Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2

Percentage Accurate: 99.8% → 99.8%

Time: 36.7s

Alternatives: 10

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (x y z)
  :precision binary64
  (- (+ (- x (* (+ y 1/2) (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 + 1/2), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z)
  :precision binary64
  (- (+ (- x (* (+ y 1/2) (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 + 1/2), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z)
  :precision binary64
  (- (- (+ y x) z) (* (log y) (- y -1/2))))

C

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

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 = ((y + x) - z) - (log(y) * (y - (-0.5d0)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := N[(N[(N[(y + x), $MachinePrecision] - z), $MachinePrecision] - N[(N[Log[y], $MachinePrecision] * N[(y - -1/2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

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

   1. lift--.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. associate--l+ N/A

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

   4. +-commutative N/A

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

   5. lift--.f64 N/A

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

   6. associate-+r- N/A

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

   7. lower--.f64 N/A

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

   8. sub-flip N/A

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

   9. +-commutative N/A

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

   10. associate-+l+ N/A

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

   11. +-commutative N/A

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

   12. associate-+r+ N/A

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

   13. remove-double-neg N/A

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

   14. distribute-neg-in N/A

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

   15. sub-flip N/A

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

   16. sub-negate-rev N/A

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

   17. associate--r- N/A

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

   18. sub-flip-reverse N/A

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

   19. sub-negate-rev N/A

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

   20. associate-+r- N/A

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

   21. +-commutative N/A

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

   22. lower--.f64 N/A

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

   23. lower-+.f64 99.8%

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

   24. lift-*.f64 N/A

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

   25. *-commutative N/A

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

   26. lower-*.f64 99.8%

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

3. Applied rewrites 99.8%

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

Alternative 2: 89.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032:\\ \;\;\;\;\left(y - z\right) - \log y \cdot \left(y - \frac{-1}{2}\right)\\ \mathbf{elif}\;z \leq 12000000000000000092156844732391635628802393190991881159827561381888:\\ \;\;\;\;\left(x + y\right) - \log y \cdot \left(\frac{1}{2} + y\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(z + \frac{1}{2} \cdot \log y\right)\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  (if (<=
     z
     -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032)
  (- (- y z) (* (log y) (- y -1/2)))
  (if (<=
       z
       12000000000000000092156844732391635628802393190991881159827561381888)
    (- (+ x y) (* (log y) (+ 1/2 y)))
    (- x (+ z (* 1/2 (log y)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -4.8e+94) {
		tmp = (y - z) - (log(y) * (y - -0.5));
	} else if (z <= 1.2e+67) {
		tmp = (x + y) - (log(y) * (0.5 + y));
	} else {
		tmp = x - (z + (0.5 * log(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 (z <= (-4.8d+94)) then
        tmp = (y - z) - (log(y) * (y - (-0.5d0)))
    else if (z <= 1.2d+67) then
        tmp = (x + y) - (log(y) * (0.5d0 + y))
    else
        tmp = x - (z + (0.5d0 * log(y)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -4.8e+94) {
		tmp = (y - z) - (Math.log(y) * (y - -0.5));
	} else if (z <= 1.2e+67) {
		tmp = (x + y) - (Math.log(y) * (0.5 + y));
	} else {
		tmp = x - (z + (0.5 * Math.log(y)));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -4.8e+94:
		tmp = (y - z) - (math.log(y) * (y - -0.5))
	elif z <= 1.2e+67:
		tmp = (x + y) - (math.log(y) * (0.5 + y))
	else:
		tmp = x - (z + (0.5 * math.log(y)))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -4.8e+94)
		tmp = Float64(Float64(y - z) - Float64(log(y) * Float64(y - -0.5)));
	elseif (z <= 1.2e+67)
		tmp = Float64(Float64(x + y) - Float64(log(y) * Float64(0.5 + y)));
	else
		tmp = Float64(x - Float64(z + Float64(0.5 * log(y))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -4.8e+94)
		tmp = (y - z) - (log(y) * (y - -0.5));
	elseif (z <= 1.2e+67)
		tmp = (x + y) - (log(y) * (0.5 + y));
	else
		tmp = x - (z + (0.5 * log(y)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032], N[(N[(y - z), $MachinePrecision] - N[(N[Log[y], $MachinePrecision] * N[(y - -1/2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 12000000000000000092156844732391635628802393190991881159827561381888], N[(N[(x + y), $MachinePrecision] - N[(N[Log[y], $MachinePrecision] * N[(1/2 + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(z + N[(1/2 * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -4.7999999999999997e94

   1. Initial program 99.8%

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

      1. lift--.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate--l+ N/A

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

      4. +-commutative N/A

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

      5. lift--.f64 N/A

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

      6. associate-+r- N/A

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

      7. lower--.f64 N/A

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

      8. sub-flip N/A

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

      9. +-commutative N/A

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

      10. associate-+l+ N/A

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

      11. +-commutative N/A

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

      12. associate-+r+ N/A

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

      13. remove-double-neg N/A

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

      14. distribute-neg-in N/A

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

      15. sub-flip N/A

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

      16. sub-negate-rev N/A

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

      17. associate--r- N/A

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

      18. sub-flip-reverse N/A

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

      19. sub-negate-rev N/A

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

      20. associate-+r- N/A

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

      21. +-commutative N/A

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

      22. lower--.f64 N/A

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

      23. lower-+.f64 99.8%

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

      24. lift-*.f64 N/A

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

      25. *-commutative N/A

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

      26. lower-*.f64 99.8%

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 71.9%

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

   if -4.7999999999999997e94 < z < 1.2e67

   1. Initial program 99.8%

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

      1. lift--.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate--l+ N/A

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

      4. +-commutative N/A

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

      5. lift--.f64 N/A

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

      6. associate-+r- N/A

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

      7. lower--.f64 N/A

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

      8. sub-flip N/A

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

      9. +-commutative N/A

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

      10. associate-+l+ N/A

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

      11. +-commutative N/A

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

      12. associate-+r+ N/A

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

      13. remove-double-neg N/A

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

      14. distribute-neg-in N/A

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

      15. sub-flip N/A

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

      16. sub-negate-rev N/A

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

      17. associate--r- N/A

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

      18. sub-flip-reverse N/A

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

      19. sub-negate-rev N/A

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

      20. associate-+r- N/A

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

      21. +-commutative N/A

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

      22. lower--.f64 N/A

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

      23. lower-+.f64 99.8%

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

      24. lift-*.f64 N/A

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

      25. *-commutative N/A

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

      26. lower-*.f64 99.8%

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 71.9%

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

   7. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\left(x + y\right) - \log y \cdot \left(\frac{1}{2} + y\right)} \]
   8. 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. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-log.f64 N/A

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

      5. lower-+.f64 71.0%

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

   9. Applied rewrites 71.0%

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

   if 1.2e67 < z

   1. Initial program 99.8%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-log.f64 69.6%

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

   4. Applied rewrites 69.6%

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

Alternative 3: 89.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_0 := \log y \cdot \left(\frac{1}{2} + y\right)\\ \mathbf{if}\;z \leq -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032:\\ \;\;\;\;\left(y - t\_0\right) - z\\ \mathbf{elif}\;z \leq 12000000000000000092156844732391635628802393190991881159827561381888:\\ \;\;\;\;\left(x + y\right) - t\_0\\ \mathbf{else}:\\ \;\;\;\;x - \left(z + \frac{1}{2} \cdot \log y\right)\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  (let* ((t_0 (* (log y) (+ 1/2 y))))
  (if (<=
       z
       -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032)
    (- (- y t_0) z)
    (if (<=
         z
         12000000000000000092156844732391635628802393190991881159827561381888)
      (- (+ x y) t_0)
      (- x (+ z (* 1/2 (log y))))))))

C

double code(double x, double y, double z) {
	double t_0 = log(y) * (0.5 + y);
	double tmp;
	if (z <= -4.8e+94) {
		tmp = (y - t_0) - z;
	} else if (z <= 1.2e+67) {
		tmp = (x + y) - t_0;
	} else {
		tmp = x - (z + (0.5 * log(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) :: t_0
    real(8) :: tmp
    t_0 = log(y) * (0.5d0 + y)
    if (z <= (-4.8d+94)) then
        tmp = (y - t_0) - z
    else if (z <= 1.2d+67) then
        tmp = (x + y) - t_0
    else
        tmp = x - (z + (0.5d0 * log(y)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = Math.log(y) * (0.5 + y);
	double tmp;
	if (z <= -4.8e+94) {
		tmp = (y - t_0) - z;
	} else if (z <= 1.2e+67) {
		tmp = (x + y) - t_0;
	} else {
		tmp = x - (z + (0.5 * Math.log(y)));
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = math.log(y) * (0.5 + y)
	tmp = 0
	if z <= -4.8e+94:
		tmp = (y - t_0) - z
	elif z <= 1.2e+67:
		tmp = (x + y) - t_0
	else:
		tmp = x - (z + (0.5 * math.log(y)))
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(log(y) * Float64(0.5 + y))
	tmp = 0.0
	if (z <= -4.8e+94)
		tmp = Float64(Float64(y - t_0) - z);
	elseif (z <= 1.2e+67)
		tmp = Float64(Float64(x + y) - t_0);
	else
		tmp = Float64(x - Float64(z + Float64(0.5 * log(y))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = log(y) * (0.5 + y);
	tmp = 0.0;
	if (z <= -4.8e+94)
		tmp = (y - t_0) - z;
	elseif (z <= 1.2e+67)
		tmp = (x + y) - t_0;
	else
		tmp = x - (z + (0.5 * log(y)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[Log[y], $MachinePrecision] * N[(1/2 + y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032], N[(N[(y - t$95$0), $MachinePrecision] - z), $MachinePrecision], If[LessEqual[z, 12000000000000000092156844732391635628802393190991881159827561381888], N[(N[(x + y), $MachinePrecision] - t$95$0), $MachinePrecision], N[(x - N[(z + N[(1/2 * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if z < -4.7999999999999997e94

   1. Initial program 99.8%

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

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 71.9%

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

   4. Applied rewrites 71.9%

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

   if -4.7999999999999997e94 < z < 1.2e67

   1. Initial program 99.8%

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

      1. lift--.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate--l+ N/A

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

      4. +-commutative N/A

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

      5. lift--.f64 N/A

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

      6. associate-+r- N/A

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

      7. lower--.f64 N/A

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

      8. sub-flip N/A

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

      9. +-commutative N/A

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

      10. associate-+l+ N/A

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

      11. +-commutative N/A

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

      12. associate-+r+ N/A

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

      13. remove-double-neg N/A

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

      14. distribute-neg-in N/A

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

      15. sub-flip N/A

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

      16. sub-negate-rev N/A

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

      17. associate--r- N/A

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

      18. sub-flip-reverse N/A

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

      19. sub-negate-rev N/A

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

      20. associate-+r- N/A

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

      21. +-commutative N/A

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

      22. lower--.f64 N/A

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

      23. lower-+.f64 99.8%

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

      24. lift-*.f64 N/A

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

      25. *-commutative N/A

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

      26. lower-*.f64 99.8%

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 71.9%

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

   7. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\left(x + y\right) - \log y \cdot \left(\frac{1}{2} + y\right)} \]
   8. 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. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-log.f64 N/A

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

      5. lower-+.f64 71.0%

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

   9. Applied rewrites 71.0%

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

   if 1.2e67 < z

   1. Initial program 99.8%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-log.f64 69.6%

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

   4. Applied rewrites 69.6%

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

Alternative 4: 88.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_0 := x - \left(z + \frac{1}{2} \cdot \log y\right)\\ \mathbf{if}\;z \leq -800000000000000024645330584772205526216201632061146770768717952555311882330651492352:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 12000000000000000092156844732391635628802393190991881159827561381888:\\ \;\;\;\;\left(x + y\right) - \log y \cdot \left(\frac{1}{2} + y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  (let* ((t_0 (- x (+ z (* 1/2 (log y))))))
  (if (<=
       z
       -800000000000000024645330584772205526216201632061146770768717952555311882330651492352)
    t_0
    (if (<=
         z
         12000000000000000092156844732391635628802393190991881159827561381888)
      (- (+ x y) (* (log y) (+ 1/2 y)))
      t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x - (z + (0.5 * log(y)));
	double tmp;
	if (z <= -8e+83) {
		tmp = t_0;
	} else if (z <= 1.2e+67) {
		tmp = (x + y) - (log(y) * (0.5 + y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, 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 - (z + (0.5d0 * log(y)))
    if (z <= (-8d+83)) then
        tmp = t_0
    else if (z <= 1.2d+67) then
        tmp = (x + y) - (log(y) * (0.5d0 + y))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = x - (z + (0.5 * Math.log(y)));
	double tmp;
	if (z <= -8e+83) {
		tmp = t_0;
	} else if (z <= 1.2e+67) {
		tmp = (x + y) - (Math.log(y) * (0.5 + y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x - (z + (0.5 * math.log(y)))
	tmp = 0
	if z <= -8e+83:
		tmp = t_0
	elif z <= 1.2e+67:
		tmp = (x + y) - (math.log(y) * (0.5 + y))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x - Float64(z + Float64(0.5 * log(y))))
	tmp = 0.0
	if (z <= -8e+83)
		tmp = t_0;
	elseif (z <= 1.2e+67)
		tmp = Float64(Float64(x + y) - Float64(log(y) * Float64(0.5 + y)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x - (z + (0.5 * log(y)));
	tmp = 0.0;
	if (z <= -8e+83)
		tmp = t_0;
	elseif (z <= 1.2e+67)
		tmp = (x + y) - (log(y) * (0.5 + y));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x - N[(z + N[(1/2 * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -800000000000000024645330584772205526216201632061146770768717952555311882330651492352], t$95$0, If[LessEqual[z, 12000000000000000092156844732391635628802393190991881159827561381888], N[(N[(x + y), $MachinePrecision] - N[(N[Log[y], $MachinePrecision] * N[(1/2 + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -8.0000000000000002e83 or 1.2e67 < z

   1. Initial program 99.8%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-log.f64 69.6%

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

   4. Applied rewrites 69.6%

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

   if -8.0000000000000002e83 < z < 1.2e67

   1. Initial program 99.8%

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

      1. lift--.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate--l+ N/A

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

      4. +-commutative N/A

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

      5. lift--.f64 N/A

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

      6. associate-+r- N/A

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

      7. lower--.f64 N/A

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

      8. sub-flip N/A

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

      9. +-commutative N/A

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

      10. associate-+l+ N/A

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

      11. +-commutative N/A

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

      12. associate-+r+ N/A

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

      13. remove-double-neg N/A

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

      14. distribute-neg-in N/A

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

      15. sub-flip N/A

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

      16. sub-negate-rev N/A

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

      17. associate--r- N/A

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

      18. sub-flip-reverse N/A

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

      19. sub-negate-rev N/A

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

      20. associate-+r- N/A

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

      21. +-commutative N/A

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

      22. lower--.f64 N/A

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

      23. lower-+.f64 99.8%

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

      24. lift-*.f64 N/A

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

      25. *-commutative N/A

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

      26. lower-*.f64 99.8%

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 71.9%

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

   7. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\left(x + y\right) - \log y \cdot \left(\frac{1}{2} + y\right)} \]
   8. 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. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-log.f64 N/A

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

      5. lower-+.f64 71.0%

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

   9. Applied rewrites 71.0%

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

Alternative 5: 69.6% accurate, 1.1× speedup

Math

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

FPCore

(FPCore (x y z)
  :precision binary64
  (- x (+ z (* 1/2 (log y)))))

C

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

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 + (0.5d0 * log(y)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := N[(x - N[(z + N[(1/2 * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

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

2. Taylor expanded in y around 0

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

   1. lower--.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-log.f64 69.6%

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

4. Applied rewrites 69.6%

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

Alternative 6: 69.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} t_0 := \left(1 - \frac{z}{x}\right) \cdot x\\ \mathbf{if}\;x \leq -780000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq \frac{3602879701896397}{281474976710656}:\\ \;\;\;\;\frac{-1}{2} \cdot \log y - z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  (let* ((t_0 (* (- 1 (/ z x)) x)))
  (if (<= x -780000000)
    t_0
    (if (<= x 3602879701896397/281474976710656)
      (- (* -1/2 (log y)) z)
      t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (1.0 - (z / x)) * x;
	double tmp;
	if (x <= -780000000.0) {
		tmp = t_0;
	} else if (x <= 12.8) {
		tmp = (-0.5 * log(y)) - z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, 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 = (1.0d0 - (z / x)) * x
    if (x <= (-780000000.0d0)) then
        tmp = t_0
    else if (x <= 12.8d0) then
        tmp = ((-0.5d0) * log(y)) - z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (1.0 - (z / x)) * x;
	double tmp;
	if (x <= -780000000.0) {
		tmp = t_0;
	} else if (x <= 12.8) {
		tmp = (-0.5 * Math.log(y)) - z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (1.0 - (z / x)) * x
	tmp = 0
	if x <= -780000000.0:
		tmp = t_0
	elif x <= 12.8:
		tmp = (-0.5 * math.log(y)) - z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(1.0 - Float64(z / x)) * x)
	tmp = 0.0
	if (x <= -780000000.0)
		tmp = t_0;
	elseif (x <= 12.8)
		tmp = Float64(Float64(-0.5 * log(y)) - z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (1.0 - (z / x)) * x;
	tmp = 0.0;
	if (x <= -780000000.0)
		tmp = t_0;
	elseif (x <= 12.8)
		tmp = (-0.5 * log(y)) - z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(1 - N[(z / x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -780000000], t$95$0, If[LessEqual[x, 3602879701896397/281474976710656], N[(N[(-1/2 * N[Log[y], $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -7.8e8 or 12.800000000000001 < x

   1. Initial program 99.8%

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

      1. lift--.f64 N/A

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

      2. sub-flip N/A

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

      3. lift-+.f64 N/A

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

      4. lift--.f64 N/A

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

      5. associate-+l- N/A

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

      6. associate-+l- N/A

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

      7. sub-to-mult N/A

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

      8. lower-unsound-*.f64 N/A

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

   3. Applied rewrites 79.2%

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

   4. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 59.7%

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

   6. Applied rewrites 59.7%

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

   7. Taylor expanded in z around inf

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

      1. lower-/.f64 47.2%

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

   9. Applied rewrites 47.2%

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

   if -7.8e8 < x < 12.800000000000001

   1. Initial program 99.8%

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

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 71.9%

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

   4. Applied rewrites 71.9%

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

   5. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

         \[\leadsto \frac{-1}{2} \cdot \log y - z \]

      2. lower-log.f64 42.4%

         \[\leadsto \frac{-1}{2} \cdot \log y - z \]

   7. Applied rewrites 42.4%

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

Alternative 7: 56.8% accurate, 3.7× speedup

Math

\[\begin{array}{l} t_0 := \left(1 - \frac{z}{x}\right) \cdot x\\ \mathbf{if}\;x \leq -115000000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq \frac{616761790044201}{21267647932558653966460912964485513216}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  (let* ((t_0 (* (- 1 (/ z x)) x)))
  (if (<= x -115000000)
    t_0
    (if (<= x 616761790044201/21267647932558653966460912964485513216)
      (- z)
      t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (1.0 - (z / x)) * x;
	double tmp;
	if (x <= -115000000.0) {
		tmp = t_0;
	} else if (x <= 2.9e-23) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, 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 = (1.0d0 - (z / x)) * x
    if (x <= (-115000000.0d0)) then
        tmp = t_0
    else if (x <= 2.9d-23) then
        tmp = -z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (1.0 - (z / x)) * x;
	double tmp;
	if (x <= -115000000.0) {
		tmp = t_0;
	} else if (x <= 2.9e-23) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (1.0 - (z / x)) * x
	tmp = 0
	if x <= -115000000.0:
		tmp = t_0
	elif x <= 2.9e-23:
		tmp = -z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(1.0 - Float64(z / x)) * x)
	tmp = 0.0
	if (x <= -115000000.0)
		tmp = t_0;
	elseif (x <= 2.9e-23)
		tmp = Float64(-z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (1.0 - (z / x)) * x;
	tmp = 0.0;
	if (x <= -115000000.0)
		tmp = t_0;
	elseif (x <= 2.9e-23)
		tmp = -z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(1 - N[(z / x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -115000000], t$95$0, If[LessEqual[x, 616761790044201/21267647932558653966460912964485513216], (-z), t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.15e8 or 2.9000000000000002e-23 < x

   1. Initial program 99.8%

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

      1. lift--.f64 N/A

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

      2. sub-flip N/A

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

      3. lift-+.f64 N/A

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

      4. lift--.f64 N/A

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

      5. associate-+l- N/A

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

      6. associate-+l- N/A

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

      7. sub-to-mult N/A

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

      8. lower-unsound-*.f64 N/A

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

   3. Applied rewrites 79.2%

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

   4. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 59.7%

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

   6. Applied rewrites 59.7%

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

   7. Taylor expanded in z around inf

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

      1. lower-/.f64 47.2%

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

   9. Applied rewrites 47.2%

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

   if -1.15e8 < x < 2.9000000000000002e-23

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 30.0%

         \[\leadsto -1 \cdot \color{blue}{z} \]

   4. Applied rewrites 30.0%

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

      1. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{z} \]

      2. mul-1-neg N/A

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

      3. lower-neg.f64 30.0%

         \[\leadsto -z \]

   6. Applied rewrites 30.0%

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

Alternative 8: 46.7% accurate, 6.5× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 400000000000000006223766451786737209707280558768424573347908232172333512465902281305994155966018979070482512347136000:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  (if (<=
     z
     -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032)
  (- z)
  (if (<=
       z
       400000000000000006223766451786737209707280558768424573347908232172333512465902281305994155966018979070482512347136000)
    (* 1 x)
    (- z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -4.8e+94) {
		tmp = -z;
	} else if (z <= 4e+116) {
		tmp = 1.0 * 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 <= (-4.8d+94)) then
        tmp = -z
    else if (z <= 4d+116) then
        tmp = 1.0d0 * 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 <= -4.8e+94) {
		tmp = -z;
	} else if (z <= 4e+116) {
		tmp = 1.0 * x;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -4.8e+94:
		tmp = -z
	elif z <= 4e+116:
		tmp = 1.0 * x
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -4.8e+94)
		tmp = Float64(-z);
	elseif (z <= 4e+116)
		tmp = Float64(1.0 * x);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -4.8e+94)
		tmp = -z;
	elseif (z <= 4e+116)
		tmp = 1.0 * x;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -47999999999999996524089971390543551186526799180673510009045877937386533124740660945353317548032], (-z), If[LessEqual[z, 400000000000000006223766451786737209707280558768424573347908232172333512465902281305994155966018979070482512347136000], N[(1 * x), $MachinePrecision], (-z)]]

Derivation

1. Split input into 2 regimes

2. if z < -4.7999999999999997e94 or 4.0000000000000001e116 < z

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 30.0%

         \[\leadsto -1 \cdot \color{blue}{z} \]

   4. Applied rewrites 30.0%

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

      1. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{z} \]

      2. mul-1-neg N/A

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

      3. lower-neg.f64 30.0%

         \[\leadsto -z \]

   6. Applied rewrites 30.0%

      \[\leadsto -z \]

   if -4.7999999999999997e94 < z < 4.0000000000000001e116

   1. Initial program 99.8%

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

      1. lift--.f64 N/A

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

      2. sub-flip N/A

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

      3. lift-+.f64 N/A

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

      4. lift--.f64 N/A

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

      5. associate-+l- N/A

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

      6. associate-+l- N/A

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

      7. sub-to-mult N/A

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

      8. lower-unsound-*.f64 N/A

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

   3. Applied rewrites 79.2%

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

   4. Taylor expanded in x around inf

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

   6. Applied rewrites 29.0%

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

Alternative 9: 30.0% accurate, 39.3× speedup

Math

\[-z \]

FPCore

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

C

double code(double x, double y, double z) {
	return -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 = -z
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := (-z)

Derivation

1. Initial program 99.8%

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

2. Taylor expanded in z around inf

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

   1. lower-*.f64 30.0%

      \[\leadsto -1 \cdot \color{blue}{z} \]

4. Applied rewrites 30.0%

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

   1. lift-*.f64 N/A

      \[\leadsto -1 \cdot \color{blue}{z} \]

   2. mul-1-neg N/A

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

   3. lower-neg.f64 30.0%

      \[\leadsto -z \]

6. Applied rewrites 30.0%

   \[\leadsto -z \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2025271 -o generate:evaluate
(FPCore (x y z)
  :name "Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2"
  :precision binary64
  (- (+ (- x (* (+ y 1/2) (log y))) y) z))