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

Percentage Accurate: 77.7% → 97.4%

Time: 4.3s

Alternatives: 8

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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y)
use fmin_fmax_functions
    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: 77.7% 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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y)
use fmin_fmax_functions
    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: 97.4% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.3 \cdot 10^{+39}:\\ \;\;\;\;\frac{e^{x \cdot \left(\frac{-1}{x} \cdot y\right)}}{x}\\ \mathbf{elif}\;x \leq 5.7 \cdot 10^{-37}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{-y}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -4.3e+39)
   (/ (exp (* x (* (/ -1.0 x) y))) x)
   (if (<= x 5.7e-37) (/ 1.0 x) (/ (exp (- y)) x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -4.3e+39) {
		tmp = exp((x * ((-1.0 / x) * y))) / x;
	} else if (x <= 5.7e-37) {
		tmp = 1.0 / x;
	} else {
		tmp = exp(-y) / x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-4.3d+39)) then
        tmp = exp((x * (((-1.0d0) / x) * y))) / x
    else if (x <= 5.7d-37) then
        tmp = 1.0d0 / x
    else
        tmp = exp(-y) / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -4.3e+39) {
		tmp = Math.exp((x * ((-1.0 / x) * y))) / x;
	} else if (x <= 5.7e-37) {
		tmp = 1.0 / x;
	} else {
		tmp = Math.exp(-y) / x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -4.3e+39:
		tmp = math.exp((x * ((-1.0 / x) * y))) / x
	elif x <= 5.7e-37:
		tmp = 1.0 / x
	else:
		tmp = math.exp(-y) / x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -4.3e+39)
		tmp = Float64(exp(Float64(x * Float64(Float64(-1.0 / x) * y))) / x);
	elseif (x <= 5.7e-37)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(exp(Float64(-y)) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -4.3e+39)
		tmp = exp((x * ((-1.0 / x) * y))) / x;
	elseif (x <= 5.7e-37)
		tmp = 1.0 / x;
	else
		tmp = exp(-y) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -4.3e+39], N[(N[Exp[N[(x * N[(N[(-1.0 / x), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 5.7e-37], N[(1.0 / x), $MachinePrecision], N[(N[Exp[(-y)], $MachinePrecision] / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -4.3e39

   1. Initial program 70.5%

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

   2. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. unpow2 N/A

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

      8. lower-*.f64 N/A

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

      9. lower-/.f64 91.9

         \[\leadsto \frac{e^{x \cdot \left(\left(\frac{y}{x \cdot x} \cdot 0.5 - \frac{1}{x}\right) \cdot y\right)}}{x} \]

   4. Applied rewrites 91.9%

      \[\leadsto \frac{e^{x \cdot \color{blue}{\left(\left(\frac{y}{x \cdot x} \cdot 0.5 - \frac{1}{x}\right) \cdot y\right)}}}{x} \]

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 100.0

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

   7. Applied rewrites 100.0%

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

   if -4.3e39 < x < 5.69999999999999973e-37

   1. Initial program 83.6%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 96.2%

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

   if 5.69999999999999973e-37 < x

   1. Initial program 75.8%

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

   2. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 96.8

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

   4. Applied rewrites 96.8%

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

Alternative 2: 97.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{e^{-y}}{x}\\ \mathbf{if}\;x \leq -4.3 \cdot 10^{+39}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 5.7 \cdot 10^{-37}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (exp (- y)) x)))
   (if (<= x -4.3e+39) t_0 (if (<= x 5.7e-37) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = exp(-y) / x;
	double tmp;
	if (x <= -4.3e+39) {
		tmp = t_0;
	} else if (x <= 5.7e-37) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = exp(-y) / x
    if (x <= (-4.3d+39)) then
        tmp = t_0
    else if (x <= 5.7d-37) 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 <= -4.3e+39) {
		tmp = t_0;
	} else if (x <= 5.7e-37) {
		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 <= -4.3e+39:
		tmp = t_0
	elif x <= 5.7e-37:
		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 <= -4.3e+39)
		tmp = t_0;
	elseif (x <= 5.7e-37)
		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 <= -4.3e+39)
		tmp = t_0;
	elseif (x <= 5.7e-37)
		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, -4.3e+39], t$95$0, If[LessEqual[x, 5.7e-37], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -4.3e39 or 5.69999999999999973e-37 < x

   1. Initial program 73.5%

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

   2. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 98.2

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

   4. Applied rewrites 98.2%

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

   if -4.3e39 < x < 5.69999999999999973e-37

   1. Initial program 83.6%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 96.2%

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

Alternative 3: 82.0% accurate, 3.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.1 \cdot 10^{+44}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, y, 0.5\right) \cdot y - 1, y, 1\right)}{x}\\ \mathbf{elif}\;x \leq 1.3 \cdot 10^{+153}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(\mathsf{fma}\left(0.5 \cdot y - 1, y, 1\right), x, \left(y \cdot y\right) \cdot 0.5\right)}{x}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5.1e+44)
   (/ (fma (- (* (fma -0.16666666666666666 y 0.5) y) 1.0) y 1.0) x)
   (if (<= x 1.3e+153)
     (/ 1.0 x)
     (/ (/ (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 <= -5.1e+44) {
		tmp = fma(((fma(-0.16666666666666666, y, 0.5) * y) - 1.0), y, 1.0) / x;
	} else if (x <= 1.3e+153) {
		tmp = 1.0 / x;
	} else {
		tmp = (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 <= -5.1e+44)
		tmp = Float64(fma(Float64(Float64(fma(-0.16666666666666666, y, 0.5) * y) - 1.0), y, 1.0) / x);
	elseif (x <= 1.3e+153)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(fma(fma(Float64(Float64(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, -5.1e+44], N[(N[(N[(N[(N[(-0.16666666666666666 * y + 0.5), $MachinePrecision] * y), $MachinePrecision] - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 1.3e+153], N[(1.0 / x), $MachinePrecision], N[(N[(N[(N[(N[(N[(0.5 * y), $MachinePrecision] - 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 < -5.1e44

   1. Initial program 70.1%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

   4. Applied rewrites 74.8%

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

   5. Taylor expanded in x around inf

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

      1. +-commutative N/A

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

      2. lower-fma.f64 74.8

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

   7. Applied rewrites 74.8%

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

   if -5.1e44 < x < 1.2999999999999999e153

   1. Initial program 85.4%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 88.7%

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

   if 1.2999999999999999e153 < x

   1. Initial program 61.6%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. lower-+.f64 N/A

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

      9. associate-*r/ N/A

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

      10. metadata-eval N/A

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

      11. lower-/.f64 58.6

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

   4. Applied rewrites 58.6%

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

   5. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. +-commutative N/A

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

      6. *-commutative N/A

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

      7. lower-fma.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. unpow2 N/A

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

      13. lower-*.f64 69.2

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

   7. Applied rewrites 69.2%

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

Alternative 4: 81.0% accurate, 5.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, y, 0.5\right) \cdot y - 1, y, 1\right)}{x}\\ \mathbf{if}\;x \leq -5.1 \cdot 10^{+44}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 1.3 \cdot 10^{+153}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (fma (- (* (fma -0.16666666666666666 y 0.5) y) 1.0) y 1.0) x)))
   (if (<= x -5.1e+44) t_0 (if (<= x 1.3e+153) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = fma(((fma(-0.16666666666666666, y, 0.5) * y) - 1.0), y, 1.0) / x;
	double tmp;
	if (x <= -5.1e+44) {
		tmp = t_0;
	} else if (x <= 1.3e+153) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(fma(Float64(Float64(fma(-0.16666666666666666, y, 0.5) * y) - 1.0), y, 1.0) / x)
	tmp = 0.0
	if (x <= -5.1e+44)
		tmp = t_0;
	elseif (x <= 1.3e+153)
		tmp = Float64(1.0 / x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(N[(N[(-0.16666666666666666 * y + 0.5), $MachinePrecision] * y), $MachinePrecision] - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[x, -5.1e+44], t$95$0, If[LessEqual[x, 1.3e+153], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -5.1e44 or 1.2999999999999999e153 < x

   1. Initial program 66.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

   4. Applied rewrites 70.2%

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

   5. Taylor expanded in x around inf

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

      1. +-commutative N/A

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

      2. lower-fma.f64 70.2

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

   7. Applied rewrites 70.2%

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

   if -5.1e44 < x < 1.2999999999999999e153

   1. Initial program 85.4%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 88.7%

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

Alternative 5: 80.9% accurate, 5.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\mathsf{fma}\left(\left(-0.16666666666666666 \cdot y\right) \cdot y - 1, y, 1\right)}{x}\\ \mathbf{if}\;x \leq -5.1 \cdot 10^{+44}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 1.3 \cdot 10^{+153}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (fma (- (* (* -0.16666666666666666 y) y) 1.0) y 1.0) x)))
   (if (<= x -5.1e+44) t_0 (if (<= x 1.3e+153) (/ 1.0 x) t_0))))

C

double code(double x, double y) {
	double t_0 = fma((((-0.16666666666666666 * y) * y) - 1.0), y, 1.0) / x;
	double tmp;
	if (x <= -5.1e+44) {
		tmp = t_0;
	} else if (x <= 1.3e+153) {
		tmp = 1.0 / x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(fma(Float64(Float64(Float64(-0.16666666666666666 * y) * y) - 1.0), y, 1.0) / x)
	tmp = 0.0
	if (x <= -5.1e+44)
		tmp = t_0;
	elseif (x <= 1.3e+153)
		tmp = Float64(1.0 / x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(N[(N[(N[(-0.16666666666666666 * y), $MachinePrecision] * y), $MachinePrecision] - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[x, -5.1e+44], t$95$0, If[LessEqual[x, 1.3e+153], N[(1.0 / x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -5.1e44 or 1.2999999999999999e153 < x

   1. Initial program 66.8%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

   4. Applied rewrites 70.2%

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

   5. Taylor expanded in x around inf

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

      1. +-commutative N/A

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

      2. lower-fma.f64 70.2

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

   7. Applied rewrites 70.2%

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

   8. Taylor expanded in y around inf

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

      1. lower-*.f64 69.9

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

   10. Applied rewrites 69.9%

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

   if -5.1e44 < x < 1.2999999999999999e153

   1. Initial program 85.4%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 88.7%

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

Alternative 6: 80.1% accurate, 5.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.1 \cdot 10^{+44}:\\ \;\;\;\;\frac{\mathsf{fma}\left(0.5 \cdot y - 1, y, 1\right)}{x}\\ \mathbf{elif}\;x \leq 8 \cdot 10^{+195}:\\ \;\;\;\;\frac{1}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(-x, y, x\right)}{x}}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5.1e+44)
   (/ (fma (- (* 0.5 y) 1.0) y 1.0) x)
   (if (<= x 8e+195) (/ 1.0 x) (/ (/ (fma (- x) y x) x) x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -5.1e+44) {
		tmp = fma(((0.5 * y) - 1.0), y, 1.0) / x;
	} else if (x <= 8e+195) {
		tmp = 1.0 / x;
	} else {
		tmp = (fma(-x, y, x) / x) / x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -5.1e+44)
		tmp = Float64(fma(Float64(Float64(0.5 * y) - 1.0), y, 1.0) / x);
	elseif (x <= 8e+195)
		tmp = Float64(1.0 / x);
	else
		tmp = Float64(Float64(fma(Float64(-x), y, x) / x) / x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, -5.1e+44], N[(N[(N[(N[(0.5 * y), $MachinePrecision] - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] / x), $MachinePrecision], If[LessEqual[x, 8e+195], N[(1.0 / x), $MachinePrecision], N[(N[(N[((-x) * y + x), $MachinePrecision] / x), $MachinePrecision] / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.1e44

   1. Initial program 70.1%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. lower-+.f64 N/A

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

      9. associate-*r/ N/A

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

      10. metadata-eval N/A

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

      11. lower-/.f64 69.8

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

   4. Applied rewrites 69.8%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 69.8%

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

   if -5.1e44 < x < 7.99999999999999982e195

   1. Initial program 84.3%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 86.3%

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

   if 7.99999999999999982e195 < x

   1. Initial program 59.1%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. lower-+.f64 N/A

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

      9. associate-*r/ N/A

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

      10. metadata-eval N/A

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

      11. lower-/.f64 60.1

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

   4. Applied rewrites 60.1%

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

   5. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. +-commutative N/A

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

      6. *-commutative N/A

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

      7. lower-fma.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. unpow2 N/A

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

      13. lower-*.f64 71.7

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

   7. Applied rewrites 71.7%

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

   8. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. associate-*r* N/A

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

      3. mul-1-neg N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-neg.f64 69.0

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

   10. Applied rewrites 69.0%

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

Alternative 7: 78.2% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.1 \cdot 10^{+44}:\\ \;\;\;\;\frac{\mathsf{fma}\left(0.5 \cdot y - 1, y, 1\right)}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -5.1e+44) (/ (fma (- (* 0.5 y) 1.0) y 1.0) x) (/ 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if (x <= -5.1e+44) {
		tmp = fma(((0.5 * y) - 1.0), y, 1.0) / x;
	} else {
		tmp = 1.0 / x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -5.1e+44)
		tmp = Float64(fma(Float64(Float64(0.5 * y) - 1.0), y, 1.0) / x);
	else
		tmp = Float64(1.0 / x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, -5.1e+44], N[(N[(N[(N[(0.5 * y), $MachinePrecision] - 1.0), $MachinePrecision] * y + 1.0), $MachinePrecision] / x), $MachinePrecision], N[(1.0 / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -5.1e44

   1. Initial program 70.1%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. +-commutative N/A

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

      8. lower-+.f64 N/A

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

      9. associate-*r/ N/A

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

      10. metadata-eval N/A

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

      11. lower-/.f64 69.8

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

   4. Applied rewrites 69.8%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 69.8%

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

   if -5.1e44 < x

   1. Initial program 80.3%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 81.0%

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

Alternative 8: 74.7% 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

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x, y)
use fmin_fmax_functions
    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 77.7%

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

2. Taylor expanded in x around 0

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

4. Applied rewrites 74.7%

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

Reproduce

herbie shell --seed 2025101 
(FPCore (x y)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, F"
  :precision binary64
  (/ (exp (* x (log (/ x (+ x y))))) x))