Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2

Percentage Accurate: 99.8% → 99.8%

Time: 3.6s

Alternatives: 10

Speedup: N/A×

Specification

Math

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

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

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.8%

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
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-negate-rev N/A

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

   3. lift-+.f64 N/A

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

   4. associate--r+ N/A

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

   5. sub-negate N/A

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

   6. lower--.f64 N/A

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

   7. sub-negate-rev N/A

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

   8. lift--.f64 N/A

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

   9. lift-*.f64 N/A

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

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

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

   11. +-commutative N/A

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

   12. associate--l+ N/A

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

   13. distribute-neg-in N/A

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

3. Applied rewrites 99.8%

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

Alternative 2: 89.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq -1.25 \cdot 10^{+122}:\\ \;\;\;\;y - \mathsf{fma}\left(\log y, y - -0.5, z\right)\\ \mathbf{elif}\;z \leq 1.8 \cdot 10^{+41}:\\ \;\;\;\;y - \left(\left(y - -0.5\right) \cdot \log y - x\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x - 0.5 \cdot \log y\right) - z\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1.25e+122)
   (- y (fma (log y) (- y -0.5) z))
   (if (<= z 1.8e+41)
     (- y (- (* (- y -0.5) (log y)) x))
     (- (- x (* 0.5 (log y))) z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.25e+122) {
		tmp = y - fma(log(y), (y - -0.5), z);
	} else if (z <= 1.8e+41) {
		tmp = y - (((y - -0.5) * log(y)) - x);
	} else {
		tmp = (x - (0.5 * log(y))) - z;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1.25e+122)
		tmp = Float64(y - fma(log(y), Float64(y - -0.5), z));
	elseif (z <= 1.8e+41)
		tmp = Float64(y - Float64(Float64(Float64(y - -0.5) * log(y)) - x));
	else
		tmp = Float64(Float64(x - Float64(0.5 * log(y))) - z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1.25e+122], N[(y - N[(N[Log[y], $MachinePrecision] * N[(y - -0.5), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.8e+41], N[(y - N[(N[(N[(y - -0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision], N[(N[(x - N[(0.5 * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.24999999999999997e122

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
   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-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

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

      6. lower--.f64 N/A

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

      7. sub-negate-rev N/A

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

      8. lift--.f64 N/A

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

      9. lift-*.f64 N/A

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

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

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

      11. +-commutative N/A

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

      12. associate--l+ N/A

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

      13. distribute-neg-in N/A

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 70.9%

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

   if -1.24999999999999997e122 < z < 1.80000000000000013e41

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
   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-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

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

      6. lower--.f64 N/A

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

      7. sub-negate-rev N/A

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

      8. lift--.f64 N/A

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

      9. lift-*.f64 N/A

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

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

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

      11. +-commutative N/A

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

      12. associate--l+ N/A

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

      13. distribute-neg-in N/A

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 71.1%

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

   6. Applied rewrites 71.1%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 71.1%

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. add-flip N/A

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

      7. metadata-eval N/A

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

      8. lower--.f64 71.1%

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

   8. Applied rewrites 71.1%

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

   if 1.80000000000000013e41 < z

   1. Initial program 99.8%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 70.5%

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

   4. Applied rewrites 70.5%

      \[\leadsto \color{blue}{\left(x - 0.5 \cdot \log y\right)} - z \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 88.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} t_0 := x \cdot 1 - z\\ \mathbf{if}\;x \leq -6.9 \cdot 10^{+44}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 4.2 \cdot 10^{+102}:\\ \;\;\;\;y - \mathsf{fma}\left(\log y, y - -0.5, z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- (* x 1.0) z)))
   (if (<= x -6.9e+44)
     t_0
     (if (<= x 4.2e+102) (- y (fma (log y) (- y -0.5) z)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (x * 1.0) - z;
	double tmp;
	if (x <= -6.9e+44) {
		tmp = t_0;
	} else if (x <= 4.2e+102) {
		tmp = y - fma(log(y), (y - -0.5), z);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x * 1.0) - z)
	tmp = 0.0
	if (x <= -6.9e+44)
		tmp = t_0;
	elseif (x <= 4.2e+102)
		tmp = Float64(y - fma(log(y), Float64(y - -0.5), z));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x * 1.0), $MachinePrecision] - z), $MachinePrecision]}, If[LessEqual[x, -6.9e+44], t$95$0, If[LessEqual[x, 4.2e+102], N[(y - N[(N[Log[y], $MachinePrecision] * N[(y - -0.5), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -6.8999999999999997e44 or 4.20000000000000003e102 < x

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower-+.f64 87.2%

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

   4. Applied rewrites 87.2%

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

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot 1 - z \]
   6. Step-by-step derivation

   7. Applied rewrites 57.9%

      \[\leadsto x \cdot 1 - z \]

   if -6.8999999999999997e44 < x < 4.20000000000000003e102

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
   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-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

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

      6. lower--.f64 N/A

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

      7. sub-negate-rev N/A

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

      8. lift--.f64 N/A

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

      9. lift-*.f64 N/A

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

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

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

      11. +-commutative N/A

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

      12. associate--l+ N/A

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

      13. distribute-neg-in N/A

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 70.9%

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

Alternative 4: 83.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \mathbf{if}\;y \leq 6.6 \cdot 10^{+168}:\\ \;\;\;\;\left(x - 0.5 \cdot \log y\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 6.6e+168) (- (- x (* 0.5 (log y))) z) (* (- 1.0 (log y)) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 6.6e+168) {
		tmp = (x - (0.5 * log(y))) - z;
	} else {
		tmp = (1.0 - log(y)) * y;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 6.6d+168) then
        tmp = (x - (0.5d0 * log(y))) - z
    else
        tmp = (1.0d0 - log(y)) * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 6.6e+168) {
		tmp = (x - (0.5 * Math.log(y))) - z;
	} else {
		tmp = (1.0 - Math.log(y)) * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 6.6e+168:
		tmp = (x - (0.5 * math.log(y))) - z
	else:
		tmp = (1.0 - math.log(y)) * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 6.6e+168)
		tmp = Float64(Float64(x - Float64(0.5 * log(y))) - z);
	else
		tmp = Float64(Float64(1.0 - log(y)) * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 6.6e+168)
		tmp = (x - (0.5 * log(y))) - z;
	else
		tmp = (1.0 - log(y)) * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 6.6e+168], N[(N[(x - N[(0.5 * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 6.5999999999999997e168

   1. Initial program 99.8%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 70.5%

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

   4. Applied rewrites 70.5%

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

   if 6.5999999999999997e168 < y

   1. Initial program 99.8%

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

   2. Taylor expanded in y around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-/.f64 31.0%

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

   4. Applied rewrites 31.0%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 31.0%

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

      4. lift-+.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. log-rec N/A

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

      8. lift-log.f64 N/A

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

      9. sub-flip-reverse N/A

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

      10. lower--.f64 31.0%

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

   6. Applied rewrites 31.0%

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

Alternative 5: 70.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_0 := \left(1 - \log y\right) \cdot y\\ t_1 := \left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z\\ t_2 := x \cdot 1 - z\\ \mathbf{if}\;t\_1 \leq -8 \cdot 10^{+247}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq -5 \cdot 10^{+44}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq -10000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 500:\\ \;\;\;\;y - \log y \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (- 1.0 (log y)) y))
        (t_1 (- (+ (- x (* (+ y 0.5) (log y))) y) z))
        (t_2 (- (* x 1.0) z)))
   (if (<= t_1 -8e+247)
     t_0
     (if (<= t_1 -5e+44)
       t_2
       (if (<= t_1 -10000.0)
         t_0
         (if (<= t_1 500.0) (- y (* (log y) 0.5)) t_2))))))

C

double code(double x, double y, double z) {
	double t_0 = (1.0 - log(y)) * y;
	double t_1 = ((x - ((y + 0.5) * log(y))) + y) - z;
	double t_2 = (x * 1.0) - z;
	double tmp;
	if (t_1 <= -8e+247) {
		tmp = t_0;
	} else if (t_1 <= -5e+44) {
		tmp = t_2;
	} else if (t_1 <= -10000.0) {
		tmp = t_0;
	} else if (t_1 <= 500.0) {
		tmp = y - (log(y) * 0.5);
	} else {
		tmp = t_2;
	}
	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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = (1.0d0 - log(y)) * y
    t_1 = ((x - ((y + 0.5d0) * log(y))) + y) - z
    t_2 = (x * 1.0d0) - z
    if (t_1 <= (-8d+247)) then
        tmp = t_0
    else if (t_1 <= (-5d+44)) then
        tmp = t_2
    else if (t_1 <= (-10000.0d0)) then
        tmp = t_0
    else if (t_1 <= 500.0d0) then
        tmp = y - (log(y) * 0.5d0)
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (1.0 - Math.log(y)) * y;
	double t_1 = ((x - ((y + 0.5) * Math.log(y))) + y) - z;
	double t_2 = (x * 1.0) - z;
	double tmp;
	if (t_1 <= -8e+247) {
		tmp = t_0;
	} else if (t_1 <= -5e+44) {
		tmp = t_2;
	} else if (t_1 <= -10000.0) {
		tmp = t_0;
	} else if (t_1 <= 500.0) {
		tmp = y - (Math.log(y) * 0.5);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (1.0 - math.log(y)) * y
	t_1 = ((x - ((y + 0.5) * math.log(y))) + y) - z
	t_2 = (x * 1.0) - z
	tmp = 0
	if t_1 <= -8e+247:
		tmp = t_0
	elif t_1 <= -5e+44:
		tmp = t_2
	elif t_1 <= -10000.0:
		tmp = t_0
	elif t_1 <= 500.0:
		tmp = y - (math.log(y) * 0.5)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(1.0 - log(y)) * y)
	t_1 = Float64(Float64(Float64(x - Float64(Float64(y + 0.5) * log(y))) + y) - z)
	t_2 = Float64(Float64(x * 1.0) - z)
	tmp = 0.0
	if (t_1 <= -8e+247)
		tmp = t_0;
	elseif (t_1 <= -5e+44)
		tmp = t_2;
	elseif (t_1 <= -10000.0)
		tmp = t_0;
	elseif (t_1 <= 500.0)
		tmp = Float64(y - Float64(log(y) * 0.5));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (1.0 - log(y)) * y;
	t_1 = ((x - ((y + 0.5) * log(y))) + y) - z;
	t_2 = (x * 1.0) - z;
	tmp = 0.0;
	if (t_1 <= -8e+247)
		tmp = t_0;
	elseif (t_1 <= -5e+44)
		tmp = t_2;
	elseif (t_1 <= -10000.0)
		tmp = t_0;
	elseif (t_1 <= 500.0)
		tmp = y - (log(y) * 0.5);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(x - N[(N[(y + 0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x * 1.0), $MachinePrecision] - z), $MachinePrecision]}, If[LessEqual[t$95$1, -8e+247], t$95$0, If[LessEqual[t$95$1, -5e+44], t$95$2, If[LessEqual[t$95$1, -10000.0], t$95$0, If[LessEqual[t$95$1, 500.0], N[(y - N[(N[Log[y], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) z) < -7.99999999999999962e247 or -4.9999999999999996e44 < (-.f64 (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) z) < -1e4

   1. Initial program 99.8%

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

   2. Taylor expanded in y around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-/.f64 31.0%

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

   4. Applied rewrites 31.0%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 31.0%

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

      4. lift-+.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. log-rec N/A

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

      8. lift-log.f64 N/A

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

      9. sub-flip-reverse N/A

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

      10. lower--.f64 31.0%

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

   6. Applied rewrites 31.0%

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

   if -7.99999999999999962e247 < (-.f64 (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) z) < -4.9999999999999996e44 or 500 < (-.f64 (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) z)

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower-+.f64 87.2%

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

   4. Applied rewrites 87.2%

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

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot 1 - z \]
   6. Step-by-step derivation

   7. Applied rewrites 57.9%

      \[\leadsto x \cdot 1 - z \]

   if -1e4 < (-.f64 (+.f64 (-.f64 x (*.f64 (+.f64 y #s(literal 1/2 binary64)) (log.f64 y))) y) z) < 500

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
   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-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

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

      6. lower--.f64 N/A

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

      7. sub-negate-rev N/A

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

      8. lift--.f64 N/A

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

      9. lift-*.f64 N/A

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

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

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

      11. +-commutative N/A

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

      12. associate--l+ N/A

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

      13. distribute-neg-in N/A

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 71.1%

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

   6. Applied rewrites 71.1%

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

   7. Taylor expanded in x around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 N/A

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

      3. lower-+.f64 42.7%

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

   9. Applied rewrites 42.7%

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

   10. Taylor expanded in y around 0

       \[\leadsto y - \log y \cdot \frac{1}{2} \]
   11. Step-by-step derivation

   12. Applied rewrites 13.7%

       \[\leadsto y - \log y \cdot 0.5 \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 69.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_0 := x \cdot 1 - z\\ \mathbf{if}\;x \leq -7.5 \cdot 10^{-93}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 0.029:\\ \;\;\;\;y - \left(y - -0.5\right) \cdot \log y\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- (* x 1.0) z)))
   (if (<= x -7.5e-93)
     t_0
     (if (<= x 0.029) (- y (* (- y -0.5) (log y))) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (x * 1.0) - z;
	double tmp;
	if (x <= -7.5e-93) {
		tmp = t_0;
	} else if (x <= 0.029) {
		tmp = y - ((y - -0.5) * log(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 * 1.0d0) - z
    if (x <= (-7.5d-93)) then
        tmp = t_0
    else if (x <= 0.029d0) then
        tmp = y - ((y - (-0.5d0)) * log(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 * 1.0) - z;
	double tmp;
	if (x <= -7.5e-93) {
		tmp = t_0;
	} else if (x <= 0.029) {
		tmp = y - ((y - -0.5) * Math.log(y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x * 1.0) - z
	tmp = 0
	if x <= -7.5e-93:
		tmp = t_0
	elif x <= 0.029:
		tmp = y - ((y - -0.5) * math.log(y))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x * 1.0) - z)
	tmp = 0.0
	if (x <= -7.5e-93)
		tmp = t_0;
	elseif (x <= 0.029)
		tmp = Float64(y - Float64(Float64(y - -0.5) * log(y)));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x * 1.0) - z;
	tmp = 0.0;
	if (x <= -7.5e-93)
		tmp = t_0;
	elseif (x <= 0.029)
		tmp = y - ((y - -0.5) * log(y));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x * 1.0), $MachinePrecision] - z), $MachinePrecision]}, If[LessEqual[x, -7.5e-93], t$95$0, If[LessEqual[x, 0.029], N[(y - N[(N[(y - -0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -7.50000000000000034e-93 or 0.0290000000000000015 < x

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower-+.f64 87.2%

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

   4. Applied rewrites 87.2%

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

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot 1 - z \]
   6. Step-by-step derivation

   7. Applied rewrites 57.9%

      \[\leadsto x \cdot 1 - z \]

   if -7.50000000000000034e-93 < x < 0.0290000000000000015

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
   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-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

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

      6. lower--.f64 N/A

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

      7. sub-negate-rev N/A

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

      8. lift--.f64 N/A

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

      9. lift-*.f64 N/A

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

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

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

      11. +-commutative N/A

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

      12. associate--l+ N/A

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

      13. distribute-neg-in N/A

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 71.1%

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

   6. Applied rewrites 71.1%

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

   7. Taylor expanded in x around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 N/A

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

      3. lower-+.f64 42.7%

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

   9. Applied rewrites 42.7%

      \[\leadsto y - \log y \cdot \color{blue}{\left(0.5 + y\right)} \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

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

       2. *-commutative N/A

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

       3. lift-+.f64 N/A

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

       4. +-commutative N/A

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

       5. lower-*.f64 N/A

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

       6. add-flip N/A

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

       7. metadata-eval N/A

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

       8. lower--.f64 42.7%

          \[\leadsto y - \left(y - -0.5\right) \cdot \log y \]

   11. Applied rewrites 42.7%

       \[\leadsto y - \color{blue}{\left(y - -0.5\right) \cdot \log y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 68.5% accurate, 1.3× speedup

Math

\[\begin{array}{l} \mathbf{if}\;y \leq 6.6 \cdot 10^{+168}:\\ \;\;\;\;x \cdot 1 - z\\ \mathbf{else}:\\ \;\;\;\;\left(1 - \log y\right) \cdot y\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y 6.6e+168) (- (* x 1.0) z) (* (- 1.0 (log y)) y)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= 6.6e+168) {
		tmp = (x * 1.0) - 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 <= 6.6d+168) then
        tmp = (x * 1.0d0) - 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 <= 6.6e+168) {
		tmp = (x * 1.0) - z;
	} else {
		tmp = (1.0 - Math.log(y)) * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= 6.6e+168:
		tmp = (x * 1.0) - z
	else:
		tmp = (1.0 - math.log(y)) * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= 6.6e+168)
		tmp = Float64(Float64(x * 1.0) - 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 <= 6.6e+168)
		tmp = (x * 1.0) - z;
	else
		tmp = (1.0 - log(y)) * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, 6.6e+168], N[(N[(x * 1.0), $MachinePrecision] - z), $MachinePrecision], N[(N[(1.0 - N[Log[y], $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 6.5999999999999997e168

   1. Initial program 99.8%

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

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower-+.f64 87.2%

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

   4. Applied rewrites 87.2%

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

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot 1 - z \]
   6. Step-by-step derivation

   7. Applied rewrites 57.9%

      \[\leadsto x \cdot 1 - z \]

   if 6.5999999999999997e168 < y

   1. Initial program 99.8%

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

   2. Taylor expanded in y around -inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-/.f64 31.0%

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

   4. Applied rewrites 31.0%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 31.0%

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

      4. lift-+.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. log-rec N/A

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

      8. lift-log.f64 N/A

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

      9. sub-flip-reverse N/A

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

      10. lower--.f64 31.0%

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

   6. Applied rewrites 31.0%

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

Alternative 8: 57.9% accurate, 3.1× speedup

Math

\[x \cdot 1 - z \]

FPCore

(FPCore (x y z) :precision binary64 (- (* x 1.0) z))

C

double code(double x, double y, double z) {
	return (x * 1.0) - 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 * 1.0d0) - z
end function

Java

public static double code(double x, double y, double z) {
	return (x * 1.0) - z;
}

Python

def code(x, y, z):
	return (x * 1.0) - z

Julia

function code(x, y, z)
	return Float64(Float64(x * 1.0) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * 1.0) - z;
end

Wolfram

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

Derivation

1. Initial program 99.8%

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

2. Taylor expanded in x around inf

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

   1. lower-*.f64 N/A

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

   2. lower--.f64 N/A

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

   3. lower-+.f64 N/A

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

   4. lower-/.f64 N/A

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

   5. lower-/.f64 N/A

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

   6. lower-*.f64 N/A

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

   7. lower-log.f64 N/A

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

   8. lower-+.f64 87.2%

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

4. Applied rewrites 87.2%

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

5. Taylor expanded in x around inf

   \[\leadsto x \cdot 1 - z \]
6. Step-by-step derivation

7. Applied rewrites 57.9%

   \[\leadsto x \cdot 1 - z \]
8. Add Preprocessing

Alternative 9: 47.8% accurate, 1.7× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq -1.25 \cdot 10^{+122}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{+39}:\\ \;\;\;\;y - \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1.25e+122) (- z) (if (<= z 6.5e+39) (- y (- x)) (- z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.25e+122) {
		tmp = -z;
	} else if (z <= 6.5e+39) {
		tmp = y - -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 <= (-1.25d+122)) then
        tmp = -z
    else if (z <= 6.5d+39) then
        tmp = y - -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 <= -1.25e+122) {
		tmp = -z;
	} else if (z <= 6.5e+39) {
		tmp = y - -x;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1.25e+122:
		tmp = -z
	elif z <= 6.5e+39:
		tmp = y - -x
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1.25e+122)
		tmp = Float64(-z);
	elseif (z <= 6.5e+39)
		tmp = Float64(y - Float64(-x));
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -1.25e+122)
		tmp = -z;
	elseif (z <= 6.5e+39)
		tmp = y - -x;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1.25e+122], (-z), If[LessEqual[z, 6.5e+39], N[(y - (-x)), $MachinePrecision], (-z)]]

Derivation

1. Split input into 2 regimes

2. if z < -1.24999999999999997e122 or 6.5000000000000001e39 < z

   1. Initial program 99.8%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 29.9%

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

   4. Applied rewrites 29.9%

      \[\leadsto \color{blue}{-1 \cdot z} \]
   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 29.9%

         \[\leadsto -z \]

   6. Applied rewrites 29.9%

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

   if -1.24999999999999997e122 < z < 6.5000000000000001e39

   1. Initial program 99.8%

      \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
   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-negate-rev N/A

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

      3. lift-+.f64 N/A

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

      4. associate--r+ N/A

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

      5. sub-negate N/A

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

      6. lower--.f64 N/A

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

      7. sub-negate-rev N/A

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

      8. lift--.f64 N/A

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

      9. lift-*.f64 N/A

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

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

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

      11. +-commutative N/A

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

      12. associate--l+ N/A

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

      13. distribute-neg-in N/A

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

   3. Applied rewrites 99.8%

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

   4. Taylor expanded in x around inf

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

      1. lower-*.f64 29.7%

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

   6. Applied rewrites 29.7%

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

      1. lift-*.f64 N/A

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

      2. mul-1-neg N/A

         \[\leadsto y - \left(\mathsf{neg}\left(x\right)\right) \]

      3. lower-neg.f64 29.7%

         \[\leadsto y - \left(-x\right) \]

   8. Applied rewrites 29.7%

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

Alternative 10: 29.9% accurate, 10.4× 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%

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

2. Taylor expanded in z around inf

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

   1. lower-*.f64 29.9%

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

4. Applied rewrites 29.9%

   \[\leadsto \color{blue}{-1 \cdot z} \]
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 29.9%

      \[\leadsto -z \]

6. Applied rewrites 29.9%

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

Reproduce

herbie shell --seed 2025187 
(FPCore (x y z)
  :name "Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2"
  :precision binary64
  (- (+ (- x (* (+ y 0.5) (log y))) y) z))