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

Percentage Accurate: 77.5% → 99.5%

Time: 9.6s

Alternatives: 9

Speedup: 7.2×

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: 77.5% 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: 99.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.14:\\ \;\;\;\;\frac{--1}{x} \cdot e^{-y}\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{e^{y} \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.14)
   (* (/ (- -1.0) x) (exp (- y)))
   (if (<= x 0.77) (/ 1.0 x) (/ 1.0 (* (exp y) x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.14) {
		tmp = (-(-1.0) / x) * exp(-y);
	} else if (x <= 0.77) {
		tmp = 1.0 / x;
	} else {
		tmp = 1.0 / (exp(y) * x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-0.14d0)) then
        tmp = (-(-1.0d0) / x) * exp(-y)
    else if (x <= 0.77d0) then
        tmp = 1.0d0 / x
    else
        tmp = 1.0d0 / (exp(y) * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -0.14) {
		tmp = (-(-1.0) / x) * Math.exp(-y);
	} else if (x <= 0.77) {
		tmp = 1.0 / x;
	} else {
		tmp = 1.0 / (Math.exp(y) * x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -0.14:
		tmp = (-(-1.0) / x) * math.exp(-y)
	elif x <= 0.77:
		tmp = 1.0 / x
	else:
		tmp = 1.0 / (math.exp(y) * x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.14)
		tmp = Float64(Float64(Float64(-(-1.0)) / x) * exp(Float64(-y)));
	elseif (x <= 0.77)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(1.0 / Float64(exp(y) * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -0.14)
		tmp = (-(-1.0) / x) * exp(-y);
	elseif (x <= 0.77)
		tmp = 1.0 / x;
	else
		tmp = 1.0 / (exp(y) * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.14], N[(N[((--1.0) / x), $MachinePrecision] * N[Exp[(-y)], $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.77], N[(1.0 / x), $MachinePrecision], N[(1.0 / N[(N[Exp[y], $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.14000000000000001

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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 99.4

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

   5. Applied rewrites 99.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. div-inv N/A

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

      4. metadata-eval N/A

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

      5. frac-2neg N/A

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

      6. lift-/.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lower-neg.f64 99.4

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

   7. Applied rewrites 99.4%

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

   if -0.14000000000000001 < x < 0.77000000000000002

   1. Initial program 83.7%

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

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 98.9%

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

   if 0.77000000000000002 < x

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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. clear-num N/A

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

      3. frac-2neg N/A

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

      4. metadata-eval N/A

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

      5. lower-/.f64 N/A

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

      6. div-inv N/A

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

      7. distribute-lft-neg-in N/A

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

      8. lower-*.f64 N/A

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

      9. lower-neg.f64 N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lift-log.f64 N/A

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

      14. exp-to-pow N/A

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

      15. pow-flip N/A

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

      16. neg-mul-1 N/A

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

      17. pow-unpow N/A

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

   4. Applied rewrites 72.8%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lower-exp.f64 100.0

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

   7. Applied rewrites 100.0%

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

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.14:\\ \;\;\;\;\frac{--1}{x} \cdot e^{-y}\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{e^{y} \cdot x}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.14:\\ \;\;\;\;\frac{e^{-y}}{x}\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{e^{y} \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.14)
   (/ (exp (- y)) x)
   (if (<= x 0.77) (/ 1.0 x) (/ 1.0 (* (exp y) x)))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.14) {
		tmp = exp(-y) / x;
	} else if (x <= 0.77) {
		tmp = 1.0 / x;
	} else {
		tmp = 1.0 / (exp(y) * x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-0.14d0)) then
        tmp = exp(-y) / x
    else if (x <= 0.77d0) then
        tmp = 1.0d0 / x
    else
        tmp = 1.0d0 / (exp(y) * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -0.14) {
		tmp = Math.exp(-y) / x;
	} else if (x <= 0.77) {
		tmp = 1.0 / x;
	} else {
		tmp = 1.0 / (Math.exp(y) * x);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -0.14:
		tmp = math.exp(-y) / x
	elif x <= 0.77:
		tmp = 1.0 / x
	else:
		tmp = 1.0 / (math.exp(y) * x)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.14)
		tmp = Float64(exp(Float64(-y)) / x);
	elseif (x <= 0.77)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(1.0 / Float64(exp(y) * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -0.14)
		tmp = exp(-y) / x;
	elseif (x <= 0.77)
		tmp = 1.0 / x;
	else
		tmp = 1.0 / (exp(y) * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.14], N[(N[Exp[(-y)], $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 0.77], N[(1.0 / x), $MachinePrecision], N[(1.0 / N[(N[Exp[y], $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.14000000000000001

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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 99.4

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

   5. Applied rewrites 99.4%

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

   if -0.14000000000000001 < x < 0.77000000000000002

   1. Initial program 83.7%

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

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 98.9%

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

   if 0.77000000000000002 < x

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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. clear-num N/A

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

      3. frac-2neg N/A

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

      4. metadata-eval N/A

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

      5. lower-/.f64 N/A

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

      6. div-inv N/A

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

      7. distribute-lft-neg-in N/A

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

      8. lower-*.f64 N/A

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

      9. lower-neg.f64 N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lift-log.f64 N/A

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

      14. exp-to-pow N/A

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

      15. pow-flip N/A

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

      16. neg-mul-1 N/A

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

      17. pow-unpow N/A

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

   4. Applied rewrites 72.8%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lower-exp.f64 100.0

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

   7. Applied rewrites 100.0%

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

Alternative 3: 99.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{e^{-y}}{x}\\ \mathbf{if}\;x \leq -0.14:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (exp (- y)) x)))
   (if (<= x -0.14) t_0 (if (<= x 0.77) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = exp(-y) / x;
	double tmp;
	if (x <= -0.14) {
		tmp = t_0;
	} else if (x <= 0.77) {
		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 <= (-0.14d0)) then
        tmp = t_0
    else if (x <= 0.77d0) 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 <= -0.14) {
		tmp = t_0;
	} else if (x <= 0.77) {
		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 <= -0.14:
		tmp = t_0
	elif x <= 0.77:
		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 <= -0.14)
		tmp = t_0;
	elseif (x <= 0.77)
		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 <= -0.14)
		tmp = t_0;
	elseif (x <= 0.77)
		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, -0.14], t$95$0, If[LessEqual[x, 0.77], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.14000000000000001 or 0.77000000000000002 < x

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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 99.7

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

   5. Applied rewrites 99.7%

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

   if -0.14000000000000001 < x < 0.77000000000000002

   1. Initial program 83.7%

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

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 98.9%

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

Alternative 4: 84.7% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{--1}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\left(0.16666666666666666 + \frac{0.3333333333333333}{x \cdot x}\right) - \frac{0.5}{x}, y, 0.5 - \frac{0.5}{x}\right), y, 1\right), y, 1\right) \cdot x}\\ \mathbf{if}\;x \leq -0.27:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0
         (/
          (- -1.0)
          (*
           (fma
            (fma
             (fma
              (-
               (+ 0.16666666666666666 (/ 0.3333333333333333 (* x x)))
               (/ 0.5 x))
              y
              (- 0.5 (/ 0.5 x)))
             y
             1.0)
            y
            1.0)
           x))))
   (if (<= x -0.27) t_0 (if (<= x 0.77) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = -(-1.0) / (fma(fma(fma(((0.16666666666666666 + (0.3333333333333333 / (x * x))) - (0.5 / x)), y, (0.5 - (0.5 / x))), y, 1.0), y, 1.0) * x);
	double tmp;
	if (x <= -0.27) {
		tmp = t_0;
	} else if (x <= 0.77) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(Float64(-(-1.0)) / Float64(fma(fma(fma(Float64(Float64(0.16666666666666666 + Float64(0.3333333333333333 / Float64(x * x))) - Float64(0.5 / x)), y, Float64(0.5 - Float64(0.5 / x))), y, 1.0), y, 1.0) * x))
	tmp = 0.0
	if (x <= -0.27)
		tmp = t_0;
	elseif (x <= 0.77)
		tmp = Float64(1.0 / x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[((--1.0) / N[(N[(N[(N[(N[(N[(0.16666666666666666 + N[(0.3333333333333333 / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(0.5 / x), $MachinePrecision]), $MachinePrecision] * y + N[(0.5 - N[(0.5 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y + 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -0.27], t$95$0, If[LessEqual[x, 0.77], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.27000000000000002 or 0.77000000000000002 < x

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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. clear-num N/A

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

      3. frac-2neg N/A

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

      4. metadata-eval N/A

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

      5. lower-/.f64 N/A

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

      6. div-inv N/A

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

      7. distribute-lft-neg-in N/A

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

      8. lower-*.f64 N/A

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

      9. lower-neg.f64 N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lift-log.f64 N/A

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

      14. exp-to-pow N/A

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

      15. pow-flip N/A

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

      16. neg-mul-1 N/A

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

      17. pow-unpow N/A

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

   4. Applied rewrites 72.8%

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

   5. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

   7. Applied rewrites 80.9%

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

   if -0.27000000000000002 < x < 0.77000000000000002

   1. Initial program 83.7%

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

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 98.9%

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

4. Final simplification 88.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.27:\\ \;\;\;\;\frac{--1}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\left(0.16666666666666666 + \frac{0.3333333333333333}{x \cdot x}\right) - \frac{0.5}{x}, y, 0.5 - \frac{0.5}{x}\right), y, 1\right), y, 1\right) \cdot x}\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{--1}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\left(0.16666666666666666 + \frac{0.3333333333333333}{x \cdot x}\right) - \frac{0.5}{x}, y, 0.5 - \frac{0.5}{x}\right), y, 1\right), y, 1\right) \cdot x}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 85.6% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5 - \frac{0.5}{x}, y, 1\right), y, 1\right) \cdot \left(-x\right)}\\ \mathbf{if}\;x \leq -1.08 \cdot 10^{+156}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq -0.095:\\ \;\;\;\;\frac{\frac{1 - y \cdot y}{x \cdot x}}{\frac{1 + y}{x}}\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ -1.0 (* (fma (fma (- 0.5 (/ 0.5 x)) y 1.0) y 1.0) (- x)))))
   (if (<= x -1.08e+156)
     t_0
     (if (<= x -0.095)
       (/ (/ (- 1.0 (* y y)) (* x x)) (/ (+ 1.0 y) x))
       (if (<= x 0.77) (/ 1.0 x) t_0)))))

C

double code(double x, double y) {
	double t_0 = -1.0 / (fma(fma((0.5 - (0.5 / x)), y, 1.0), y, 1.0) * -x);
	double tmp;
	if (x <= -1.08e+156) {
		tmp = t_0;
	} else if (x <= -0.095) {
		tmp = ((1.0 - (y * y)) / (x * x)) / ((1.0 + y) / x);
	} else if (x <= 0.77) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(-1.0 / Float64(fma(fma(Float64(0.5 - Float64(0.5 / x)), y, 1.0), y, 1.0) * Float64(-x)))
	tmp = 0.0
	if (x <= -1.08e+156)
		tmp = t_0;
	elseif (x <= -0.095)
		tmp = Float64(Float64(Float64(1.0 - Float64(y * y)) / Float64(x * x)) / Float64(Float64(1.0 + y) / x));
	elseif (x <= 0.77)
		tmp = Float64(1.0 / x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(-1.0 / N[(N[(N[(N[(0.5 - N[(0.5 / x), $MachinePrecision]), $MachinePrecision] * y + 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] * (-x)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.08e+156], t$95$0, If[LessEqual[x, -0.095], N[(N[(N[(1.0 - N[(y * y), $MachinePrecision]), $MachinePrecision] / N[(x * x), $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 + y), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.77], N[(1.0 / x), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.07999999999999997e156 or 0.77000000000000002 < x

   1. Initial program 68.9%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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. clear-num N/A

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

      3. frac-2neg N/A

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

      4. metadata-eval N/A

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

      5. lower-/.f64 N/A

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

      6. div-inv N/A

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

      7. distribute-lft-neg-in N/A

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

      8. lower-*.f64 N/A

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

      9. lower-neg.f64 N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lift-log.f64 N/A

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

      14. exp-to-pow N/A

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

      15. pow-flip N/A

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

      16. neg-mul-1 N/A

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

      17. pow-unpow N/A

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

   4. Applied rewrites 68.9%

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

   5. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. *-commutative N/A

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

      6. lower-fma.f64 N/A

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

      7. lower--.f64 N/A

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

      8. associate-*r/ N/A

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

      9. metadata-eval N/A

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

      10. lower-/.f64 79.8

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

   7. Applied rewrites 79.8%

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

   if -1.07999999999999997e156 < x < -0.095000000000000001

   1. Initial program 85.7%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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 72.5

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

   5. Applied rewrites 72.5%

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

   7. Applied rewrites 86.5%

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

   if -0.095000000000000001 < x < 0.77000000000000002

   1. Initial program 83.7%

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

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 98.9%

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

4. Final simplification 89.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.08 \cdot 10^{+156}:\\ \;\;\;\;\frac{-1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5 - \frac{0.5}{x}, y, 1\right), y, 1\right) \cdot \left(-x\right)}\\ \mathbf{elif}\;x \leq -0.095:\\ \;\;\;\;\frac{\frac{1 - y \cdot y}{x \cdot x}}{\frac{1 + y}{x}}\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5 - \frac{0.5}{x}, y, 1\right), y, 1\right) \cdot \left(-x\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 83.7% accurate, 4.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5 - \frac{0.5}{x}, y, 1\right), y, 1\right) \cdot \left(-x\right)}\\ \mathbf{if}\;x \leq -0.095:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ -1.0 (* (fma (fma (- 0.5 (/ 0.5 x)) y 1.0) y 1.0) (- x)))))
   (if (<= x -0.095) t_0 (if (<= x 0.77) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = -1.0 / (fma(fma((0.5 - (0.5 / x)), y, 1.0), y, 1.0) * -x);
	double tmp;
	if (x <= -0.095) {
		tmp = t_0;
	} else if (x <= 0.77) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(-1.0 / Float64(fma(fma(Float64(0.5 - Float64(0.5 / x)), y, 1.0), y, 1.0) * Float64(-x)))
	tmp = 0.0
	if (x <= -0.095)
		tmp = t_0;
	elseif (x <= 0.77)
		tmp = Float64(1.0 / x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(-1.0 / N[(N[(N[(N[(0.5 - N[(0.5 / x), $MachinePrecision]), $MachinePrecision] * y + 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] * (-x)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -0.095], t$95$0, If[LessEqual[x, 0.77], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.095000000000000001 or 0.77000000000000002 < x

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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. clear-num N/A

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

      3. frac-2neg N/A

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

      4. metadata-eval N/A

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

      5. lower-/.f64 N/A

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

      6. div-inv N/A

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

      7. distribute-lft-neg-in N/A

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

      8. lower-*.f64 N/A

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

      9. lower-neg.f64 N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lift-log.f64 N/A

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

      14. exp-to-pow N/A

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

      15. pow-flip N/A

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

      16. neg-mul-1 N/A

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

      17. pow-unpow N/A

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

   4. Applied rewrites 72.8%

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

   5. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. +-commutative N/A

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

      5. *-commutative N/A

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

      6. lower-fma.f64 N/A

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

      7. lower--.f64 N/A

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

      8. associate-*r/ N/A

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

      9. metadata-eval N/A

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

      10. lower-/.f64 78.3

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

   7. Applied rewrites 78.3%

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

   if -0.095000000000000001 < x < 0.77000000000000002

   1. Initial program 83.7%

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

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 98.9%

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

4. Final simplification 87.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -0.095:\\ \;\;\;\;\frac{-1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5 - \frac{0.5}{x}, y, 1\right), y, 1\right) \cdot \left(-x\right)}\\ \mathbf{elif}\;x \leq 0.77:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5 - \frac{0.5}{x}, y, 1\right), y, 1\right) \cdot \left(-x\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 84.2% accurate, 6.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -0.14:\\ \;\;\;\;\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, y, 0.5\right), y, -1\right), y, 1\right)}{x}\\ \mathbf{elif}\;x \leq 0.29:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{-\mathsf{fma}\left(y, x, x\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -0.14)
   (/ (fma (fma (fma -0.16666666666666666 y 0.5) y -1.0) y 1.0) x)
   (if (<= x 0.29) (/ 1.0 x) (/ -1.0 (- (fma y x x))))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -0.14) {
		tmp = fma(fma(fma(-0.16666666666666666, y, 0.5), y, -1.0), y, 1.0) / x;
	} else if (x <= 0.29) {
		tmp = 1.0 / x;
	} else {
		tmp = -1.0 / -fma(y, x, x);
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -0.14)
		tmp = Float64(fma(fma(fma(-0.16666666666666666, y, 0.5), y, -1.0), y, 1.0) / x);
	elseif (x <= 0.29)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(-1.0 / Float64(-fma(y, x, x)));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, -0.14], N[(N[(N[(N[(-0.16666666666666666 * y + 0.5), $MachinePrecision] * y + -1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 0.29], N[(1.0 / x), $MachinePrecision], N[(-1.0 / (-N[(y * x + x), $MachinePrecision])), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.14000000000000001

   1. Initial program 72.8%

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

      1. lift-exp.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-log.f64 N/A

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

      5. exp-to-pow N/A

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

      6. lower-pow.f64 72.8

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

      7. lift-+.f64 N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 72.8

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

   4. Applied rewrites 72.8%

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

   5. Taylor expanded in y around 0

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

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

   7. Applied rewrites 76.8%

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

   8. Taylor expanded in x around inf

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{2} + \frac{-1}{6} \cdot y, y, -1\right), y, 1\right)}{x} \]
   9. Step-by-step derivation

   10. Applied rewrites 76.4%

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

   if -0.14000000000000001 < x < 0.28999999999999998

   1. Initial program 83.7%

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

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 98.9%

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

   if 0.28999999999999998 < x

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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. clear-num N/A

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

      3. frac-2neg N/A

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

      4. metadata-eval N/A

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

      5. lower-/.f64 N/A

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

      6. div-inv N/A

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

      7. distribute-lft-neg-in N/A

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

      8. lower-*.f64 N/A

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

      9. lower-neg.f64 N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lift-log.f64 N/A

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

      14. exp-to-pow N/A

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

      15. pow-flip N/A

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

      16. neg-mul-1 N/A

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

      17. pow-unpow N/A

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

   4. Applied rewrites 72.8%

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

   5. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. mul-1-neg N/A

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

      3. mul-1-neg N/A

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

      4. distribute-neg-out N/A

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

      5. lower-neg.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-fma.f64 72.7

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

   7. Applied rewrites 72.7%

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

Alternative 8: 80.8% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{-1}{-\mathsf{fma}\left(y, x, x\right)}\\ \mathbf{if}\;x \leq -0.14:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 0.29:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ -1.0 (- (fma y x x)))))
   (if (<= x -0.14) t_0 (if (<= x 0.29) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = -1.0 / -fma(y, x, x);
	double tmp;
	if (x <= -0.14) {
		tmp = t_0;
	} else if (x <= 0.29) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(-1.0 / Float64(-fma(y, x, x)))
	tmp = 0.0
	if (x <= -0.14)
		tmp = t_0;
	elseif (x <= 0.29)
		tmp = Float64(1.0 / x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(-1.0 / (-N[(y * x + x), $MachinePrecision])), $MachinePrecision]}, If[LessEqual[x, -0.14], t$95$0, If[LessEqual[x, 0.29], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.14000000000000001 or 0.28999999999999998 < x

   1. Initial program 72.8%

      \[\frac{e^{x \cdot \log \left(\frac{x}{x + y}\right)}}{x} \]
   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. clear-num N/A

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

      3. frac-2neg N/A

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

      4. metadata-eval N/A

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

      5. lower-/.f64 N/A

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

      6. div-inv N/A

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

      7. distribute-lft-neg-in N/A

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

      8. lower-*.f64 N/A

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

      9. lower-neg.f64 N/A

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

      10. lift-exp.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lift-log.f64 N/A

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

      14. exp-to-pow N/A

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

      15. pow-flip N/A

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

      16. neg-mul-1 N/A

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

      17. pow-unpow N/A

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

   4. Applied rewrites 72.8%

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

   5. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. mul-1-neg N/A

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

      3. mul-1-neg N/A

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

      4. distribute-neg-out N/A

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

      5. lower-neg.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-fma.f64 73.6

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

   7. Applied rewrites 73.6%

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

   if -0.14000000000000001 < x < 0.28999999999999998

   1. Initial program 83.7%

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

   3. Taylor expanded in y around 0

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

   5. Applied rewrites 98.9%

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

Alternative 9: 74.5% 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 77.6%

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

3. Taylor expanded in y around 0

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

5. Applied rewrites 77.2%

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

Developer Target 1: 77.4% 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 2024277 
(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))