Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2

Percentage Accurate: 99.8% → 99.8%

Time: 7.6s

Alternatives: 11

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: 99.8% accurate, 0.9× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

code[x_, y_, z_] := N[(N[((-N[(N[(0.5 + y), $MachinePrecision] * N[Log[y], $MachinePrecision] + (-x)), $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. 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 88.1

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

4. Applied rewrites 88.1%

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

5. Taylor expanded in x around 0

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

   1. mul-1-neg N/A

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

   2. +-commutative N/A

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

   3. *-commutative N/A

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

   4. lower-fma.f64 N/A

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

   5. lift-+.f64 N/A

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

   6. lift-log.f64 N/A

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

   7. lower-neg.f64 99.8

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

7. Applied rewrites 99.8%

   \[\leadsto \left(\left(-\mathsf{fma}\left(0.5 + y, \log y, -x\right)\right) + y\right) - z \]
8. 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: 99.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 0.0074:\\ \;\;\;\;\left(x - \left(\log \left(\sqrt{y}\right) - y\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(x - \left(\log y \cdot y - y\right)\right) - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 0.0074)
   (- (- x (- (log (sqrt y)) y)) z)
   (- (- x (- (* (log y) y) y)) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 0.0074) {
		tmp = (x - (log(sqrt(y)) - y)) - z;
	} else {
		tmp = (x - ((log(y) * 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 <= 0.0074d0) then
        tmp = (x - (log(sqrt(y)) - y)) - z
    else
        tmp = (x - ((log(y) * y) - y)) - z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 0.0074) {
		tmp = (x - (Math.log(Math.sqrt(y)) - y)) - z;
	} else {
		tmp = (x - ((Math.log(y) * y) - y)) - z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 0.0074:
		tmp = (x - (math.log(math.sqrt(y)) - y)) - z
	else:
		tmp = (x - ((math.log(y) * y) - y)) - z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 0.0074)
		tmp = Float64(Float64(x - Float64(log(sqrt(y)) - y)) - z);
	else
		tmp = Float64(Float64(x - Float64(Float64(log(y) * y) - y)) - z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 0.0074)
		tmp = (x - (log(sqrt(y)) - y)) - z;
	else
		tmp = (x - ((log(y) * y) - y)) - z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 0.0074], N[(N[(x - N[(N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision] - y), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision], N[(N[(x - N[(N[(N[Log[y], $MachinePrecision] * y), $MachinePrecision] - y), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 0.0074000000000000003

   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

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

      1. log-pow-rev N/A

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

      2. lower-log.f64 N/A

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

      3. unpow1/2 N/A

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

      4. lower-sqrt.f64 69.9

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

   4. Applied rewrites 69.9%

      \[\leadsto \left(\left(x - \color{blue}{\log \left(\sqrt{y}\right)}\right) + y\right) - z \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. associate-+l- N/A

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

      4. lower--.f64 N/A

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

      5. lower--.f64 69.9

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

   6. Applied rewrites 69.9%

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

   if 0.0074000000000000003 < y

   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 inf

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

   4. Applied rewrites 87.0%

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

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. associate-+l- N/A

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

      4. lower--.f64 N/A

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

      5. lower--.f64 87.0

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

      6. lift-*.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 N/A

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

      10. lift-log.f64 87.0

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

   6. Applied rewrites 87.0%

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

Alternative 4: 89.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.45 \cdot 10^{+34}:\\ \;\;\;\;\left(x - \log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.45e+34) (- (- x (log (sqrt y))) z) (- (* (- 1.0 (log y)) y) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.45e+34) {
		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 <= 1.45d+34) 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 <= 1.45e+34) {
		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 <= 1.45e+34:
		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 <= 1.45e+34)
		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 <= 1.45e+34)
		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, 1.45e+34], 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 < 1.4500000000000001e34

   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

      \[\leadsto \color{blue}{\left(x - \frac{1}{2} \cdot \log y\right)} - z \]
   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 70.9

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

   4. Applied rewrites 70.9%

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

   if 1.4500000000000001e34 < y

   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 inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. mul-1-neg N/A

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

      5. lower-neg.f64 N/A

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

      6. log-rec N/A

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

      7. lower-neg.f64 N/A

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

      8. lift-log.f64 57.8

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

   4. Applied rewrites 57.8%

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

      1. lift-neg.f64 N/A

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

      2. lift-log.f64 N/A

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

      3. lift-neg.f64 N/A

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

      4. remove-double-neg N/A

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

      5. lift-log.f64 57.8

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

   6. Applied rewrites 57.8%

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

Alternative 5: 89.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.45 \cdot 10^{+34}:\\ \;\;\;\;\left(x - \log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;y - \mathsf{fma}\left(\log y, y, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.45e+34) (- (- x (log (sqrt y))) z) (- y (fma (log y) y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.45e+34) {
		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 <= 1.45e+34)
		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, 1.45e+34], 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 < 1.4500000000000001e34

   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

      \[\leadsto \color{blue}{\left(x - \frac{1}{2} \cdot \log y\right)} - z \]
   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 70.9

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

   4. Applied rewrites 70.9%

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

   if 1.4500000000000001e34 < y

   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 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 70.1

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

   4. Applied rewrites 70.1%

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

   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 57.8%

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

Alternative 6: 84.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 4.4 \cdot 10^{+148}:\\ \;\;\;\;\left(x - \log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 4.4e+148) (- (- x (log (sqrt y))) z) (* (- 1.0 (log y)) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 4.4e+148) {
		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 <= 4.4d+148) 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 <= 4.4e+148) {
		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 <= 4.4e+148:
		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 <= 4.4e+148)
		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 <= 4.4e+148)
		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, 4.4e+148], 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 < 4.3999999999999998e148

   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

      \[\leadsto \color{blue}{\left(x - \frac{1}{2} \cdot \log y\right)} - z \]
   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 70.9

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

   4. Applied rewrites 70.9%

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

   if 4.3999999999999998e148 < y

   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 inf

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

   4. Applied rewrites 87.0%

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

   5. Taylor expanded in y around inf

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

      1. neg-log N/A

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

      2. mul-1-neg N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. remove-double-neg N/A

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

      6. lower--.f64 N/A

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

      7. lift-log.f64 30.4

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

   7. Applied rewrites 30.4%

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

Alternative 7: 74.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x - \left(y + 0.5\right) \cdot \log y\right) + y\\ \mathbf{if}\;t\_0 \leq -5 \cdot 10^{+174}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \mathbf{elif}\;t\_0 \leq -5 \cdot 10^{+44}:\\ \;\;\;\;x - z\\ \mathbf{elif}\;t\_0 \leq 340:\\ \;\;\;\;\left(-\log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ (- x (* (+ y 0.5) (log y))) y)))
   (if (<= t_0 -5e+174)
     (* (- 1.0 (log y)) y)
     (if (<= t_0 -5e+44)
       (- x z)
       (if (<= t_0 340.0) (- (- (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 <= -5e+174) {
		tmp = (1.0 - log(y)) * y;
	} else if (t_0 <= -5e+44) {
		tmp = x - z;
	} else if (t_0 <= 340.0) {
		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 <= (-5d+174)) then
        tmp = (1.0d0 - log(y)) * y
    else if (t_0 <= (-5d+44)) then
        tmp = x - z
    else if (t_0 <= 340.0d0) 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 <= -5e+174) {
		tmp = (1.0 - Math.log(y)) * y;
	} else if (t_0 <= -5e+44) {
		tmp = x - z;
	} else if (t_0 <= 340.0) {
		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 <= -5e+174:
		tmp = (1.0 - math.log(y)) * y
	elif t_0 <= -5e+44:
		tmp = x - z
	elif t_0 <= 340.0:
		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 <= -5e+174)
		tmp = Float64(Float64(1.0 - log(y)) * y);
	elseif (t_0 <= -5e+44)
		tmp = Float64(x - z);
	elseif (t_0 <= 340.0)
		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 <= -5e+174)
		tmp = (1.0 - log(y)) * y;
	elseif (t_0 <= -5e+44)
		tmp = x - z;
	elseif (t_0 <= 340.0)
		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, -5e+174], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[t$95$0, -5e+44], N[(x - z), $MachinePrecision], If[LessEqual[t$95$0, 340.0], 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) < -4.9999999999999997e174

   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 inf

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

   4. Applied rewrites 87.0%

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

   5. Taylor expanded in y around inf

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

      1. neg-log N/A

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

      2. mul-1-neg N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. remove-double-neg N/A

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

      6. lower--.f64 N/A

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

      7. lift-log.f64 30.4

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

   7. Applied rewrites 30.4%

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

   if -4.9999999999999997e174 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < -4.9999999999999996e44 or 340 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y)

   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 58.1%

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

   if -4.9999999999999996e44 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < 340

   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

      \[\leadsto \color{blue}{\left(x - \frac{1}{2} \cdot \log y\right)} - z \]
   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 70.9

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

   4. Applied rewrites 70.9%

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

   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. pow1/2 N/A

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

      3. log-pow-rev N/A

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

      4. lower-neg.f64 N/A

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

      5. log-pow-rev N/A

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

      6. pow1/2 N/A

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

      7. lift-sqrt.f64 N/A

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

      8. lift-log.f64 42.0

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

   7. Applied rewrites 42.0%

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

Alternative 8: 70.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -205:\\ \;\;\;\;x - z\\ \mathbf{elif}\;x \leq 1150000:\\ \;\;\;\;\left(-\log \left(\sqrt{y}\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -205.0)
   (- x z)
   (if (<= x 1150000.0) (- (- (log (sqrt y))) z) (- x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -205.0) {
		tmp = x - z;
	} else if (x <= 1150000.0) {
		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) :: tmp
    if (x <= (-205.0d0)) then
        tmp = x - z
    else if (x <= 1150000.0d0) 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 tmp;
	if (x <= -205.0) {
		tmp = x - z;
	} else if (x <= 1150000.0) {
		tmp = -Math.log(Math.sqrt(y)) - z;
	} else {
		tmp = x - z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -205.0:
		tmp = x - z
	elif x <= 1150000.0:
		tmp = -math.log(math.sqrt(y)) - z
	else:
		tmp = x - z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -205.0)
		tmp = Float64(x - z);
	elseif (x <= 1150000.0)
		tmp = Float64(Float64(-log(sqrt(y))) - z);
	else
		tmp = Float64(x - z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -205.0)
		tmp = x - z;
	elseif (x <= 1150000.0)
		tmp = -log(sqrt(y)) - z;
	else
		tmp = x - z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -205.0], N[(x - z), $MachinePrecision], If[LessEqual[x, 1150000.0], N[((-N[Log[N[Sqrt[y], $MachinePrecision]], $MachinePrecision]) - z), $MachinePrecision], N[(x - z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -205 or 1.15e6 < x

   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 58.1%

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

   if -205 < x < 1.15e6

   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

      \[\leadsto \color{blue}{\left(x - \frac{1}{2} \cdot \log y\right)} - z \]
   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 70.9

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

   4. Applied rewrites 70.9%

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

   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. pow1/2 N/A

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

      3. log-pow-rev N/A

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

      4. lower-neg.f64 N/A

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

      5. log-pow-rev N/A

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

      6. pow1/2 N/A

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

      7. lift-sqrt.f64 N/A

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

      8. lift-log.f64 42.0

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

   7. Applied rewrites 42.0%

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

Alternative 9: 58.1% 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 58.1%

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

Alternative 10: 48.3% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1 \cdot 10^{+120}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 8.8 \cdot 10^{+15}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1e+120) x (if (<= x 8.8e+15) (- z) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1e+120) {
		tmp = x;
	} else if (x <= 8.8e+15) {
		tmp = -z;
	} else {
		tmp = 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, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-1d+120)) then
        tmp = x
    else if (x <= 8.8d+15) then
        tmp = -z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -1e+120) {
		tmp = x;
	} else if (x <= 8.8e+15) {
		tmp = -z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1e+120:
		tmp = x
	elif x <= 8.8e+15:
		tmp = -z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1e+120)
		tmp = x;
	elseif (x <= 8.8e+15)
		tmp = Float64(-z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1e+120)
		tmp = x;
	elseif (x <= 8.8e+15)
		tmp = -z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1e+120], x, If[LessEqual[x, 8.8e+15], (-z), x]]

Derivation

1. Split input into 2 regimes

2. if x < -9.9999999999999998e119 or 8.8e15 < x

   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 30.7%

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

   if -9.9999999999999998e119 < x < 8.8e15

   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 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 29.4

         \[\leadsto -z \]

   4. Applied rewrites 29.4%

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

Alternative 11: 30.7% 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 30.7%

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

Reproduce

herbie shell --seed 2025123 
(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))