), $MachinePrecision] * N[(y / x), $MachinePrecision] + 1.0), $MachinePrecision], If[LessEqual[t$95$0, 1e+237], N[(N[(x * x + N[(N[(y * y), $MachinePrecision] * -4.0), $MachinePrecision]), $MachinePrecision] / N[(y * N[(y * 4.0), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x / y), $MachinePrecision] * N[(N[(x * 0.5), $MachinePrecision] / y), $MachinePrecision] + -1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 y #s(literal 4 binary64)) y) < 3.9999999999999998e-225

   1. Initial program 64.5%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\left(1 + -4 \cdot \frac{{y}^{2}}{{x}^{2}}\right) - 4 \cdot \frac{{y}^{2}}{{x}^{2}}} \]
   4. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{1 + \left(-4 \cdot \frac{{y}^{2}}{{x}^{2}} - 4 \cdot \frac{{y}^{2}}{{x}^{2}}\right)} \]

      2. distribute-rgt-out-- N/A

         \[\leadsto 1 + \color{blue}{\frac{{y}^{2}}{{x}^{2}} \cdot \left(-4 - 4\right)} \]

      3. metadata-eval N/A

         \[\leadsto 1 + \frac{{y}^{2}}{{x}^{2}} \cdot \color{blue}{-8} \]

      4. *-commutative N/A

         \[\leadsto 1 + \color{blue}{-8 \cdot \frac{{y}^{2}}{{x}^{2}}} \]

      5. +-commutative N/A

         \[\leadsto \color{blue}{-8 \cdot \frac{{y}^{2}}{{x}^{2}} + 1} \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{y}^{2}}{{x}^{2}} \cdot -8} + 1 \]

      7. unpow2 N/A

         \[\leadsto \frac{\color{blue}{y \cdot y}}{{x}^{2}} \cdot -8 + 1 \]

      8. associate-/l* N/A

         \[\leadsto \color{blue}{\left(y \cdot \frac{y}{{x}^{2}}\right)} \cdot -8 + 1 \]

      9. associate-*l* N/A

         \[\leadsto \color{blue}{y \cdot \left(\frac{y}{{x}^{2}} \cdot -8\right)} + 1 \]

      10. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{y}{{x}^{2}} \cdot -8, 1\right)} \]

      11. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{\frac{y}{{x}^{2}} \cdot -8}, 1\right) \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{\frac{y}{{x}^{2}}} \cdot -8, 1\right) \]

      13. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(y, \frac{y}{\color{blue}{x \cdot x}} \cdot -8, 1\right) \]

      14. lower-*.f64 80.3

          \[\leadsto \mathsf{fma}\left(y, \frac{y}{\color{blue}{x \cdot x}} \cdot -8, 1\right) \]

   5. Simplified 80.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{y}{x \cdot x} \cdot -8, 1\right)} \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto y \cdot \left(\frac{y}{\color{blue}{x \cdot x}} \cdot -8\right) + 1 \]

      2. lift-/.f64 N/A

         \[\leadsto y \cdot \left(\color{blue}{\frac{y}{x \cdot x}} \cdot -8\right) + 1 \]

      3. lift-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(\frac{y}{x \cdot x} \cdot -8\right)} + 1 \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{y}{x \cdot x} \cdot -8\right) \cdot y} + 1 \]

      5. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{y}{x \cdot x} \cdot -8\right)} \cdot y + 1 \]

      6. lift-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{y}{x \cdot x}} \cdot -8\right) \cdot y + 1 \]

      7. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{y \cdot -8}{x \cdot x}} \cdot y + 1 \]

      8. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\left(y \cdot -8\right) \cdot y}{x \cdot x}} + 1 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(y \cdot -8\right) \cdot y}{\color{blue}{x \cdot x}} + 1 \]

      10. times-frac N/A

          \[\leadsto \color{blue}{\frac{y \cdot -8}{x} \cdot \frac{y}{x}} + 1 \]

      11. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{y \cdot -8}{x}, \frac{y}{x}, 1\right)} \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{y \cdot -8}{x}}, \frac{y}{x}, 1\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\color{blue}{y \cdot -8}}{x}, \frac{y}{x}, 1\right) \]

      14. lower-/.f64 88.4

          \[\leadsto \mathsf{fma}\left(\frac{y \cdot -8}{x}, \color{blue}{\frac{y}{x}}, 1\right) \]

   7. Applied egg-rr 88.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{y \cdot -8}{x}, \frac{y}{x}, 1\right)} \]

   if 3.9999999999999998e-225 < (*.f64 (*.f64 y #s(literal 4 binary64)) y) < 9.9999999999999994e236

   1. Initial program 80.6%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. lift-+.f64 N/A

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

      8. lift--.f64 N/A

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

      9. lift-/.f64 80.6

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

   4. Applied egg-rr 80.6%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(x, x, \left(y \cdot y\right) \cdot -4\right)}{\mathsf{fma}\left(y, y \cdot 4, x \cdot x\right)}} \]

   if 9.9999999999999994e236 < (*.f64 (*.f64 y #s(literal 4 binary64)) y)

   1. Initial program 12.2%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{x}^{2}}{{y}^{2}} - 1} \]
   4. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{x}^{2}}{{y}^{2}} + \left(\mathsf{neg}\left(1\right)\right)} \]

      2. *-lft-identity N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\color{blue}{1 \cdot {x}^{2}}}{{y}^{2}} + \left(\mathsf{neg}\left(1\right)\right) \]

      3. associate-*l/ N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\left(\frac{1}{{y}^{2}} \cdot {x}^{2}\right)} + \left(\mathsf{neg}\left(1\right)\right) \]

      4. associate-*l* N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{{y}^{2}}\right) \cdot {x}^{2}} + \left(\mathsf{neg}\left(1\right)\right) \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{2} \cdot \frac{1}{{y}^{2}}\right)} + \left(\mathsf{neg}\left(1\right)\right) \]

      6. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{2} \cdot \frac{1}{{y}^{2}}, \mathsf{neg}\left(1\right)\right)} \]

      7. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{2} \cdot \frac{1}{{y}^{2}}, \mathsf{neg}\left(1\right)\right) \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{2} \cdot \frac{1}{{y}^{2}}, \mathsf{neg}\left(1\right)\right) \]

      9. associate-*r/ N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{\frac{1}{2} \cdot 1}{{y}^{2}}}, \mathsf{neg}\left(1\right)\right) \]

      10. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{\color{blue}{\frac{1}{2}}}{{y}^{2}}, \mathsf{neg}\left(1\right)\right) \]

      11. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{\frac{1}{2}}{{y}^{2}}}, \mathsf{neg}\left(1\right)\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{\frac{1}{2}}{\color{blue}{y \cdot y}}, \mathsf{neg}\left(1\right)\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{\frac{1}{2}}{\color{blue}{y \cdot y}}, \mathsf{neg}\left(1\right)\right) \]

      14. metadata-eval 73.3

          \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{0.5}{y \cdot y}, \color{blue}{-1}\right) \]

   5. Simplified 73.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, \frac{0.5}{y \cdot y}, -1\right)} \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot x\right)} \cdot \frac{\frac{1}{2}}{y \cdot y} + -1 \]

      2. lift-*.f64 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \frac{\frac{1}{2}}{\color{blue}{y \cdot y}} + -1 \]

      3. lift-/.f64 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{\frac{\frac{1}{2}}{y \cdot y}} + -1 \]

      4. lift-/.f64 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{\frac{\frac{1}{2}}{y \cdot y}} + -1 \]

      5. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{\left(x \cdot x\right) \cdot \frac{1}{2}}{y \cdot y}} + -1 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(x \cdot x\right) \cdot \frac{1}{2}}{\color{blue}{y \cdot y}} + -1 \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{2}}{y \cdot y} + -1 \]

      8. associate-*l* N/A

         \[\leadsto \frac{\color{blue}{x \cdot \left(x \cdot \frac{1}{2}\right)}}{y \cdot y} + -1 \]

      9. times-frac N/A

         \[\leadsto \color{blue}{\frac{x}{y} \cdot \frac{x \cdot \frac{1}{2}}{y}} + -1 \]

      10. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot \frac{1}{2}}{y}, -1\right)} \]

      11. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{x}{y}}, \frac{x \cdot \frac{1}{2}}{y}, -1\right) \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{x}{y}, \color{blue}{\frac{x \cdot \frac{1}{2}}{y}}, -1\right) \]

      13. lower-*.f64 86.2

          \[\leadsto \mathsf{fma}\left(\frac{x}{y}, \frac{\color{blue}{x \cdot 0.5}}{y}, -1\right) \]

   7. Applied egg-rr 86.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot 0.5}{y}, -1\right)} \]
3. Recombined 3 regimes into one program.

4. Final simplification 84.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot \left(y \cdot 4\right) \leq 4 \cdot 10^{-225}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y \cdot -8}{x}, \frac{y}{x}, 1\right)\\ \mathbf{elif}\;y \cdot \left(y \cdot 4\right) \leq 10^{+237}:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, x, \left(y \cdot y\right) \cdot -4\right)}{\mathsf{fma}\left(y, y \cdot 4, x \cdot x\right)}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot 0.5}{y}, -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 75.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(y \cdot 4\right)\\ t_1 := \frac{x \cdot x - t\_0}{t\_0 + x \cdot x}\\ t_2 := \mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot 0.5}{y}, -1\right)\\ \mathbf{if}\;t\_1 \leq -0.5:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 2:\\ \;\;\;\;\mathsf{fma}\left(y, -8 \cdot \frac{y}{x \cdot x}, 1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (* y 4.0)))
        (t_1 (/ (- (* x x) t_0) (+ t_0 (* x x))))
        (t_2 (fma (/ x y) (/ (* x 0.5) y) -1.0)))
   (if (<= t_1 -0.5)
     t_2
     (if (<= t_1 2.0) (fma y (* -8.0 (/ y (* x x))) 1.0) t_2))))

C

double code(double x, double y) {
	double t_0 = y * (y * 4.0);
	double t_1 = ((x * x) - t_0) / (t_0 + (x * x));
	double t_2 = fma((x / y), ((x * 0.5) / y), -1.0);
	double tmp;
	if (t_1 <= -0.5) {
		tmp = t_2;
	} else if (t_1 <= 2.0) {
		tmp = fma(y, (-8.0 * (y / (x * x))), 1.0);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(y * Float64(y * 4.0))
	t_1 = Float64(Float64(Float64(x * x) - t_0) / Float64(t_0 + Float64(x * x)))
	t_2 = fma(Float64(x / y), Float64(Float64(x * 0.5) / y), -1.0)
	tmp = 0.0
	if (t_1 <= -0.5)
		tmp = t_2;
	elseif (t_1 <= 2.0)
		tmp = fma(y, Float64(-8.0 * Float64(y / Float64(x * x))), 1.0);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[(y * 4.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(x * x), $MachinePrecision] - t$95$0), $MachinePrecision] / N[(t$95$0 + N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x / y), $MachinePrecision] * N[(N[(x * 0.5), $MachinePrecision] / y), $MachinePrecision] + -1.0), $MachinePrecision]}, If[LessEqual[t$95$1, -0.5], t$95$2, If[LessEqual[t$95$1, 2.0], N[(y * N[(-8.0 * N[(y / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y))) < -0.5 or 2 < (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)))

   1. Initial program 37.2%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{x}^{2}}{{y}^{2}} - 1} \]
   4. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{x}^{2}}{{y}^{2}} + \left(\mathsf{neg}\left(1\right)\right)} \]

      2. *-lft-identity N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\color{blue}{1 \cdot {x}^{2}}}{{y}^{2}} + \left(\mathsf{neg}\left(1\right)\right) \]

      3. associate-*l/ N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\left(\frac{1}{{y}^{2}} \cdot {x}^{2}\right)} + \left(\mathsf{neg}\left(1\right)\right) \]

      4. associate-*l* N/A

         \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{{y}^{2}}\right) \cdot {x}^{2}} + \left(\mathsf{neg}\left(1\right)\right) \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{2} \cdot \frac{1}{{y}^{2}}\right)} + \left(\mathsf{neg}\left(1\right)\right) \]

      6. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{2} \cdot \frac{1}{{y}^{2}}, \mathsf{neg}\left(1\right)\right)} \]

      7. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{2} \cdot \frac{1}{{y}^{2}}, \mathsf{neg}\left(1\right)\right) \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{2} \cdot \frac{1}{{y}^{2}}, \mathsf{neg}\left(1\right)\right) \]

      9. associate-*r/ N/A

         \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{\frac{1}{2} \cdot 1}{{y}^{2}}}, \mathsf{neg}\left(1\right)\right) \]

      10. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{\color{blue}{\frac{1}{2}}}{{y}^{2}}, \mathsf{neg}\left(1\right)\right) \]

      11. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{\frac{1}{2}}{{y}^{2}}}, \mathsf{neg}\left(1\right)\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{\frac{1}{2}}{\color{blue}{y \cdot y}}, \mathsf{neg}\left(1\right)\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{\frac{1}{2}}{\color{blue}{y \cdot y}}, \mathsf{neg}\left(1\right)\right) \]

      14. metadata-eval 63.9

          \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{0.5}{y \cdot y}, \color{blue}{-1}\right) \]

   5. Simplified 63.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, \frac{0.5}{y \cdot y}, -1\right)} \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot x\right)} \cdot \frac{\frac{1}{2}}{y \cdot y} + -1 \]

      2. lift-*.f64 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \frac{\frac{1}{2}}{\color{blue}{y \cdot y}} + -1 \]

      3. lift-/.f64 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{\frac{\frac{1}{2}}{y \cdot y}} + -1 \]

      4. lift-/.f64 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{\frac{\frac{1}{2}}{y \cdot y}} + -1 \]

      5. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{\left(x \cdot x\right) \cdot \frac{1}{2}}{y \cdot y}} + -1 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(x \cdot x\right) \cdot \frac{1}{2}}{\color{blue}{y \cdot y}} + -1 \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{2}}{y \cdot y} + -1 \]

      8. associate-*l* N/A

         \[\leadsto \frac{\color{blue}{x \cdot \left(x \cdot \frac{1}{2}\right)}}{y \cdot y} + -1 \]

      9. times-frac N/A

         \[\leadsto \color{blue}{\frac{x}{y} \cdot \frac{x \cdot \frac{1}{2}}{y}} + -1 \]

      10. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot \frac{1}{2}}{y}, -1\right)} \]

      11. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{x}{y}}, \frac{x \cdot \frac{1}{2}}{y}, -1\right) \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{x}{y}, \color{blue}{\frac{x \cdot \frac{1}{2}}{y}}, -1\right) \]

      13. lower-*.f64 72.0

          \[\leadsto \mathsf{fma}\left(\frac{x}{y}, \frac{\color{blue}{x \cdot 0.5}}{y}, -1\right) \]

   7. Applied egg-rr 72.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot 0.5}{y}, -1\right)} \]

   if -0.5 < (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y))) < 2

   1. Initial program 100.0%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\left(1 + -4 \cdot \frac{{y}^{2}}{{x}^{2}}\right) - 4 \cdot \frac{{y}^{2}}{{x}^{2}}} \]
   4. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{1 + \left(-4 \cdot \frac{{y}^{2}}{{x}^{2}} - 4 \cdot \frac{{y}^{2}}{{x}^{2}}\right)} \]

      2. distribute-rgt-out-- N/A

         \[\leadsto 1 + \color{blue}{\frac{{y}^{2}}{{x}^{2}} \cdot \left(-4 - 4\right)} \]

      3. metadata-eval N/A

         \[\leadsto 1 + \frac{{y}^{2}}{{x}^{2}} \cdot \color{blue}{-8} \]

      4. *-commutative N/A

         \[\leadsto 1 + \color{blue}{-8 \cdot \frac{{y}^{2}}{{x}^{2}}} \]

      5. +-commutative N/A

         \[\leadsto \color{blue}{-8 \cdot \frac{{y}^{2}}{{x}^{2}} + 1} \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{y}^{2}}{{x}^{2}} \cdot -8} + 1 \]

      7. unpow2 N/A

         \[\leadsto \frac{\color{blue}{y \cdot y}}{{x}^{2}} \cdot -8 + 1 \]

      8. associate-/l* N/A

         \[\leadsto \color{blue}{\left(y \cdot \frac{y}{{x}^{2}}\right)} \cdot -8 + 1 \]

      9. associate-*l* N/A

         \[\leadsto \color{blue}{y \cdot \left(\frac{y}{{x}^{2}} \cdot -8\right)} + 1 \]

      10. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{y}{{x}^{2}} \cdot -8, 1\right)} \]

      11. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{\frac{y}{{x}^{2}} \cdot -8}, 1\right) \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{\frac{y}{{x}^{2}}} \cdot -8, 1\right) \]

      13. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(y, \frac{y}{\color{blue}{x \cdot x}} \cdot -8, 1\right) \]

      14. lower-*.f64 99.6

          \[\leadsto \mathsf{fma}\left(y, \frac{y}{\color{blue}{x \cdot x}} \cdot -8, 1\right) \]

   5. Simplified 99.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{y}{x \cdot x} \cdot -8, 1\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x \cdot x - y \cdot \left(y \cdot 4\right)}{y \cdot \left(y \cdot 4\right) + x \cdot x} \leq -0.5:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot 0.5}{y}, -1\right)\\ \mathbf{elif}\;\frac{x \cdot x - y \cdot \left(y \cdot 4\right)}{y \cdot \left(y \cdot 4\right) + x \cdot x} \leq 2:\\ \;\;\;\;\mathsf{fma}\left(y, -8 \cdot \frac{y}{x \cdot x}, 1\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{x}{y}, \frac{x \cdot 0.5}{y}, -1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 74.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(y \cdot 4\right)\\ t_1 := \frac{x \cdot x - t\_0}{t\_0 + x \cdot x}\\ \mathbf{if}\;t\_1 \leq -0.5:\\ \;\;\;\;-1\\ \mathbf{elif}\;t\_1 \leq 2:\\ \;\;\;\;\mathsf{fma}\left(y, -8 \cdot \frac{y}{x \cdot x}, 1\right)\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (* y 4.0))) (t_1 (/ (- (* x x) t_0) (+ t_0 (* x x)))))
   (if (<= t_1 -0.5)
     -1.0
     (if (<= t_1 2.0) (fma y (* -8.0 (/ y (* x x))) 1.0) -1.0))))

C

double code(double x, double y) {
	double t_0 = y * (y * 4.0);
	double t_1 = ((x * x) - t_0) / (t_0 + (x * x));
	double tmp;
	if (t_1 <= -0.5) {
		tmp = -1.0;
	} else if (t_1 <= 2.0) {
		tmp = fma(y, (-8.0 * (y / (x * x))), 1.0);
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(y * Float64(y * 4.0))
	t_1 = Float64(Float64(Float64(x * x) - t_0) / Float64(t_0 + Float64(x * x)))
	tmp = 0.0
	if (t_1 <= -0.5)
		tmp = -1.0;
	elseif (t_1 <= 2.0)
		tmp = fma(y, Float64(-8.0 * Float64(y / Float64(x * x))), 1.0);
	else
		tmp = -1.0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[(y * 4.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(x * x), $MachinePrecision] - t$95$0), $MachinePrecision] / N[(t$95$0 + N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -0.5], -1.0, If[LessEqual[t$95$1, 2.0], N[(y * N[(-8.0 * N[(y / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision], -1.0]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y))) < -0.5 or 2 < (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)))

   1. Initial program 37.2%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-1} \]
   4. Step-by-step derivation

   5. Simplified 71.1%

      \[\leadsto \color{blue}{-1} \]

   if -0.5 < (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y))) < 2

   1. Initial program 100.0%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\left(1 + -4 \cdot \frac{{y}^{2}}{{x}^{2}}\right) - 4 \cdot \frac{{y}^{2}}{{x}^{2}}} \]
   4. Step-by-step derivation

      1. associate--l+ N/A

         \[\leadsto \color{blue}{1 + \left(-4 \cdot \frac{{y}^{2}}{{x}^{2}} - 4 \cdot \frac{{y}^{2}}{{x}^{2}}\right)} \]

      2. distribute-rgt-out-- N/A

         \[\leadsto 1 + \color{blue}{\frac{{y}^{2}}{{x}^{2}} \cdot \left(-4 - 4\right)} \]

      3. metadata-eval N/A

         \[\leadsto 1 + \frac{{y}^{2}}{{x}^{2}} \cdot \color{blue}{-8} \]

      4. *-commutative N/A

         \[\leadsto 1 + \color{blue}{-8 \cdot \frac{{y}^{2}}{{x}^{2}}} \]

      5. +-commutative N/A

         \[\leadsto \color{blue}{-8 \cdot \frac{{y}^{2}}{{x}^{2}} + 1} \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{{y}^{2}}{{x}^{2}} \cdot -8} + 1 \]

      7. unpow2 N/A

         \[\leadsto \frac{\color{blue}{y \cdot y}}{{x}^{2}} \cdot -8 + 1 \]

      8. associate-/l* N/A

         \[\leadsto \color{blue}{\left(y \cdot \frac{y}{{x}^{2}}\right)} \cdot -8 + 1 \]

      9. associate-*l* N/A

         \[\leadsto \color{blue}{y \cdot \left(\frac{y}{{x}^{2}} \cdot -8\right)} + 1 \]

      10. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{y}{{x}^{2}} \cdot -8, 1\right)} \]

      11. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{\frac{y}{{x}^{2}} \cdot -8}, 1\right) \]

      12. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(y, \color{blue}{\frac{y}{{x}^{2}}} \cdot -8, 1\right) \]

      13. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(y, \frac{y}{\color{blue}{x \cdot x}} \cdot -8, 1\right) \]

      14. lower-*.f64 99.6

          \[\leadsto \mathsf{fma}\left(y, \frac{y}{\color{blue}{x \cdot x}} \cdot -8, 1\right) \]

   5. Simplified 99.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{y}{x \cdot x} \cdot -8, 1\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 78.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x \cdot x - y \cdot \left(y \cdot 4\right)}{y \cdot \left(y \cdot 4\right) + x \cdot x} \leq -0.5:\\ \;\;\;\;-1\\ \mathbf{elif}\;\frac{x \cdot x - y \cdot \left(y \cdot 4\right)}{y \cdot \left(y \cdot 4\right) + x \cdot x} \leq 2:\\ \;\;\;\;\mathsf{fma}\left(y, -8 \cdot \frac{y}{x \cdot x}, 1\right)\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 74.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(y \cdot 4\right)\\ t_1 := \frac{x \cdot x - t\_0}{t\_0 + x \cdot x}\\ \mathbf{if}\;t\_1 \leq -1 \cdot 10^{-313}:\\ \;\;\;\;-1\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (* y 4.0))) (t_1 (/ (- (* x x) t_0) (+ t_0 (* x x)))))
   (if (<= t_1 -1e-313) -1.0 (if (<= t_1 INFINITY) 1.0 -1.0))))

C

double code(double x, double y) {
	double t_0 = y * (y * 4.0);
	double t_1 = ((x * x) - t_0) / (t_0 + (x * x));
	double tmp;
	if (t_1 <= -1e-313) {
		tmp = -1.0;
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double t_0 = y * (y * 4.0);
	double t_1 = ((x * x) - t_0) / (t_0 + (x * x));
	double tmp;
	if (t_1 <= -1e-313) {
		tmp = -1.0;
	} else if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = 1.0;
	} else {
		tmp = -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = y * (y * 4.0)
	t_1 = ((x * x) - t_0) / (t_0 + (x * x))
	tmp = 0
	if t_1 <= -1e-313:
		tmp = -1.0
	elif t_1 <= math.inf:
		tmp = 1.0
	else:
		tmp = -1.0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(y * Float64(y * 4.0))
	t_1 = Float64(Float64(Float64(x * x) - t_0) / Float64(t_0 + Float64(x * x)))
	tmp = 0.0
	if (t_1 <= -1e-313)
		tmp = -1.0;
	elseif (t_1 <= Inf)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = y * (y * 4.0);
	t_1 = ((x * x) - t_0) / (t_0 + (x * x));
	tmp = 0.0;
	if (t_1 <= -1e-313)
		tmp = -1.0;
	elseif (t_1 <= Inf)
		tmp = 1.0;
	else
		tmp = -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[(y * 4.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(x * x), $MachinePrecision] - t$95$0), $MachinePrecision] / N[(t$95$0 + N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -1e-313], -1.0, If[LessEqual[t$95$1, Infinity], 1.0, -1.0]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y))) < -1.00000000001e-313 or +inf.0 < (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)))

   1. Initial program 37.2%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-1} \]
   4. Step-by-step derivation

   5. Simplified 71.1%

      \[\leadsto \color{blue}{-1} \]

   if -1.00000000001e-313 < (/.f64 (-.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y)) (+.f64 (*.f64 x x) (*.f64 (*.f64 y #s(literal 4 binary64)) y))) < +inf.0

   1. Initial program 100.0%

      \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Simplified 98.9%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 78.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x \cdot x - y \cdot \left(y \cdot 4\right)}{y \cdot \left(y \cdot 4\right) + x \cdot x} \leq -1 \cdot 10^{-313}:\\ \;\;\;\;-1\\ \mathbf{elif}\;\frac{x \cdot x - y \cdot \left(y \cdot 4\right)}{y \cdot \left(y \cdot 4\right) + x \cdot x} \leq \infty:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 50.8% accurate, 48.0× speedup

Math

\[\begin{array}{l} \\ -1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 -1.0)

C

double code(double x, double y) {
	return -1.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = -1.0d0
end function

Java

public static double code(double x, double y) {
	return -1.0;
}

Python

def code(x, y):
	return -1.0

Julia

function code(x, y)
	return -1.0
end

MATLAB

function tmp = code(x, y)
	tmp = -1.0;
end

Wolfram

code[x_, y_] := -1.0

Derivation

1. Initial program 53.9%

   \[\frac{x \cdot x - \left(y \cdot 4\right) \cdot y}{x \cdot x + \left(y \cdot 4\right) \cdot y} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{-1} \]
4. Step-by-step derivation

5. Simplified 52.6%

   \[\leadsto \color{blue}{-1} \]
6. Add Preprocessing

Developer Target 1: 50.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(y \cdot y\right) \cdot 4\\ t_1 := x \cdot x + t\_0\\ t_2 := \frac{t\_0}{t\_1}\\ t_3 := \left(y \cdot 4\right) \cdot y\\ \mathbf{if}\;\frac{x \cdot x - t\_3}{x \cdot x + t\_3} < 0.9743233849626781:\\ \;\;\;\;\frac{x \cdot x}{t\_1} - t\_2\\ \mathbf{else}:\\ \;\;\;\;{\left(\frac{x}{\sqrt{t\_1}}\right)}^{2} - t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (* y y) 4.0))
        (t_1 (+ (* x x) t_0))
        (t_2 (/ t_0 t_1))
        (t_3 (* (* y 4.0) y)))
   (if (< (/ (- (* x x) t_3) (+ (* x x) t_3)) 0.9743233849626781)
     (- (/ (* x x) t_1) t_2)
     (- (pow (/ x (sqrt t_1)) 2.0) t_2))))

C

double code(double x, double y) {
	double t_0 = (y * y) * 4.0;
	double t_1 = (x * x) + t_0;
	double t_2 = t_0 / t_1;
	double t_3 = (y * 4.0) * y;
	double tmp;
	if ((((x * x) - t_3) / ((x * x) + t_3)) < 0.9743233849626781) {
		tmp = ((x * x) / t_1) - t_2;
	} else {
		tmp = pow((x / sqrt(t_1)), 2.0) - t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_0 = (y * y) * 4.0d0
    t_1 = (x * x) + t_0
    t_2 = t_0 / t_1
    t_3 = (y * 4.0d0) * y
    if ((((x * x) - t_3) / ((x * x) + t_3)) < 0.9743233849626781d0) then
        tmp = ((x * x) / t_1) - t_2
    else
        tmp = ((x / sqrt(t_1)) ** 2.0d0) - t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (y * y) * 4.0;
	double t_1 = (x * x) + t_0;
	double t_2 = t_0 / t_1;
	double t_3 = (y * 4.0) * y;
	double tmp;
	if ((((x * x) - t_3) / ((x * x) + t_3)) < 0.9743233849626781) {
		tmp = ((x * x) / t_1) - t_2;
	} else {
		tmp = Math.pow((x / Math.sqrt(t_1)), 2.0) - t_2;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (y * y) * 4.0
	t_1 = (x * x) + t_0
	t_2 = t_0 / t_1
	t_3 = (y * 4.0) * y
	tmp = 0
	if (((x * x) - t_3) / ((x * x) + t_3)) < 0.9743233849626781:
		tmp = ((x * x) / t_1) - t_2
	else:
		tmp = math.pow((x / math.sqrt(t_1)), 2.0) - t_2
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(y * y) * 4.0)
	t_1 = Float64(Float64(x * x) + t_0)
	t_2 = Float64(t_0 / t_1)
	t_3 = Float64(Float64(y * 4.0) * y)
	tmp = 0.0
	if (Float64(Float64(Float64(x * x) - t_3) / Float64(Float64(x * x) + t_3)) < 0.9743233849626781)
		tmp = Float64(Float64(Float64(x * x) / t_1) - t_2);
	else
		tmp = Float64((Float64(x / sqrt(t_1)) ^ 2.0) - t_2);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (y * y) * 4.0;
	t_1 = (x * x) + t_0;
	t_2 = t_0 / t_1;
	t_3 = (y * 4.0) * y;
	tmp = 0.0;
	if ((((x * x) - t_3) / ((x * x) + t_3)) < 0.9743233849626781)
		tmp = ((x * x) / t_1) - t_2;
	else
		tmp = ((x / sqrt(t_1)) ^ 2.0) - t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(y * y), $MachinePrecision] * 4.0), $MachinePrecision]}, Block[{t$95$1 = N[(N[(x * x), $MachinePrecision] + t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$0 / t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[(N[(y * 4.0), $MachinePrecision] * y), $MachinePrecision]}, If[Less[N[(N[(N[(x * x), $MachinePrecision] - t$95$3), $MachinePrecision] / N[(N[(x * x), $MachinePrecision] + t$95$3), $MachinePrecision]), $MachinePrecision], 0.9743233849626781], N[(N[(N[(x * x), $MachinePrecision] / t$95$1), $MachinePrecision] - t$95$2), $MachinePrecision], N[(N[Power[N[(x / N[Sqrt[t$95$1], $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] - t$95$2), $MachinePrecision]]]]]]

Reproduce

herbie shell --seed 2024207 
(FPCore (x y)
  :name "Diagrams.TwoD.Arc:arcBetween from diagrams-lib-1.3.0.3"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< (/ (- (* x x) (* (* y 4) y)) (+ (* x x) (* (* y 4) y))) 9743233849626781/10000000000000000) (- (/ (* x x) (+ (* x x) (* (* y y) 4))) (/ (* (* y y) 4) (+ (* x x) (* (* y y) 4)))) (- (pow (/ x (sqrt (+ (* x x) (* (* y y) 4)))) 2) (/ (* (* y y) 4) (+ (* x x) (* (* y y) 4))))))

  (/ (- (* x x) (* (* y 4.0) y)) (+ (* x x) (* (* y 4.0) y))))