Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2

Percentage Accurate: 99.8% → 99.9%

Time: 7.1s

Alternatives: 14

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(1 - \log y, y, \mathsf{fma}\left(-0.5, \log y, x\right)\right) - z \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (- (fma (- 1.0 (log y)) y (fma -0.5 (log y) x)) z))

C

double code(double x, double y, double z) {
	return fma((1.0 - log(y)), y, fma(-0.5, log(y), x)) - z;
}

Julia

function code(x, y, z)
	return Float64(fma(Float64(1.0 - log(y)), y, fma(-0.5, log(y), x)) - z)
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y + N[(-0.5 * N[Log[y], $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
2. Add Preprocessing
3. 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

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

   3. lift-*.f64 N/A

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

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

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

   5. associate-+l+ N/A

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

   6. lower-+.f64 N/A

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

   7. *-commutative N/A

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

   8. distribute-rgt-neg-in N/A

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

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

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

   10. lower-fma.f64 N/A

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

   11. lower-neg.f64 99.8

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

   12. lift-+.f64 N/A

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

   13. +-commutative N/A

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

   14. lower-+.f64 99.8

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

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\left(x + \mathsf{fma}\left(-\log y, 0.5 + y, y\right)\right)} - z \]
5. Step-by-step derivation

   1. lift-fma.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. distribute-lft-in N/A

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

   4. associate-+l+ N/A

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

   5. lower-fma.f64 N/A

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

   6. lower-fma.f64 99.8

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

6. Applied rewrites 99.8%

   \[\leadsto \left(x + \color{blue}{\mathsf{fma}\left(-\log y, 0.5, \mathsf{fma}\left(-\log y, y, y\right)\right)}\right) - z \]
7. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-fma.f64 N/A

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

   3. associate-+r+ N/A

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

   4. +-commutative N/A

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

   5. lift-fma.f64 N/A

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

   6. distribute-lft1-in N/A

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

   7. lower-fma.f64 N/A

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

   8. metadata-eval N/A

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

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

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

   10. metadata-eval N/A

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

   11. metadata-eval N/A

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

   12. lower--.f64 N/A

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

   13. lift-neg.f64 N/A

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

   14. fp-cancel-sub-sign N/A

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

   15. *-commutative N/A

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

   16. metadata-eval N/A

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

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

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

   18. +-commutative N/A

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

   19. lift-fma.f64 99.8

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

8. Applied rewrites 99.8%

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

9. Taylor expanded in y around 0

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

    1. lower--.f64 N/A

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

    2. lower-log.f64 99.8

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

11. Applied rewrites 99.8%

    \[\leadsto \mathsf{fma}\left(\color{blue}{1 - \log y}, y, \mathsf{fma}\left(-0.5, \log y, x\right)\right) - z \]
12. Add Preprocessing

Alternative 2: 75.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x - \left(y + 0.5\right) \cdot \log y\right) + y\\ \mathbf{if}\;t\_0 \leq -2 \cdot 10^{+295}:\\ \;\;\;\;\mathsf{fma}\left(-0.5 - y, \log y, x\right)\\ \mathbf{elif}\;t\_0 \leq -4.8 \cdot 10^{+188}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \mathbf{elif}\;t\_0 \leq -1 \cdot 10^{+24} \lor \neg \left(t\_0 \leq 500\right):\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;-0.5 \cdot \log y - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ (- x (* (+ y 0.5) (log y))) y)))
   (if (<= t_0 -2e+295)
     (fma (- -0.5 y) (log y) x)
     (if (<= t_0 -4.8e+188)
       (* (- 1.0 (log y)) y)
       (if (or (<= t_0 -1e+24) (not (<= t_0 500.0)))
         (- x z)
         (- (* -0.5 (log y)) 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 <= -2e+295) {
		tmp = fma((-0.5 - y), log(y), x);
	} else if (t_0 <= -4.8e+188) {
		tmp = (1.0 - log(y)) * y;
	} else if ((t_0 <= -1e+24) || !(t_0 <= 500.0)) {
		tmp = x - z;
	} else {
		tmp = (-0.5 * log(y)) - 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 <= -2e+295)
		tmp = fma(Float64(-0.5 - y), log(y), x);
	elseif (t_0 <= -4.8e+188)
		tmp = Float64(Float64(1.0 - log(y)) * y);
	elseif ((t_0 <= -1e+24) || !(t_0 <= 500.0))
		tmp = Float64(x - z);
	else
		tmp = Float64(Float64(-0.5 * log(y)) - z);
	end
	return 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, -2e+295], N[(N[(-0.5 - y), $MachinePrecision] * N[Log[y], $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t$95$0, -4.8e+188], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[Or[LessEqual[t$95$0, -1e+24], N[Not[LessEqual[t$95$0, 500.0]], $MachinePrecision]], N[(x - z), $MachinePrecision], N[(N[(-0.5 * N[Log[y], $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < -2e295

   1. Initial program 99.5%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

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

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. +-commutative N/A

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

      7. distribute-neg-in N/A

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

      8. metadata-eval N/A

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

      9. mul-1-neg N/A

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

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

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

      11. metadata-eval N/A

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

      12. *-lft-identity N/A

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

      13. lower--.f64 N/A

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

      14. lower-log.f64 N/A

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

      15. +-commutative N/A

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

      16. lower-+.f64 99.6

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

   5. Applied rewrites 99.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 70.6%

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

   if -2e295 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < -4.7999999999999999e188

   1. Initial program 99.6%

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

   3. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. mul-1-neg N/A

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

      3. log-rec N/A

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

      4. remove-double-neg N/A

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

      5. lower-*.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lower-log.f64 70.5

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

   5. Applied rewrites 70.5%

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

   if -4.7999999999999999e188 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < -9.9999999999999998e23 or 500 < (+.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. Add Preprocessing

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 74.3%

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

   if -9.9999999999999998e23 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < 500

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0

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

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

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

      2. +-commutative N/A

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

      3. metadata-eval N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-log.f64 91.4

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

   5. Applied rewrites 91.4%

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

   6. Taylor expanded in x around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 89.8

         \[\leadsto -0.5 \cdot \log y - z \]

   8. Applied rewrites 89.8%

      \[\leadsto -0.5 \cdot \color{blue}{\log y} - z \]
3. Recombined 4 regimes into one program.

4. Final simplification 77.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(x - \left(y + 0.5\right) \cdot \log y\right) + y \leq -2 \cdot 10^{+295}:\\ \;\;\;\;\mathsf{fma}\left(-0.5 - y, \log y, x\right)\\ \mathbf{elif}\;\left(x - \left(y + 0.5\right) \cdot \log y\right) + y \leq -4.8 \cdot 10^{+188}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \mathbf{elif}\;\left(x - \left(y + 0.5\right) \cdot \log y\right) + y \leq -1 \cdot 10^{+24} \lor \neg \left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y \leq 500\right):\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;-0.5 \cdot \log y - z\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 75.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x - \left(y + 0.5\right) \cdot \log y\right) + y\\ \mathbf{if}\;t\_0 \leq -4.8 \cdot 10^{+188}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \mathbf{elif}\;t\_0 \leq -1 \cdot 10^{+24} \lor \neg \left(t\_0 \leq 500\right):\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;-0.5 \cdot \log y - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ (- x (* (+ y 0.5) (log y))) y)))
   (if (<= t_0 -4.8e+188)
     (* (- 1.0 (log y)) y)
     (if (or (<= t_0 -1e+24) (not (<= t_0 500.0)))
       (- x z)
       (- (* -0.5 (log y)) 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 <= -4.8e+188) {
		tmp = (1.0 - log(y)) * y;
	} else if ((t_0 <= -1e+24) || !(t_0 <= 500.0)) {
		tmp = x - z;
	} else {
		tmp = (-0.5 * log(y)) - z;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x - ((y + 0.5d0) * log(y))) + y
    if (t_0 <= (-4.8d+188)) then
        tmp = (1.0d0 - log(y)) * y
    else if ((t_0 <= (-1d+24)) .or. (.not. (t_0 <= 500.0d0))) then
        tmp = x - z
    else
        tmp = ((-0.5d0) * log(y)) - 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 <= -4.8e+188) {
		tmp = (1.0 - Math.log(y)) * y;
	} else if ((t_0 <= -1e+24) || !(t_0 <= 500.0)) {
		tmp = x - z;
	} else {
		tmp = (-0.5 * Math.log(y)) - z;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x - ((y + 0.5) * math.log(y))) + y
	tmp = 0
	if t_0 <= -4.8e+188:
		tmp = (1.0 - math.log(y)) * y
	elif (t_0 <= -1e+24) or not (t_0 <= 500.0):
		tmp = x - z
	else:
		tmp = (-0.5 * math.log(y)) - 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 <= -4.8e+188)
		tmp = Float64(Float64(1.0 - log(y)) * y);
	elseif ((t_0 <= -1e+24) || !(t_0 <= 500.0))
		tmp = Float64(x - z);
	else
		tmp = Float64(Float64(-0.5 * log(y)) - 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 <= -4.8e+188)
		tmp = (1.0 - log(y)) * y;
	elseif ((t_0 <= -1e+24) || ~((t_0 <= 500.0)))
		tmp = x - z;
	else
		tmp = (-0.5 * log(y)) - 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, -4.8e+188], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], If[Or[LessEqual[t$95$0, -1e+24], N[Not[LessEqual[t$95$0, 500.0]], $MachinePrecision]], N[(x - z), $MachinePrecision], N[(N[(-0.5 * N[Log[y], $MachinePrecision]), $MachinePrecision] - 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) < -4.7999999999999999e188

   1. Initial program 99.6%

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

   3. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. mul-1-neg N/A

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

      3. log-rec N/A

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

      4. remove-double-neg N/A

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

      5. lower-*.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lower-log.f64 63.9

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

   5. Applied rewrites 63.9%

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

   if -4.7999999999999999e188 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < -9.9999999999999998e23 or 500 < (+.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. Add Preprocessing

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 74.3%

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

   if -9.9999999999999998e23 < (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) < 500

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0

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

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

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

      2. +-commutative N/A

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

      3. metadata-eval N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-log.f64 91.4

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

   5. Applied rewrites 91.4%

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

   6. Taylor expanded in x around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 89.8

         \[\leadsto -0.5 \cdot \log y - z \]

   8. Applied rewrites 89.8%

      \[\leadsto -0.5 \cdot \color{blue}{\log y} - z \]
3. Recombined 3 regimes into one program.

4. Final simplification 75.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(x - \left(y + 0.5\right) \cdot \log y\right) + y \leq -4.8 \cdot 10^{+188}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \mathbf{elif}\;\left(x - \left(y + 0.5\right) \cdot \log y\right) + y \leq -1 \cdot 10^{+24} \lor \neg \left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y \leq 500\right):\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;-0.5 \cdot \log y - z\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 88.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{+88} \lor \neg \left(z \leq 1.15 \cdot 10^{+127}\right):\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.5 - y, \log y, y\right) + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -5.8e+88) (not (<= z 1.15e+127)))
   (- x z)
   (+ (fma (- -0.5 y) (log y) y) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -5.8e+88) || !(z <= 1.15e+127)) {
		tmp = x - z;
	} else {
		tmp = fma((-0.5 - y), log(y), y) + x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -5.8e+88) || !(z <= 1.15e+127))
		tmp = Float64(x - z);
	else
		tmp = Float64(fma(Float64(-0.5 - y), log(y), y) + x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -5.8e+88], N[Not[LessEqual[z, 1.15e+127]], $MachinePrecision]], N[(x - z), $MachinePrecision], N[(N[(N[(-0.5 - y), $MachinePrecision] * N[Log[y], $MachinePrecision] + y), $MachinePrecision] + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -5.7999999999999999e88 or 1.1500000000000001e127 < z

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 88.1%

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

   if -5.7999999999999999e88 < z < 1.1500000000000001e127

   1. Initial program 99.7%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

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

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. +-commutative N/A

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

      7. distribute-neg-in N/A

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

      8. metadata-eval N/A

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

      9. mul-1-neg N/A

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

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

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

      11. metadata-eval N/A

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

      12. *-lft-identity N/A

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

      13. lower--.f64 N/A

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

      14. lower-log.f64 N/A

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

      15. +-commutative N/A

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

      16. lower-+.f64 94.7

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

   5. Applied rewrites 94.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5 - y, \log y, y + x\right)} \]
   6. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate-+r+ N/A

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

      4. lower-+.f64 N/A

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

      5. lower-fma.f64 94.8

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

   7. Applied rewrites 94.8%

      \[\leadsto \mathsf{fma}\left(-0.5 - y, \log y, y\right) + \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 92.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{+88} \lor \neg \left(z \leq 1.15 \cdot 10^{+127}\right):\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.5 - y, \log y, y\right) + x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 88.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{+88} \lor \neg \left(z \leq 1.15 \cdot 10^{+127}\right):\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.5 - y, \log y, x\right) + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -5.8e+88) (not (<= z 1.15e+127)))
   (- x z)
   (+ (fma (- -0.5 y) (log y) x) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -5.8e+88) || !(z <= 1.15e+127)) {
		tmp = x - z;
	} else {
		tmp = fma((-0.5 - y), log(y), x) + y;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -5.8e+88) || !(z <= 1.15e+127))
		tmp = Float64(x - z);
	else
		tmp = Float64(fma(Float64(-0.5 - y), log(y), x) + y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -5.8e+88], N[Not[LessEqual[z, 1.15e+127]], $MachinePrecision]], N[(x - z), $MachinePrecision], N[(N[(N[(-0.5 - y), $MachinePrecision] * N[Log[y], $MachinePrecision] + x), $MachinePrecision] + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -5.7999999999999999e88 or 1.1500000000000001e127 < z

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 88.1%

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

   if -5.7999999999999999e88 < z < 1.1500000000000001e127

   1. Initial program 99.7%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

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

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. +-commutative N/A

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

      7. distribute-neg-in N/A

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

      8. metadata-eval N/A

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

      9. mul-1-neg N/A

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

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

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

      11. metadata-eval N/A

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

      12. *-lft-identity N/A

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

      13. lower--.f64 N/A

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

      14. lower-log.f64 N/A

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

      15. +-commutative N/A

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

      16. lower-+.f64 94.7

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

   5. Applied rewrites 94.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5 - y, \log y, y + x\right)} \]
   6. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate-+r+ N/A

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

      5. lower-+.f64 N/A

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

      6. lower-fma.f64 94.7

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

   7. Applied rewrites 94.7%

      \[\leadsto \mathsf{fma}\left(-0.5 - y, \log y, x\right) + \color{blue}{y} \]
3. Recombined 2 regimes into one program.

4. Final simplification 92.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -5.8 \cdot 10^{+88} \lor \neg \left(z \leq 1.15 \cdot 10^{+127}\right):\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.5 - y, \log y, x\right) + y\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 0.28:\\ \;\;\;\;\mathsf{fma}\left(-0.5, \log y, x\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(\left(x - y \cdot \log y\right) + y\right) - z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 0.28) (- (fma -0.5 (log y) x) z) (- (+ (- x (* y (log y))) y) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 0.28) {
		tmp = fma(-0.5, log(y), x) - z;
	} else {
		tmp = ((x - (y * log(y))) + y) - z;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 0.28)
		tmp = Float64(fma(-0.5, log(y), x) - z);
	else
		tmp = Float64(Float64(Float64(x - Float64(y * log(y))) + y) - z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 0.28], N[(N[(-0.5 * N[Log[y], $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision], N[(N[(N[(x - N[(y * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 0.28000000000000003

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0

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

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

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

      2. +-commutative N/A

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

      3. metadata-eval N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-log.f64 98.7

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

   5. Applied rewrites 98.7%

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

   if 0.28000000000000003 < y

   1. Initial program 99.6%

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

   3. Taylor expanded in y around inf

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

   5. Applied rewrites 98.5%

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

Alternative 7: 89.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.65 \cdot 10^{+60}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, \log y, x\right) - z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-y, \log y, y\right) + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.65e+60) (- (fma -0.5 (log y) x) z) (+ (fma (- y) (log y) y) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.65e+60) {
		tmp = fma(-0.5, log(y), x) - z;
	} else {
		tmp = fma(-y, log(y), y) + x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 1.65e+60)
		tmp = Float64(fma(-0.5, log(y), x) - z);
	else
		tmp = Float64(fma(Float64(-y), log(y), y) + x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 1.65e+60], N[(N[(-0.5 * N[Log[y], $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision], N[(N[((-y) * N[Log[y], $MachinePrecision] + y), $MachinePrecision] + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.6499999999999999e60

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0

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

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

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

      2. +-commutative N/A

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

      3. metadata-eval N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-log.f64 91.7

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

   5. Applied rewrites 91.7%

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

   if 1.6499999999999999e60 < y

   1. Initial program 99.5%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

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

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. +-commutative N/A

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

      7. distribute-neg-in N/A

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

      8. metadata-eval N/A

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

      9. mul-1-neg N/A

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

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

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

      11. metadata-eval N/A

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

      12. *-lft-identity N/A

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

      13. lower--.f64 N/A

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

      14. lower-log.f64 N/A

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

      15. +-commutative N/A

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

      16. lower-+.f64 86.6

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

   5. Applied rewrites 86.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5 - y, \log y, y + x\right)} \]
   6. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-*.f64 86.5

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

   7. Applied rewrites 86.5%

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

   8. Taylor expanded in y around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 86.5

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

   10. Applied rewrites 86.5%

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

       1. lift-+.f64 N/A

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

       2. lift-+.f64 N/A

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

       3. associate-+l+ N/A

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

       4. +-commutative N/A

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

       5. lower-+.f64 N/A

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

       6. +-commutative N/A

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

       7. lift-*.f64 N/A

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

       8. lower-fma.f64 86.6

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

   12. Applied rewrites 86.6%

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

Alternative 8: 89.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.65 \cdot 10^{+60}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, \log y, x\right) - z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-y, \log y, x\right) + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 1.65e+60) (- (fma -0.5 (log y) x) z) (+ (fma (- y) (log y) x) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.65e+60) {
		tmp = fma(-0.5, log(y), x) - z;
	} else {
		tmp = fma(-y, log(y), x) + y;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 1.65e+60)
		tmp = Float64(fma(-0.5, log(y), x) - z);
	else
		tmp = Float64(fma(Float64(-y), log(y), x) + y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 1.65e+60], N[(N[(-0.5 * N[Log[y], $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision], N[(N[((-y) * N[Log[y], $MachinePrecision] + x), $MachinePrecision] + y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.6499999999999999e60

   1. Initial program 99.9%

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

   3. Taylor expanded in y around 0

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

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

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

      2. +-commutative N/A

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

      3. metadata-eval N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-log.f64 91.7

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

   5. Applied rewrites 91.7%

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

   if 1.6499999999999999e60 < y

   1. Initial program 99.5%

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

   3. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

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

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

      4. +-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. +-commutative N/A

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

      7. distribute-neg-in N/A

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

      8. metadata-eval N/A

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

      9. mul-1-neg N/A

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

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

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

      11. metadata-eval N/A

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

      12. *-lft-identity N/A

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

      13. lower--.f64 N/A

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

      14. lower-log.f64 N/A

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

      15. +-commutative N/A

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

      16. lower-+.f64 86.6

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

   5. Applied rewrites 86.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5 - y, \log y, y + x\right)} \]
   6. Step-by-step derivation

      1. lift-fma.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. lower-*.f64 86.5

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

   7. Applied rewrites 86.5%

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

   8. Taylor expanded in y around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 86.5

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

   10. Applied rewrites 86.5%

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

       1. lift-+.f64 N/A

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

       2. +-commutative N/A

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

       3. lift-+.f64 N/A

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

       4. associate-+r+ N/A

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

       5. lower-+.f64 N/A

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

       6. lift-*.f64 N/A

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

       7. lower-fma.f64 86.5

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

   12. Applied rewrites 86.5%

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

Alternative 9: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.7%

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
2. Add Preprocessing
3. 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

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

   3. lift-*.f64 N/A

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

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

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

   5. associate-+l+ N/A

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

   6. lower-+.f64 N/A

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

   7. *-commutative N/A

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

   8. distribute-rgt-neg-in N/A

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

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

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

   10. lower-fma.f64 N/A

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

   11. lower-neg.f64 99.8

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

   12. lift-+.f64 N/A

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

   13. +-commutative N/A

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

   14. lower-+.f64 99.8

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

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\left(x + \mathsf{fma}\left(-\log y, 0.5 + y, y\right)\right)} - z \]
5. Add Preprocessing

Alternative 10: 83.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 3.5 \cdot 10^{+182}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, \log y, x\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 3.5e+182) (- (fma -0.5 (log y) x) z) (* (- 1.0 (log y)) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 3.5e+182) {
		tmp = fma(-0.5, log(y), x) - z;
	} else {
		tmp = (1.0 - log(y)) * y;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 3.5e+182)
		tmp = Float64(fma(-0.5, log(y), x) - z);
	else
		tmp = Float64(Float64(1.0 - log(y)) * y);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 3.5e+182], N[(N[(-0.5 * N[Log[y], $MachinePrecision] + x), $MachinePrecision] - z), $MachinePrecision], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 3.50000000000000023e182

   1. Initial program 99.8%

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

   3. Taylor expanded in y around 0

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

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

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

      2. +-commutative N/A

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

      3. metadata-eval N/A

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

      4. lower-fma.f64 N/A

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

      5. lower-log.f64 83.1

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

   5. Applied rewrites 83.1%

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

   if 3.50000000000000023e182 < y

   1. Initial program 99.4%

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

   3. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. mul-1-neg N/A

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

      3. log-rec N/A

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

      4. remove-double-neg N/A

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

      5. lower-*.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lower-log.f64 85.6

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

   5. Applied rewrites 85.6%

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

Alternative 11: 70.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 3.5 \cdot 10^{+182}:\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 3.5e+182) (- x z) (* (- 1.0 (log y)) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 3.5e+182) {
		tmp = x - 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 <= 3.5d+182) then
        tmp = x - 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 <= 3.5e+182) {
		tmp = x - z;
	} else {
		tmp = (1.0 - Math.log(y)) * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 3.5e+182:
		tmp = x - z
	else:
		tmp = (1.0 - math.log(y)) * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 3.5e+182)
		tmp = Float64(x - 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 <= 3.5e+182)
		tmp = x - z;
	else
		tmp = (1.0 - log(y)) * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 3.5e+182], N[(x - z), $MachinePrecision], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 3.50000000000000023e182

   1. Initial program 99.8%

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

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 66.9%

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

   if 3.50000000000000023e182 < y

   1. Initial program 99.4%

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

   3. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. mul-1-neg N/A

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

      3. log-rec N/A

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

      4. remove-double-neg N/A

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

      5. lower-*.f64 N/A

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

      6. lower--.f64 N/A

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

      7. lower-log.f64 85.6

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

   5. Applied rewrites 85.6%

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

Alternative 12: 48.6% accurate, 7.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.35 \cdot 10^{+38}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 44000000000000:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -3.35e+38) x (if (<= x 44000000000000.0) (- z) x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -3.35e+38) {
		tmp = x;
	} else if (x <= 44000000000000.0) {
		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 <= (-3.35d+38)) then
        tmp = x
    else if (x <= 44000000000000.0d0) 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 <= -3.35e+38) {
		tmp = x;
	} else if (x <= 44000000000000.0) {
		tmp = -z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -3.35e+38:
		tmp = x
	elif x <= 44000000000000.0:
		tmp = -z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -3.35e+38)
		tmp = x;
	elseif (x <= 44000000000000.0)
		tmp = Float64(-z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -3.35e+38)
		tmp = x;
	elseif (x <= 44000000000000.0)
		tmp = -z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -3.35e+38], x, If[LessEqual[x, 44000000000000.0], (-z), x]]

Derivation

1. Split input into 2 regimes

2. if x < -3.35000000000000012e38 or 4.4e13 < x

   1. Initial program 99.8%

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

   3. Taylor expanded in x around inf

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

   5. Applied rewrites 63.9%

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

   if -3.35000000000000012e38 < x < 4.4e13

   1. Initial program 99.7%

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

   3. Taylor expanded in z around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 35.5

         \[\leadsto -z \]

   5. Applied rewrites 35.5%

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

Alternative 13: 58.3% accurate, 29.5× 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 99.7%

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

3. Taylor expanded in x around inf

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

5. Applied rewrites 55.7%

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

Alternative 14: 29.6% accurate, 118.0× 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 99.7%

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

3. Taylor expanded in x around inf

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

5. Applied rewrites 30.2%

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

Developer Target 1: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return ((y + x) - z) - ((y + 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 = ((y + x) - z) - ((y + 0.5d0) * log(y))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2025026 
(FPCore (x y z)
  :name "Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2"
  :precision binary64

  :alt
  (! :herbie-platform default (- (- (+ y x) z) (* (+ y 1/2) (log y))))

  (- (+ (- x (* (+ y 0.5) (log y))) y) z))