Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2

Percentage Accurate: 99.8% → 99.9%

Time: 8.3s

Alternatives: 13

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (+ (- x (* (+ y 0.5) (log y))) y) z))

C

double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * log(y))) + y) - z;
}

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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((x - ((y + 0.5d0) * log(y))) + y) - z
end function

Java

public static double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * Math.log(y))) + y) - z;
}

Python

def code(x, y, z):
	return ((x - ((y + 0.5) * math.log(y))) + y) - z

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x - Float64(Float64(y + 0.5) * log(y))) + y) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x - ((y + 0.5) * log(y))) + y) - z;
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x - N[(N[(y + 0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (+ (- x (* (+ y 0.5) (log y))) y) z))

C

double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * log(y))) + y) - z;
}

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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((x - ((y + 0.5d0) * log(y))) + y) - z
end function

Java

public static double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * Math.log(y))) + y) - z;
}

Python

def code(x, y, z):
	return ((x - ((y + 0.5) * math.log(y))) + y) - z

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x - Float64(Float64(y + 0.5) * log(y))) + y) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x - ((y + 0.5) * log(y))) + y) - z;
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x - N[(N[(y + 0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \left(\mathsf{fma}\left(1 - \log y, y, x\right) - \log \left(\sqrt{y}\right)\right) - z \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (- (- (fma (- 1.0 (log y)) y x) (log (sqrt y))) z))

C

double code(double x, double y, double z) {
	return (fma((1.0 - log(y)), y, x) - log(sqrt(y))) - z;
}

Julia

function code(x, y, z)
	return Float64(Float64(fma(Float64(1.0 - log(y)), y, x) - log(sqrt(y))) - z)
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y + x), $MachinePrecision] - N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

2. Taylor expanded in y around 0

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

   1. lower--.f64 N/A

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

   2. +-commutative N/A

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

   3. *-commutative N/A

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

   4. lower-fma.f64 N/A

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

   5. lower--.f64 N/A

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

   6. lift-log.f64 N/A

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

   7. log-pow-rev N/A

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

   8. lower-log.f64 N/A

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

   9. unpow1/2 N/A

      \[\leadsto \left(\mathsf{fma}\left(1 - \log y, y, x\right) - \log \left(\sqrt{y}\right)\right) - z \]

   10. lower-sqrt.f64 99.9

       \[\leadsto \left(\mathsf{fma}\left(1 - \log y, y, x\right) - \log \left(\sqrt{y}\right)\right) - z \]

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\left(\mathsf{fma}\left(1 - \log y, y, x\right) - \log \left(\sqrt{y}\right)\right)} - z \]
5. Add Preprocessing

Alternative 2: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (+ (- x (* (+ y 0.5) (log y))) y) z))

C

double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * log(y))) + y) - z;
}

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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((x - ((y + 0.5d0) * log(y))) + y) - z
end function

Java

public static double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * Math.log(y))) + y) - z;
}

Python

def code(x, y, z):
	return ((x - ((y + 0.5) * math.log(y))) + y) - z

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x - Float64(Float64(y + 0.5) * log(y))) + y) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x - ((y + 0.5) * log(y))) + y) - z;
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x - N[(N[(y + 0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
2. Add Preprocessing

Alternative 3: 89.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.45 \cdot 10^{+72}:\\ \;\;\;\;\left(x - \log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(y + x\right) - \log y \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.45e+72) (- (- x (log (sqrt y))) z) (- (+ y x) (* (log y) y))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.45e+72) {
		tmp = (x - log(sqrt(y))) - z;
	} else {
		tmp = (y + x) - (log(y) * y);
	}
	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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 1.45d+72) then
        tmp = (x - log(sqrt(y))) - z
    else
        tmp = (y + x) - (log(y) * y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.45e+72) {
		tmp = (x - Math.log(Math.sqrt(y))) - z;
	} else {
		tmp = (y + x) - (Math.log(y) * y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 1.45e+72:
		tmp = (x - math.log(math.sqrt(y))) - z
	else:
		tmp = (y + x) - (math.log(y) * y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 1.45e+72)
		tmp = Float64(Float64(x - log(sqrt(y))) - z);
	else
		tmp = Float64(Float64(y + x) - Float64(log(y) * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 1.45e+72)
		tmp = (x - log(sqrt(y))) - z;
	else
		tmp = (y + x) - (log(y) * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 1.45e+72], N[(N[(x - N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision], N[(N[(y + x), $MachinePrecision] - N[(N[Log[y], $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.45000000000000009e72

   1. Initial program 100.0%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. log-pow-rev N/A

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

      3. lower-log.f64 N/A

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

      4. unpow1/2 N/A

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

      5. lower-sqrt.f64 93.9

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

   4. Applied rewrites 93.9%

      \[\leadsto \color{blue}{\left(x - \log \left(\sqrt{y}\right)\right)} - z \]

   if 1.45000000000000009e72 < y

   1. Initial program 99.6%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. metadata-eval N/A

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

      8. fp-cancel-sign-sub-inv N/A

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. lower--.f64 82.0

          \[\leadsto \left(y + x\right) - \log y \cdot \left(y - \color{blue}{-0.5}\right) \]

   4. Applied rewrites 82.0%

      \[\leadsto \color{blue}{\left(y + x\right) - \log y \cdot \left(y - -0.5\right)} \]

   5. Taylor expanded in y around inf

      \[\leadsto \left(y + x\right) - \log y \cdot y \]
   6. Step-by-step derivation

   7. Applied rewrites 82.0%

      \[\leadsto \left(y + x\right) - \log y \cdot y \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 89.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.35 \cdot 10^{+101}:\\ \;\;\;\;\left(x - \log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.35e+101) (- (- x (log (sqrt y))) z) (- (* (- 1.0 (log y)) y) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.35e+101) {
		tmp = (x - log(sqrt(y))) - z;
	} else {
		tmp = ((1.0 - log(y)) * y) - z;
	}
	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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 1.35d+101) then
        tmp = (x - log(sqrt(y))) - z
    else
        tmp = ((1.0d0 - log(y)) * y) - z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.35e+101) {
		tmp = (x - Math.log(Math.sqrt(y))) - z;
	} else {
		tmp = ((1.0 - Math.log(y)) * y) - z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 1.35e+101:
		tmp = (x - math.log(math.sqrt(y))) - z
	else:
		tmp = ((1.0 - math.log(y)) * y) - z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 1.35e+101)
		tmp = Float64(Float64(x - log(sqrt(y))) - z);
	else
		tmp = Float64(Float64(Float64(1.0 - log(y)) * y) - z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 1.35e+101)
		tmp = (x - log(sqrt(y))) - z;
	else
		tmp = ((1.0 - log(y)) * y) - z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 1.35e+101], N[(N[(x - N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision], N[(N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision] - z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.35000000000000003e101

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. log-pow-rev N/A

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

      3. lower-log.f64 N/A

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

      4. unpow1/2 N/A

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

      5. lower-sqrt.f64 91.6

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

   4. Applied rewrites 91.6%

      \[\leadsto \color{blue}{\left(x - \log \left(\sqrt{y}\right)\right)} - z \]

   if 1.35000000000000003e101 < y

   1. Initial program 99.6%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in x around -inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. fp-cancel-sub-sign-inv N/A

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

      7. associate-/l* N/A

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

      8. metadata-eval N/A

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

      9. metadata-eval N/A

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

      10. lower-fma.f64 N/A

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

      11. lift-log.f64 N/A

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

      12. +-commutative N/A

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

      13. lower-/.f64 N/A

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

      14. metadata-eval N/A

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

      15. fp-cancel-sign-sub-inv N/A

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

      16. metadata-eval N/A

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

      17. metadata-eval N/A

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

      18. lower--.f64 66.9

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

   4. Applied rewrites 66.9%

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

   5. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. mul-1-neg N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)\right) \cdot y - z \]

      3. neg-log N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\log y\right)\right)\right)\right)\right) \cdot y - z \]

      4. lower-*.f64 N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\log y\right)\right)\right)\right)\right) \cdot \color{blue}{y} - z \]

      5. remove-double-neg N/A

         \[\leadsto \left(1 - \log y\right) \cdot y - z \]

      6. lower--.f64 N/A

         \[\leadsto \left(1 - \log y\right) \cdot y - z \]

      7. lift-log.f64 85.1

         \[\leadsto \left(1 - \log y\right) \cdot y - z \]

   7. Applied rewrites 85.1%

      \[\leadsto \color{blue}{\left(1 - \log y\right) \cdot y} - z \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 89.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.35 \cdot 10^{+101}:\\ \;\;\;\;\left(x - \log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(y - y \cdot \log y\right) - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.35e+101) (- (- x (log (sqrt y))) z) (- (- y (* y (log y))) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.35e+101) {
		tmp = (x - log(sqrt(y))) - z;
	} else {
		tmp = (y - (y * log(y))) - z;
	}
	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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 1.35d+101) then
        tmp = (x - log(sqrt(y))) - z
    else
        tmp = (y - (y * log(y))) - z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.35e+101) {
		tmp = (x - Math.log(Math.sqrt(y))) - z;
	} else {
		tmp = (y - (y * Math.log(y))) - z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 1.35e+101:
		tmp = (x - math.log(math.sqrt(y))) - z
	else:
		tmp = (y - (y * math.log(y))) - z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 1.35e+101)
		tmp = Float64(Float64(x - log(sqrt(y))) - z);
	else
		tmp = Float64(Float64(y - Float64(y * log(y))) - z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 1.35e+101)
		tmp = (x - log(sqrt(y))) - z;
	else
		tmp = (y - (y * log(y))) - z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 1.35e+101], N[(N[(x - N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision], N[(N[(y - N[(y * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.35000000000000003e101

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. log-pow-rev N/A

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

      3. lower-log.f64 N/A

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

      4. unpow1/2 N/A

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

      5. lower-sqrt.f64 91.6

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

   4. Applied rewrites 91.6%

      \[\leadsto \color{blue}{\left(x - \log \left(\sqrt{y}\right)\right)} - z \]

   if 1.35000000000000003e101 < y

   1. Initial program 99.6%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. +-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. metadata-eval N/A

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

      7. fp-cancel-sign-sub-inv N/A

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

      8. metadata-eval N/A

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

      9. metadata-eval N/A

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

      10. lower--.f64 85.0

          \[\leadsto y - \mathsf{fma}\left(\log y, y - \color{blue}{-0.5}, z\right) \]

   4. Applied rewrites 85.0%

      \[\leadsto \color{blue}{y - \mathsf{fma}\left(\log y, y - -0.5, z\right)} \]

   5. Taylor expanded in y around inf

      \[\leadsto y - \mathsf{fma}\left(\log y, y, z\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 85.0%

      \[\leadsto y - \mathsf{fma}\left(\log y, y, z\right) \]
   8. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto y - \color{blue}{\mathsf{fma}\left(\log y, y, z\right)} \]

      2. lift-fma.f64 N/A

         \[\leadsto y - \left(\log y \cdot y + \color{blue}{z}\right) \]

      3. associate--r+ N/A

         \[\leadsto \left(y - \log y \cdot y\right) - \color{blue}{z} \]

      4. lower--.f64 N/A

         \[\leadsto \left(y - \log y \cdot y\right) - \color{blue}{z} \]

      5. lower--.f64 N/A

         \[\leadsto \left(y - \log y \cdot y\right) - z \]

      6. lift-log.f64 N/A

         \[\leadsto \left(y - \log y \cdot y\right) - z \]

      7. *-commutative N/A

         \[\leadsto \left(y - y \cdot \log y\right) - z \]

      8. lower-*.f64 N/A

         \[\leadsto \left(y - y \cdot \log y\right) - z \]

      9. lift-log.f64 85.0

         \[\leadsto \left(y - y \cdot \log y\right) - z \]

   9. Applied rewrites 85.0%

      \[\leadsto \left(y - y \cdot \log y\right) - \color{blue}{z} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 89.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.35 \cdot 10^{+101}:\\ \;\;\;\;\left(x - \log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;y - \mathsf{fma}\left(\log y, y, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.35e+101) (- (- x (log (sqrt y))) z) (- y (fma (log y) y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.35e+101) {
		tmp = (x - log(sqrt(y))) - z;
	} else {
		tmp = y - fma(log(y), y, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 1.35e+101)
		tmp = Float64(Float64(x - log(sqrt(y))) - z);
	else
		tmp = Float64(y - fma(log(y), y, z));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 1.35e+101], N[(N[(x - N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision], N[(y - N[(N[Log[y], $MachinePrecision] * y + z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.35000000000000003e101

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. log-pow-rev N/A

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

      3. lower-log.f64 N/A

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

      4. unpow1/2 N/A

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

      5. lower-sqrt.f64 91.6

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

   4. Applied rewrites 91.6%

      \[\leadsto \color{blue}{\left(x - \log \left(\sqrt{y}\right)\right)} - z \]

   if 1.35000000000000003e101 < y

   1. Initial program 99.6%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. +-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. metadata-eval N/A

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

      7. fp-cancel-sign-sub-inv N/A

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

      8. metadata-eval N/A

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

      9. metadata-eval N/A

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

      10. lower--.f64 85.0

          \[\leadsto y - \mathsf{fma}\left(\log y, y - \color{blue}{-0.5}, z\right) \]

   4. Applied rewrites 85.0%

      \[\leadsto \color{blue}{y - \mathsf{fma}\left(\log y, y - -0.5, z\right)} \]

   5. Taylor expanded in y around inf

      \[\leadsto y - \mathsf{fma}\left(\log y, y, z\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 85.0%

      \[\leadsto y - \mathsf{fma}\left(\log y, y, z\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 84.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 2.15 \cdot 10^{+111}:\\ \;\;\;\;\left(x - \log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 2.15e+111) (- (- x (log (sqrt y))) z) (* (- 1.0 (log y)) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 2.15e+111) {
		tmp = (x - log(sqrt(y))) - z;
	} else {
		tmp = (1.0 - log(y)) * y;
	}
	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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 2.15d+111) then
        tmp = (x - log(sqrt(y))) - z
    else
        tmp = (1.0d0 - log(y)) * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 2.15e+111) {
		tmp = (x - Math.log(Math.sqrt(y))) - z;
	} else {
		tmp = (1.0 - Math.log(y)) * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 2.15e+111:
		tmp = (x - math.log(math.sqrt(y))) - z
	else:
		tmp = (1.0 - math.log(y)) * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 2.15e+111)
		tmp = Float64(Float64(x - log(sqrt(y))) - z);
	else
		tmp = Float64(Float64(1.0 - log(y)) * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 2.15e+111)
		tmp = (x - log(sqrt(y))) - z;
	else
		tmp = (1.0 - log(y)) * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 2.15e+111], N[(N[(x - N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 2.14999999999999997e111

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. log-pow-rev N/A

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

      3. lower-log.f64 N/A

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

      4. unpow1/2 N/A

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

      5. lower-sqrt.f64 90.8

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

   4. Applied rewrites 90.8%

      \[\leadsto \color{blue}{\left(x - \log \left(\sqrt{y}\right)\right)} - z \]

   if 2.14999999999999997e111 < y

   1. Initial program 99.6%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. mul-1-neg N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)\right) \cdot y \]

      5. lower-neg.f64 N/A

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

      6. log-rec N/A

         \[\leadsto \left(1 - \left(-\left(\mathsf{neg}\left(\log y\right)\right)\right)\right) \cdot y \]

      7. lower-neg.f64 N/A

         \[\leadsto \left(1 - \left(-\left(-\log y\right)\right)\right) \cdot y \]

      8. lift-log.f64 71.5

         \[\leadsto \left(1 - \left(-\left(-\log y\right)\right)\right) \cdot y \]

   4. Applied rewrites 71.5%

      \[\leadsto \color{blue}{\left(1 - \left(-\left(-\log y\right)\right)\right) \cdot y} \]
   5. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\left(-\log y\right)\right)\right)\right) \cdot y \]

      2. lift-log.f64 N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\left(-\log y\right)\right)\right)\right) \cdot y \]

      3. lift-neg.f64 N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\log y\right)\right)\right)\right)\right) \cdot y \]

      4. remove-double-neg N/A

         \[\leadsto \left(1 - \log y\right) \cdot y \]

      5. lift-log.f64 71.5

         \[\leadsto \left(1 - \log y\right) \cdot y \]

   6. Applied rewrites 71.5%

      \[\leadsto \color{blue}{\left(1 - \log y\right) \cdot y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 75.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x - \left(y + 0.5\right) \cdot \log y\right) + y\\ \mathbf{if}\;t\_0 \leq -1.2 \cdot 10^{+170}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \mathbf{elif}\;t\_0 \leq -1 \cdot 10^{+39}:\\ \;\;\;\;x - z\\ \mathbf{elif}\;t\_0 \leq 500:\\ \;\;\;\;\left(-\log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ (- x (* (+ y 0.5) (log y))) y)))
   (if (<= t_0 -1.2e+170)
     (* (- 1.0 (log y)) y)
     (if (<= t_0 -1e+39)
       (- x z)
       (if (<= t_0 500.0) (- (- (log (sqrt y))) z) (- x z))))))

C

double code(double x, double y, double z) {
	double t_0 = (x - ((y + 0.5) * log(y))) + y;
	double tmp;
	if (t_0 <= -1.2e+170) {
		tmp = (1.0 - log(y)) * y;
	} else if (t_0 <= -1e+39) {
		tmp = x - z;
	} else if (t_0 <= 500.0) {
		tmp = -log(sqrt(y)) - z;
	} else {
		tmp = x - z;
	}
	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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x - ((y + 0.5d0) * log(y))) + y
    if (t_0 <= (-1.2d+170)) then
        tmp = (1.0d0 - log(y)) * y
    else if (t_0 <= (-1d+39)) then
        tmp = x - z
    else if (t_0 <= 500.0d0) then
        tmp = -log(sqrt(y)) - z
    else
        tmp = x - z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (x - ((y + 0.5) * Math.log(y))) + y;
	double tmp;
	if (t_0 <= -1.2e+170) {
		tmp = (1.0 - Math.log(y)) * y;
	} else if (t_0 <= -1e+39) {
		tmp = x - z;
	} else if (t_0 <= 500.0) {
		tmp = -Math.log(Math.sqrt(y)) - z;
	} else {
		tmp = x - z;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x - ((y + 0.5) * math.log(y))) + y
	tmp = 0
	if t_0 <= -1.2e+170:
		tmp = (1.0 - math.log(y)) * y
	elif t_0 <= -1e+39:
		tmp = x - z
	elif t_0 <= 500.0:
		tmp = -math.log(math.sqrt(y)) - z
	else:
		tmp = x - z
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x - Float64(Float64(y + 0.5) * log(y))) + y)
	tmp = 0.0
	if (t_0 <= -1.2e+170)
		tmp = Float64(Float64(1.0 - log(y)) * y);
	elseif (t_0 <= -1e+39)
		tmp = Float64(x - z);
	elseif (t_0 <= 500.0)
		tmp = Float64(Float64(-log(sqrt(y))) - z);
	else
		tmp = Float64(x - z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x - ((y + 0.5) * log(y))) + y;
	tmp = 0.0;
	if (t_0 <= -1.2e+170)
		tmp = (1.0 - log(y)) * y;
	elseif (t_0 <= -1e+39)
		tmp = x - z;
	elseif (t_0 <= 500.0)
		tmp = -log(sqrt(y)) - z;
	else
		tmp = x - z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x - N[(N[(y + 0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision]}, If[LessEqual[t$95$0, -1.2e+170], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[t$95$0, -1e+39], N[(x - z), $MachinePrecision], If[LessEqual[t$95$0, 500.0], N[((-N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]) - z), $MachinePrecision], N[(x - z), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < -1.2e170

   1. Initial program 99.7%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. mul-1-neg N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\log \left(\frac{1}{y}\right)\right)\right)\right) \cdot y \]

      5. lower-neg.f64 N/A

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

      6. log-rec N/A

         \[\leadsto \left(1 - \left(-\left(\mathsf{neg}\left(\log y\right)\right)\right)\right) \cdot y \]

      7. lower-neg.f64 N/A

         \[\leadsto \left(1 - \left(-\left(-\log y\right)\right)\right) \cdot y \]

      8. lift-log.f64 59.3

         \[\leadsto \left(1 - \left(-\left(-\log y\right)\right)\right) \cdot y \]

   4. Applied rewrites 59.3%

      \[\leadsto \color{blue}{\left(1 - \left(-\left(-\log y\right)\right)\right) \cdot y} \]
   5. Step-by-step derivation

      1. lift-neg.f64 N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\left(-\log y\right)\right)\right)\right) \cdot y \]

      2. lift-log.f64 N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\left(-\log y\right)\right)\right)\right) \cdot y \]

      3. lift-neg.f64 N/A

         \[\leadsto \left(1 - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\log y\right)\right)\right)\right)\right) \cdot y \]

      4. remove-double-neg N/A

         \[\leadsto \left(1 - \log y\right) \cdot y \]

      5. lift-log.f64 59.3

         \[\leadsto \left(1 - \log y\right) \cdot y \]

   6. Applied rewrites 59.3%

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

   if -1.2e170 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < -9.9999999999999994e38 or 500 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y)

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 75.4%

      \[\leadsto \color{blue}{x} - z \]

   if -9.9999999999999994e38 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < 500

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. log-pow-rev N/A

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

      3. lower-log.f64 N/A

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

      4. unpow1/2 N/A

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

      5. lower-sqrt.f64 94.2

         \[\leadsto \left(x - \log \left(\sqrt{y}\right)\right) - z \]

   4. Applied rewrites 94.2%

      \[\leadsto \color{blue}{\left(x - \log \left(\sqrt{y}\right)\right)} - z \]

   5. Taylor expanded in x around 0

      \[\leadsto -1 \cdot \color{blue}{\log \left(\sqrt{y}\right)} - z \]
   6. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\log \left(\sqrt{y}\right)\right)\right) - z \]

      2. lower-neg.f64 N/A

         \[\leadsto \left(-\log \left(\sqrt{y}\right)\right) - z \]

      3. lift-sqrt.f64 N/A

         \[\leadsto \left(-\log \left(\sqrt{y}\right)\right) - z \]

      4. lift-log.f64 90.6

         \[\leadsto \left(-\log \left(\sqrt{y}\right)\right) - z \]

   7. Applied rewrites 90.6%

      \[\leadsto \left(-\log \left(\sqrt{y}\right)\right) - z \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 70.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -57000000000000:\\ \;\;\;\;x - z\\ \mathbf{elif}\;z \leq 1.2 \cdot 10^{-9}:\\ \;\;\;\;x - \log \left(\sqrt{y}\right)\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -57000000000000.0)
   (- x z)
   (if (<= z 1.2e-9) (- x (log (sqrt y))) (- x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -57000000000000.0) {
		tmp = x - z;
	} else if (z <= 1.2e-9) {
		tmp = x - log(sqrt(y));
	} else {
		tmp = x - z;
	}
	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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-57000000000000.0d0)) then
        tmp = x - z
    else if (z <= 1.2d-9) then
        tmp = x - log(sqrt(y))
    else
        tmp = x - z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -57000000000000.0) {
		tmp = x - z;
	} else if (z <= 1.2e-9) {
		tmp = x - Math.log(Math.sqrt(y));
	} else {
		tmp = x - z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -57000000000000.0:
		tmp = x - z
	elif z <= 1.2e-9:
		tmp = x - math.log(math.sqrt(y))
	else:
		tmp = x - z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -57000000000000.0)
		tmp = Float64(x - z);
	elseif (z <= 1.2e-9)
		tmp = Float64(x - log(sqrt(y)));
	else
		tmp = Float64(x - z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -57000000000000.0)
		tmp = x - z;
	elseif (z <= 1.2e-9)
		tmp = x - log(sqrt(y));
	else
		tmp = x - z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -57000000000000.0], N[(x - z), $MachinePrecision], If[LessEqual[z, 1.2e-9], N[(x - N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(x - z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if z < -5.7e13 or 1.2e-9 < z

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 78.3%

      \[\leadsto \color{blue}{x} - z \]

   if -5.7e13 < z < 1.2e-9

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. metadata-eval N/A

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

      8. fp-cancel-sign-sub-inv N/A

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

      9. metadata-eval N/A

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

      10. metadata-eval N/A

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

      11. lower--.f64 99.1

          \[\leadsto \left(y + x\right) - \log y \cdot \left(y - \color{blue}{-0.5}\right) \]

   4. Applied rewrites 99.1%

      \[\leadsto \color{blue}{\left(y + x\right) - \log y \cdot \left(y - -0.5\right)} \]

   5. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. log-pow-rev N/A

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

      3. lower-log.f64 N/A

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

      4. unpow1/2 N/A

         \[\leadsto x - \log \left(\sqrt{y}\right) \]

      5. lower-sqrt.f64 62.3

         \[\leadsto x - \log \left(\sqrt{y}\right) \]

   7. Applied rewrites 62.3%

      \[\leadsto x - \color{blue}{\log \left(\sqrt{y}\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 58.3% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ x - z \end{array} \]

FPCore

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

C

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

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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x - z
end function

Java

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

Python

def code(x, y, z):
	return x - z

Julia

function code(x, y, z)
	return Float64(x - z)
end

MATLAB

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

Wolfram

code[x_, y_, z_] := N[(x - z), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

2. Taylor expanded in x around inf

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

4. Applied rewrites 58.3%

   \[\leadsto \color{blue}{x} - z \]
5. Add Preprocessing

Alternative 11: 48.6% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+22}:\\ \;\;\;\;y - z\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{+59}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.1e+22) (- y z) (if (<= z 4.2e+59) x (- z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.1e+22) {
		tmp = y - z;
	} else if (z <= 4.2e+59) {
		tmp = x;
	} else {
		tmp = -z;
	}
	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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-2.1d+22)) then
        tmp = y - z
    else if (z <= 4.2d+59) then
        tmp = x
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.1e+22) {
		tmp = y - z;
	} else if (z <= 4.2e+59) {
		tmp = x;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.1e+22:
		tmp = y - z
	elif z <= 4.2e+59:
		tmp = x
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.1e+22)
		tmp = Float64(y - z);
	elseif (z <= 4.2e+59)
		tmp = x;
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.1e+22)
		tmp = y - z;
	elseif (z <= 4.2e+59)
		tmp = x;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.1e+22], N[(y - z), $MachinePrecision], If[LessEqual[z, 4.2e+59], x, (-z)]]

Derivation

1. Split input into 3 regimes

2. if z < -2.0999999999999998e22

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. +-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. metadata-eval N/A

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

      7. fp-cancel-sign-sub-inv N/A

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

      8. metadata-eval N/A

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

      9. metadata-eval N/A

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

      10. lower--.f64 80.3

          \[\leadsto y - \mathsf{fma}\left(\log y, y - \color{blue}{-0.5}, z\right) \]

   4. Applied rewrites 80.3%

      \[\leadsto \color{blue}{y - \mathsf{fma}\left(\log y, y - -0.5, z\right)} \]

   5. Taylor expanded in z around inf

      \[\leadsto y - z \]
   6. Step-by-step derivation

   7. Applied rewrites 59.4%

      \[\leadsto y - z \]

   if -2.0999999999999998e22 < z < 4.19999999999999968e59

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 37.9%

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

   if 4.19999999999999968e59 < z

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in z around inf

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

      1. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(z\right) \]

      2. lower-neg.f64 66.5

         \[\leadsto -z \]

   4. Applied rewrites 66.5%

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

Alternative 12: 48.6% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+22}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 4.2 \cdot 10^{+59}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.1e+22) (- z) (if (<= z 4.2e+59) x (- z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.1e+22) {
		tmp = -z;
	} else if (z <= 4.2e+59) {
		tmp = x;
	} else {
		tmp = -z;
	}
	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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-2.1d+22)) then
        tmp = -z
    else if (z <= 4.2d+59) then
        tmp = x
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.1e+22) {
		tmp = -z;
	} else if (z <= 4.2e+59) {
		tmp = x;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.1e+22:
		tmp = -z
	elif z <= 4.2e+59:
		tmp = x
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.1e+22)
		tmp = Float64(-z);
	elseif (z <= 4.2e+59)
		tmp = x;
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.1e+22)
		tmp = -z;
	elseif (z <= 4.2e+59)
		tmp = x;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.1e+22], (-z), If[LessEqual[z, 4.2e+59], x, (-z)]]

Derivation

1. Split input into 2 regimes

2. if z < -2.0999999999999998e22 or 4.19999999999999968e59 < z

   1. Initial program 99.9%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in z around inf

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

      1. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(z\right) \]

      2. lower-neg.f64 62.7

         \[\leadsto -z \]

   4. Applied rewrites 62.7%

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

   if -2.0999999999999998e22 < z < 4.19999999999999968e59

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 37.9%

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

Alternative 13: 29.8% accurate, 20.4× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 x)

C

double code(double x, double y, double z) {
	return 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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x
end function

Java

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

Python

def code(x, y, z):
	return x

Julia

function code(x, y, z)
	return x
end

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 99.8%

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

2. Taylor expanded in x around inf

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

4. Applied rewrites 29.8%

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

Reproduce

herbie shell --seed 2025106 
(FPCore (x y z)
  :name "Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2"
  :precision binary64
  (- (+ (- x (* (+ y 0.5) (log y))) y) z))