Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, F

Percentage Accurate: 78.1% → 98.8%

Time: 8.8s

Alternatives: 7

Speedup: 5.4×

Specification

Math

\[\begin{array}{l} \\ \frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (exp (* x (log (/ x (+ x y))))) x))

C

double code(double x, double y) {
	return exp((x * log((x / (x + y))))) / x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = exp((x * log((x / (x + y))))) / x
end function

Java

public static double code(double x, double y) {
	return Math.exp((x * Math.log((x / (x + y))))) / x;
}

Python

def code(x, y):
	return math.exp((x * math.log((x / (x + y))))) / x

Julia

function code(x, y)
	return Float64(exp(Float64(x * log(Float64(x / Float64(x + y))))) / x)
end

MATLAB

function tmp = code(x, y)
	tmp = exp((x * log((x / (x + y))))) / x;
end

Wolfram

code[x_, y_] := N[(N[Exp[N[(x * N[Log[N[(x / N[(x + y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / x), $MachinePrecision]

Initial Program: 78.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (exp (* x (log (/ x (+ x y))))) x))

C

double code(double x, double y) {
	return exp((x * log((x / (x + y))))) / x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = exp((x * log((x / (x + y))))) / x
end function

Java

public static double code(double x, double y) {
	return Math.exp((x * Math.log((x / (x + y))))) / x;
}

Python

def code(x, y):
	return math.exp((x * math.log((x / (x + y))))) / x

Julia

function code(x, y)
	return Float64(exp(Float64(x * log(Float64(x / Float64(x + y))))) / x)
end

MATLAB

function tmp = code(x, y)
	tmp = exp((x * log((x / (x + y))))) / x;
end

Wolfram

code[x_, y_] := N[(N[Exp[N[(x * N[Log[N[(x / N[(x + y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / x), $MachinePrecision]

Alternative 1: 98.8% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{e^{-y}}{x}\\ \mathbf{if}\;x \leq -1.58:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 1.8 \cdot 10^{-38}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (exp (- y)) x)))
   (if (<= x -1.58) t_0 (if (<= x 1.8e-38) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = exp(-y) / x;
	double tmp;
	if (x <= -1.58) {
		tmp = t_0;
	} else if (x <= 1.8e-38) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	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) :: tmp
    t_0 = exp(-y) / x
    if (x <= (-1.58d0)) then
        tmp = t_0
    else if (x <= 1.8d-38) then
        tmp = 1.0d0 / x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = Math.exp(-y) / x;
	double tmp;
	if (x <= -1.58) {
		tmp = t_0;
	} else if (x <= 1.8e-38) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = math.exp(-y) / x
	tmp = 0
	if x <= -1.58:
		tmp = t_0
	elif x <= 1.8e-38:
		tmp = 1.0 / x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(exp(Float64(-y)) / x)
	tmp = 0.0
	if (x <= -1.58)
		tmp = t_0;
	elseif (x <= 1.8e-38)
		tmp = Float64(1.0 / x);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = exp(-y) / x;
	tmp = 0.0;
	if (x <= -1.58)
		tmp = t_0;
	elseif (x <= 1.8e-38)
		tmp = 1.0 / x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Exp[(-y)], $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[x, -1.58], t$95$0, If[LessEqual[x, 1.8e-38], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.5800000000000001 or 1.8e-38 < x

   1. Initial program 68.2%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. mul-1-neg N/A

         \[\leadsto \frac{e^{\color{blue}{\mathsf{neg}\left(y\right)}}}{x} \]

      2. lower-neg.f64 100.0

         \[\leadsto \frac{e^{\color{blue}{-y}}}{x} \]

   5. Applied rewrites 100.0%

      \[\leadsto \frac{e^{\color{blue}{-y}}}{x} \]

   if -1.5800000000000001 < x < 1.8e-38

   1. Initial program 78.0%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 100.0%

      \[\leadsto \frac{\color{blue}{1}}{x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 83.0% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.95:\\ \;\;\;\;\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{0.3333333333333333}{x \cdot x} + \left(\frac{0.5}{x} + 0.16666666666666666\right), -y, \frac{0.5}{x} + 0.5\right), y, -1\right), y, 1\right)}{x}\\ \mathbf{elif}\;x \leq 2.55 \cdot 10^{+73}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.5, y, -1\right), y, 1\right), x, \left(y \cdot y\right) \cdot 0.5\right)}{x}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -1.95)
   (/
    (fma
     (fma
      (fma
       (+ (/ 0.3333333333333333 (* x x)) (+ (/ 0.5 x) 0.16666666666666666))
       (- y)
       (+ (/ 0.5 x) 0.5))
      y
      -1.0)
     y
     1.0)
    x)
   (if (<= x 2.55e+73)
     (/ 1.0 x)
     (/ (/ (fma (fma (fma 0.5 y -1.0) y 1.0) x (* (* y y) 0.5)) x) x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -1.95) {
		tmp = fma(fma(fma(((0.3333333333333333 / (x * x)) + ((0.5 / x) + 0.16666666666666666)), -y, ((0.5 / x) + 0.5)), y, -1.0), y, 1.0) / x;
	} else if (x <= 2.55e+73) {
		tmp = 1.0 / x;
	} else {
		tmp = (fma(fma(fma(0.5, y, -1.0), y, 1.0), x, ((y * y) * 0.5)) / x) / x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -1.95)
		tmp = Float64(fma(fma(fma(Float64(Float64(0.3333333333333333 / Float64(x * x)) + Float64(Float64(0.5 / x) + 0.16666666666666666)), Float64(-y), Float64(Float64(0.5 / x) + 0.5)), y, -1.0), y, 1.0) / x);
	elseif (x <= 2.55e+73)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(fma(fma(fma(0.5, y, -1.0), y, 1.0), x, Float64(Float64(y * y) * 0.5)) / x) / x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, -1.95], N[(N[(N[(N[(N[(N[(0.3333333333333333 / N[(x * x), $MachinePrecision]), $MachinePrecision] + N[(N[(0.5 / x), $MachinePrecision] + 0.16666666666666666), $MachinePrecision]), $MachinePrecision] * (-y) + N[(N[(0.5 / x), $MachinePrecision] + 0.5), $MachinePrecision]), $MachinePrecision] * y + -1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 2.55e+73], N[(1.0 / x), $MachinePrecision], N[(N[(N[(N[(N[(0.5 * y + -1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] * x + N[(N[(y * y), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.94999999999999996

   1. Initial program 70.2%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \frac{\color{blue}{1 + y \cdot \left(y \cdot \left(\frac{1}{2} + \left(-1 \cdot \left(y \cdot \left(\frac{1}{6} + \left(\frac{1}{3} \cdot \frac{1}{{x}^{2}} + \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) + \frac{1}{2} \cdot \frac{1}{x}\right)\right) - 1\right)}}{x} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{\color{blue}{y \cdot \left(y \cdot \left(\frac{1}{2} + \left(-1 \cdot \left(y \cdot \left(\frac{1}{6} + \left(\frac{1}{3} \cdot \frac{1}{{x}^{2}} + \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) + \frac{1}{2} \cdot \frac{1}{x}\right)\right) - 1\right) + 1}}{x} \]

      2. *-commutative N/A

         \[\leadsto \frac{\color{blue}{\left(y \cdot \left(\frac{1}{2} + \left(-1 \cdot \left(y \cdot \left(\frac{1}{6} + \left(\frac{1}{3} \cdot \frac{1}{{x}^{2}} + \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) + \frac{1}{2} \cdot \frac{1}{x}\right)\right) - 1\right) \cdot y} + 1}{x} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(y \cdot \left(\frac{1}{2} + \left(-1 \cdot \left(y \cdot \left(\frac{1}{6} + \left(\frac{1}{3} \cdot \frac{1}{{x}^{2}} + \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) + \frac{1}{2} \cdot \frac{1}{x}\right)\right) - 1, y, 1\right)}}{x} \]

   5. Applied rewrites 73.8%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\left(0.16666666666666666 + \frac{0.5}{x}\right) + \frac{0.3333333333333333}{x \cdot x}, -y, \frac{0.5}{x} + 0.5\right), y, -1\right), y, 1\right)}}{x} \]

   if -1.94999999999999996 < x < 2.55000000000000012e73

   1. Initial program 81.1%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 96.3%

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

   if 2.55000000000000012e73 < x

   1. Initial program 52.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. lower-fma.f64 N/A

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

   5. Applied rewrites 47.7%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 64.4%

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

4. Final simplification 83.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.95:\\ \;\;\;\;\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{0.3333333333333333}{x \cdot x} + \left(\frac{0.5}{x} + 0.16666666666666666\right), -y, \frac{0.5}{x} + 0.5\right), y, -1\right), y, 1\right)}{x}\\ \mathbf{elif}\;x \leq 2.55 \cdot 10^{+73}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.5, y, -1\right), y, 1\right), x, \left(y \cdot y\right) \cdot 0.5\right)}{x}}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 81.6% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.41:\\ \;\;\;\;\frac{\frac{x - y \cdot x}{x}}{x}\\ \mathbf{elif}\;x \leq 2.55 \cdot 10^{+73}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.5, y, -1\right), y, 1\right), x, \left(y \cdot y\right) \cdot 0.5\right)}{x}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.41)
   (/ (/ (- x (* y x)) x) x)
   (if (<= x 2.55e+73)
     (/ 1.0 x)
     (/ (/ (fma (fma (fma 0.5 y -1.0) y 1.0) x (* (* y y) 0.5)) x) x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.41) {
		tmp = ((x - (y * x)) / x) / x;
	} else if (x <= 2.55e+73) {
		tmp = 1.0 / x;
	} else {
		tmp = (fma(fma(fma(0.5, y, -1.0), y, 1.0), x, ((y * y) * 0.5)) / x) / x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.41)
		tmp = Float64(Float64(Float64(x - Float64(y * x)) / x) / x);
	elseif (x <= 2.55e+73)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(fma(fma(fma(0.5, y, -1.0), y, 1.0), x, Float64(Float64(y * y) * 0.5)) / x) / x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.41], N[(N[(N[(x - N[(y * x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 2.55e+73], N[(1.0 / x), $MachinePrecision], N[(N[(N[(N[(N[(0.5 * y + -1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] * x + N[(N[(y * y), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.409999999999999976

   1. Initial program 70.2%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. mul-1-neg N/A

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

      3. unsub-neg N/A

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

      4. lower--.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-/.f64 59.6

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

   5. Applied rewrites 59.6%

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

   7. Applied rewrites 71.2%

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

   if -0.409999999999999976 < x < 2.55000000000000012e73

   1. Initial program 81.1%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 96.3%

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

   if 2.55000000000000012e73 < x

   1. Initial program 52.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. lower-fma.f64 N/A

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

   5. Applied rewrites 47.7%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 64.4%

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

Alternative 4: 79.3% accurate, 5.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4 \cdot 10^{+74}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(0.5, x, 0.5\right) \cdot \left(y \cdot y\right)}{x}}{x}\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+47}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 \cdot x}{x \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -4e+74)
   (/ (/ (* (fma 0.5 x 0.5) (* y y)) x) x)
   (if (<= y 1.55e+47) (/ 1.0 x) (/ (* 1.0 x) (* x x)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -4e+74) {
		tmp = ((fma(0.5, x, 0.5) * (y * y)) / x) / x;
	} else if (y <= 1.55e+47) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 * x) / (x * x);
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -4e+74)
		tmp = Float64(Float64(Float64(fma(0.5, x, 0.5) * Float64(y * y)) / x) / x);
	elseif (y <= 1.55e+47)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(1.0 * x) / Float64(x * x));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, -4e+74], N[(N[(N[(N[(0.5 * x + 0.5), $MachinePrecision] * N[(y * y), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[y, 1.55e+47], N[(1.0 / x), $MachinePrecision], N[(N[(1.0 * x), $MachinePrecision] / N[(x * x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -3.99999999999999981e74

   1. Initial program 35.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. lower-fma.f64 N/A

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

   5. Applied rewrites 10.1%

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

   6. Taylor expanded in x around 0

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

   8. Applied rewrites 41.2%

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

   9. Taylor expanded in y around inf

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

   11. Applied rewrites 59.2%

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

   if -3.99999999999999981e74 < y < 1.55e47

   1. Initial program 89.0%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 90.5%

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

   if 1.55e47 < y

   1. Initial program 36.8%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. neg-sub0 N/A

         \[\leadsto \frac{\color{blue}{0 - e^{x \cdot \log \left(\frac{x}{x + y}\right)}}}{\mathsf{neg}\left(x\right)} \]

      4. metadata-eval N/A

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

      5. div-sub N/A

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

      6. frac-sub N/A

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

      7. sqr-neg N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 69.5%

      \[\leadsto \color{blue}{\frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot {\left(\frac{x}{y + x}\right)}^{x}}{x \cdot x}} \]

   5. Taylor expanded in y around 0

      \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \color{blue}{\left(1 + y \cdot \left(y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) - 1\right)\right)}}{x \cdot x} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \color{blue}{\left(y \cdot \left(y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) - 1\right) + 1\right)}}{x \cdot x} \]

      2. *-commutative N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \left(\color{blue}{\left(y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) - 1\right) \cdot y} + 1\right)}{x \cdot x} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \color{blue}{\mathsf{fma}\left(y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) - 1, y, 1\right)}}{x \cdot x} \]

      4. sub-neg N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\color{blue}{y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) + \left(\mathsf{neg}\left(1\right)\right)}, y, 1\right)}{x \cdot x} \]

      5. *-commutative N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\color{blue}{\left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) \cdot y} + \left(\mathsf{neg}\left(1\right)\right), y, 1\right)}{x \cdot x} \]

      6. metadata-eval N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) \cdot y + \color{blue}{-1}, y, 1\right)}{x \cdot x} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}, y, -1\right)}, y, 1\right)}{x \cdot x} \]

      8. +-commutative N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot \frac{1}{x} + \frac{1}{2}}, y, -1\right), y, 1\right)}{x \cdot x} \]

      9. lower-+.f64 N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot \frac{1}{x} + \frac{1}{2}}, y, -1\right), y, 1\right)}{x \cdot x} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{\frac{1}{2} \cdot 1}{x}} + \frac{1}{2}, y, -1\right), y, 1\right)}{x \cdot x} \]

      11. metadata-eval N/A

          \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\frac{\color{blue}{\frac{1}{2}}}{x} + \frac{1}{2}, y, -1\right), y, 1\right)}{x \cdot x} \]

      12. lower-/.f64 4.8

          \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{0.5}{x}} + 0.5, y, -1\right), y, 1\right)}{x \cdot x} \]

   7. Applied rewrites 4.8%

      \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{0.5}{x} + 0.5, y, -1\right), y, 1\right)}}{x \cdot x} \]

   8. Taylor expanded in y around 0

      \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot 1}{x \cdot x} \]
   9. Step-by-step derivation

   10. Applied rewrites 69.3%

       \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot 1}{x \cdot x} \]
   11. Step-by-step derivation

   12. Applied rewrites 69.3%

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

4. Final simplification 82.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4 \cdot 10^{+74}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(0.5, x, 0.5\right) \cdot \left(y \cdot y\right)}{x}}{x}\\ \mathbf{elif}\;y \leq 1.55 \cdot 10^{+47}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 \cdot x}{x \cdot x}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 81.0% accurate, 5.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\frac{x - y \cdot x}{x}}{x}\\ \mathbf{if}\;x \leq -0.41:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 2.55 \cdot 10^{+73}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (/ (- x (* y x)) x) x)))
   (if (<= x -0.41) t_0 (if (<= x 2.55e+73) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = ((x - (y * x)) / x) / x;
	double tmp;
	if (x <= -0.41) {
		tmp = t_0;
	} else if (x <= 2.55e+73) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	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) :: tmp
    t_0 = ((x - (y * x)) / x) / x
    if (x <= (-0.41d0)) then
        tmp = t_0
    else if (x <= 2.55d+73) then
        tmp = 1.0d0 / x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = ((x - (y * x)) / x) / x;
	double tmp;
	if (x <= -0.41) {
		tmp = t_0;
	} else if (x <= 2.55e+73) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = ((x - (y * x)) / x) / x
	tmp = 0
	if x <= -0.41:
		tmp = t_0
	elif x <= 2.55e+73:
		tmp = 1.0 / x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(Float64(x - Float64(y * x)) / x) / x)
	tmp = 0.0
	if (x <= -0.41)
		tmp = t_0;
	elseif (x <= 2.55e+73)
		tmp = Float64(1.0 / x);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = ((x - (y * x)) / x) / x;
	tmp = 0.0;
	if (x <= -0.41)
		tmp = t_0;
	elseif (x <= 2.55e+73)
		tmp = 1.0 / x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(x - N[(y * x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[x, -0.41], t$95$0, If[LessEqual[x, 2.55e+73], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.409999999999999976 or 2.55000000000000012e73 < x

   1. Initial program 63.1%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. mul-1-neg N/A

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

      3. unsub-neg N/A

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

      4. lower--.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-/.f64 54.5

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

   5. Applied rewrites 54.5%

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

   7. Applied rewrites 66.6%

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

   if -0.409999999999999976 < x < 2.55000000000000012e73

   1. Initial program 81.1%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 96.3%

      \[\leadsto \frac{\color{blue}{1}}{x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 77.3% accurate, 8.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.55 \cdot 10^{+47}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 \cdot x}{x \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 1.55e+47) (/ 1.0 x) (/ (* 1.0 x) (* x x))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1.55e+47) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 * x) / (x * x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 1.55d+47) then
        tmp = 1.0d0 / x
    else
        tmp = (1.0d0 * x) / (x * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 1.55e+47) {
		tmp = 1.0 / x;
	} else {
		tmp = (1.0 * x) / (x * x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 1.55e+47:
		tmp = 1.0 / x
	else:
		tmp = (1.0 * x) / (x * x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1.55e+47)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(1.0 * x) / Float64(x * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 1.55e+47)
		tmp = 1.0 / x;
	else
		tmp = (1.0 * x) / (x * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1.55e+47], N[(1.0 / x), $MachinePrecision], N[(N[(1.0 * x), $MachinePrecision] / N[(x * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.55e47

   1. Initial program 79.3%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 81.3%

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

   if 1.55e47 < y

   1. Initial program 36.8%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. neg-sub0 N/A

         \[\leadsto \frac{\color{blue}{0 - e^{x \cdot \log \left(\frac{x}{x + y}\right)}}}{\mathsf{neg}\left(x\right)} \]

      4. metadata-eval N/A

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

      5. div-sub N/A

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

      6. frac-sub N/A

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

      7. sqr-neg N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 69.5%

      \[\leadsto \color{blue}{\frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot {\left(\frac{x}{y + x}\right)}^{x}}{x \cdot x}} \]

   5. Taylor expanded in y around 0

      \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \color{blue}{\left(1 + y \cdot \left(y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) - 1\right)\right)}}{x \cdot x} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \color{blue}{\left(y \cdot \left(y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) - 1\right) + 1\right)}}{x \cdot x} \]

      2. *-commutative N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \left(\color{blue}{\left(y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) - 1\right) \cdot y} + 1\right)}{x \cdot x} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \color{blue}{\mathsf{fma}\left(y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) - 1, y, 1\right)}}{x \cdot x} \]

      4. sub-neg N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\color{blue}{y \cdot \left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) + \left(\mathsf{neg}\left(1\right)\right)}, y, 1\right)}{x \cdot x} \]

      5. *-commutative N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\color{blue}{\left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) \cdot y} + \left(\mathsf{neg}\left(1\right)\right), y, 1\right)}{x \cdot x} \]

      6. metadata-eval N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}\right) \cdot y + \color{blue}{-1}, y, 1\right)}{x \cdot x} \]

      7. lower-fma.f64 N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \frac{1}{x}, y, -1\right)}, y, 1\right)}{x \cdot x} \]

      8. +-commutative N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot \frac{1}{x} + \frac{1}{2}}, y, -1\right), y, 1\right)}{x \cdot x} \]

      9. lower-+.f64 N/A

         \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot \frac{1}{x} + \frac{1}{2}}, y, -1\right), y, 1\right)}{x \cdot x} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{\frac{1}{2} \cdot 1}{x}} + \frac{1}{2}, y, -1\right), y, 1\right)}{x \cdot x} \]

      11. metadata-eval N/A

          \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\frac{\color{blue}{\frac{1}{2}}}{x} + \frac{1}{2}, y, -1\right), y, 1\right)}{x \cdot x} \]

      12. lower-/.f64 4.8

          \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{0.5}{x}} + 0.5, y, -1\right), y, 1\right)}{x \cdot x} \]

   7. Applied rewrites 4.8%

      \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{0.5}{x} + 0.5, y, -1\right), y, 1\right)}}{x \cdot x} \]

   8. Taylor expanded in y around 0

      \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot 1}{x \cdot x} \]
   9. Step-by-step derivation

   10. Applied rewrites 69.3%

       \[\leadsto \frac{0 \cdot \left(-x\right) - \left(-x\right) \cdot 1}{x \cdot x} \]
   11. Step-by-step derivation

   12. Applied rewrites 69.3%

       \[\leadsto \color{blue}{\frac{1 \cdot x}{x \cdot x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 74.8% accurate, 19.3× speedup

Math

\[\begin{array}{l} \\ \frac{1}{x} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return 1.0 / x

Julia

function code(x, y)
	return Float64(1.0 / x)
end

MATLAB

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

Wolfram

code[x_, y_] := N[(1.0 / x), $MachinePrecision]

Derivation

1. Initial program 72.3%

   \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

5. Applied rewrites 75.5%

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

Developer Target 1: 77.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{e^{\frac{-1}{y}}}{x}\\ t_1 := \frac{{\left(\frac{x}{y + x}\right)}^{x}}{x}\\ \mathbf{if}\;y < -3.7311844206647956 \cdot 10^{+94}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y < 2.817959242728288 \cdot 10^{+37}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y < 2.347387415166998 \cdot 10^{+178}:\\ \;\;\;\;\log \left(e^{t\_1}\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (exp (/ -1.0 y)) x)) (t_1 (/ (pow (/ x (+ y x)) x) x)))
   (if (< y -3.7311844206647956e+94)
     t_0
     (if (< y 2.817959242728288e+37)
       t_1
       (if (< y 2.347387415166998e+178) (log (exp t_1)) t_0)))))

C

double code(double x, double y) {
	double t_0 = exp((-1.0 / y)) / x;
	double t_1 = pow((x / (y + x)), x) / x;
	double tmp;
	if (y < -3.7311844206647956e+94) {
		tmp = t_0;
	} else if (y < 2.817959242728288e+37) {
		tmp = t_1;
	} else if (y < 2.347387415166998e+178) {
		tmp = log(exp(t_1));
	} else {
		tmp = t_0;
	}
	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) :: tmp
    t_0 = exp(((-1.0d0) / y)) / x
    t_1 = ((x / (y + x)) ** x) / x
    if (y < (-3.7311844206647956d+94)) then
        tmp = t_0
    else if (y < 2.817959242728288d+37) then
        tmp = t_1
    else if (y < 2.347387415166998d+178) then
        tmp = log(exp(t_1))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = Math.exp((-1.0 / y)) / x;
	double t_1 = Math.pow((x / (y + x)), x) / x;
	double tmp;
	if (y < -3.7311844206647956e+94) {
		tmp = t_0;
	} else if (y < 2.817959242728288e+37) {
		tmp = t_1;
	} else if (y < 2.347387415166998e+178) {
		tmp = Math.log(Math.exp(t_1));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = math.exp((-1.0 / y)) / x
	t_1 = math.pow((x / (y + x)), x) / x
	tmp = 0
	if y < -3.7311844206647956e+94:
		tmp = t_0
	elif y < 2.817959242728288e+37:
		tmp = t_1
	elif y < 2.347387415166998e+178:
		tmp = math.log(math.exp(t_1))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(exp(Float64(-1.0 / y)) / x)
	t_1 = Float64((Float64(x / Float64(y + x)) ^ x) / x)
	tmp = 0.0
	if (y < -3.7311844206647956e+94)
		tmp = t_0;
	elseif (y < 2.817959242728288e+37)
		tmp = t_1;
	elseif (y < 2.347387415166998e+178)
		tmp = log(exp(t_1));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = exp((-1.0 / y)) / x;
	t_1 = ((x / (y + x)) ^ x) / x;
	tmp = 0.0;
	if (y < -3.7311844206647956e+94)
		tmp = t_0;
	elseif (y < 2.817959242728288e+37)
		tmp = t_1;
	elseif (y < 2.347387415166998e+178)
		tmp = log(exp(t_1));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Exp[N[(-1.0 / y), $MachinePrecision]], $MachinePrecision] / x), $MachinePrecision]}, Block[{t$95$1 = N[(N[Power[N[(x / N[(y + x), $MachinePrecision]), $MachinePrecision], x], $MachinePrecision] / x), $MachinePrecision]}, If[Less[y, -3.7311844206647956e+94], t$95$0, If[Less[y, 2.817959242728288e+37], t$95$1, If[Less[y, 2.347387415166998e+178], N[Log[N[Exp[t$95$1], $MachinePrecision]], $MachinePrecision], t$95$0]]]]]

Reproduce

herbie shell --seed 2024254 
(FPCore (x y)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, F"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< y -37311844206647956000000000000000000000000000000000000000000000000000000000000000000000000000000) (/ (exp (/ -1 y)) x) (if (< y 28179592427282880000000000000000000000) (/ (pow (/ x (+ y x)) x) x) (if (< y 23473874151669980000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (log (exp (/ (pow (/ x (+ y x)) x) x))) (/ (exp (/ -1 y)) x)))))

  (/ (exp (* x (log (/ x (+ x y))))) x))