Numeric.SpecFunctions:incompleteGamma from math-functions-0.1.5.2, A

Percentage Accurate: 99.9% → 99.9%

Time: 5.7s

Alternatives: 12

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

3. Taylor expanded in z around 0

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

   1. lower--.f64 N/A

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

   2. +-commutative N/A

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

   3. *-commutative N/A

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

   4. lower-fma.f64 N/A

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

   5. lift-log.f64 N/A

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

   6. mul-1-neg N/A

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

   7. lower-neg.f64 99.9

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

5. Applied rewrites 99.9%

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

Alternative 2: 71.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \log y - y\\ t_2 := \log y \cdot x\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+264}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq -2 \cdot 10^{+15}:\\ \;\;\;\;\mathsf{fma}\left(\frac{-z}{x}, x, -y\right)\\ \mathbf{elif}\;t\_1 \leq 10^{+128}:\\ \;\;\;\;\left(-z\right) + \log t\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (* x (log y)) y)) (t_2 (* (log y) x)))
   (if (<= t_1 -5e+264)
     t_2
     (if (<= t_1 -2e+15)
       (fma (/ (- z) x) x (- y))
       (if (<= t_1 1e+128) (+ (- z) (log t)) t_2)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x * log(y)) - y;
	double t_2 = log(y) * x;
	double tmp;
	if (t_1 <= -5e+264) {
		tmp = t_2;
	} else if (t_1 <= -2e+15) {
		tmp = fma((-z / x), x, -y);
	} else if (t_1 <= 1e+128) {
		tmp = -z + log(t);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x * log(y)) - y)
	t_2 = Float64(log(y) * x)
	tmp = 0.0
	if (t_1 <= -5e+264)
		tmp = t_2;
	elseif (t_1 <= -2e+15)
		tmp = fma(Float64(Float64(-z) / x), x, Float64(-y));
	elseif (t_1 <= 1e+128)
		tmp = Float64(Float64(-z) + log(t));
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x * N[Log[y], $MachinePrecision]), $MachinePrecision] - y), $MachinePrecision]}, Block[{t$95$2 = N[(N[Log[y], $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+264], t$95$2, If[LessEqual[t$95$1, -2e+15], N[(N[((-z) / x), $MachinePrecision] * x + (-y)), $MachinePrecision], If[LessEqual[t$95$1, 1e+128], N[((-z) + N[Log[t], $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (*.f64 x (log.f64 y)) y) < -5.00000000000000033e264 or 1.0000000000000001e128 < (-.f64 (*.f64 x (log.f64 y)) y)

   1. Initial program 99.7%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \log y \cdot \color{blue}{x} \]

      2. lower-*.f64 N/A

         \[\leadsto \log y \cdot \color{blue}{x} \]

      3. lift-log.f64 63.4

         \[\leadsto \log y \cdot x \]

   5. Applied rewrites 63.4%

      \[\leadsto \color{blue}{\log y \cdot x} \]

   if -5.00000000000000033e264 < (-.f64 (*.f64 x (log.f64 y)) y) < -2e15

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. associate--l+ N/A

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

      4. div-add-rev N/A

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

      5. div-sub N/A

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

      6. lower-+.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. associate--r+ N/A

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

      10. lower--.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lift-log.f64 75.9

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

   5. Applied rewrites 75.9%

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

   6. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. sub-div N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 N/A

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

      10. lift-log.f64 N/A

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

      11. mul-1-neg N/A

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

      12. lift-neg.f64 89.2

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

   8. Applied rewrites 89.2%

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

   9. Taylor expanded in z around inf

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

       1. associate-*r/ N/A

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

       2. lower-/.f64 N/A

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

       3. mul-1-neg N/A

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

       4. lower-neg.f64 65.3

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

   11. Applied rewrites 65.3%

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

   if -2e15 < (-.f64 (*.f64 x (log.f64 y)) y) < 1.0000000000000001e128

   1. Initial program 99.9%

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

   3. Taylor expanded in z around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 85.5

         \[\leadsto \left(-z\right) + \log t \]

   5. Applied rewrites 85.5%

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

Alternative 3: 89.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\log t - y\right) - z\\ \mathbf{if}\;z \leq -2.45 \cdot 10^{+86}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.5 \cdot 10^{+27}:\\ \;\;\;\;\mathsf{fma}\left(\log y, x, \log t\right) - y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (- (log t) y) z)))
   (if (<= z -2.45e+86)
     t_1
     (if (<= z 3.5e+27) (- (fma (log y) x (log t)) y) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (log(t) - y) - z;
	double tmp;
	if (z <= -2.45e+86) {
		tmp = t_1;
	} else if (z <= 3.5e+27) {
		tmp = fma(log(y), x, log(t)) - y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(log(t) - y) - z)
	tmp = 0.0
	if (z <= -2.45e+86)
		tmp = t_1;
	elseif (z <= 3.5e+27)
		tmp = Float64(fma(log(y), x, log(t)) - y);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[Log[t], $MachinePrecision] - y), $MachinePrecision] - z), $MachinePrecision]}, If[LessEqual[z, -2.45e+86], t$95$1, If[LessEqual[z, 3.5e+27], N[(N[(N[Log[y], $MachinePrecision] * x + N[Log[t], $MachinePrecision]), $MachinePrecision] - y), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.45e86 or 3.5000000000000002e27 < z

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

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

      1. associate--r+ N/A

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

      2. lower--.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lift-log.f64 81.4

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

   5. Applied rewrites 81.4%

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

   if -2.45e86 < z < 3.5000000000000002e27

   1. Initial program 99.8%

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

   3. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lift-log.f64 95.0

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

   5. Applied rewrites 95.0%

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

Alternative 4: 88.8% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\log y + \frac{-z}{x}\right) \cdot x\\ \mathbf{if}\;x \leq -1.05 \cdot 10^{+135}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 1.1 \cdot 10^{+26}:\\ \;\;\;\;\left(\log t - y\right) - z\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (+ (log y) (/ (- z) x)) x)))
   (if (<= x -1.05e+135) t_1 (if (<= x 1.1e+26) (- (- (log t) y) z) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (log(y) + (-z / x)) * x;
	double tmp;
	if (x <= -1.05e+135) {
		tmp = t_1;
	} else if (x <= 1.1e+26) {
		tmp = (log(t) - y) - z;
	} else {
		tmp = t_1;
	}
	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, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (log(y) + (-z / x)) * x
    if (x <= (-1.05d+135)) then
        tmp = t_1
    else if (x <= 1.1d+26) then
        tmp = (log(t) - y) - z
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (Math.log(y) + (-z / x)) * x;
	double tmp;
	if (x <= -1.05e+135) {
		tmp = t_1;
	} else if (x <= 1.1e+26) {
		tmp = (Math.log(t) - y) - z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (math.log(y) + (-z / x)) * x
	tmp = 0
	if x <= -1.05e+135:
		tmp = t_1
	elif x <= 1.1e+26:
		tmp = (math.log(t) - y) - z
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(log(y) + Float64(Float64(-z) / x)) * x)
	tmp = 0.0
	if (x <= -1.05e+135)
		tmp = t_1;
	elseif (x <= 1.1e+26)
		tmp = Float64(Float64(log(t) - y) - z);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (log(y) + (-z / x)) * x;
	tmp = 0.0;
	if (x <= -1.05e+135)
		tmp = t_1;
	elseif (x <= 1.1e+26)
		tmp = (log(t) - y) - z;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[Log[y], $MachinePrecision] + N[((-z) / x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -1.05e+135], t$95$1, If[LessEqual[x, 1.1e+26], N[(N[(N[Log[t], $MachinePrecision] - y), $MachinePrecision] - z), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -1.05000000000000005e135 or 1.10000000000000004e26 < x

   1. Initial program 99.7%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. associate--l+ N/A

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

      4. div-add-rev N/A

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

      5. div-sub N/A

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

      6. lower-+.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. associate--r+ N/A

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

      10. lower--.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lift-log.f64 99.7

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

   5. Applied rewrites 99.7%

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

   6. Taylor expanded in z around inf

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

      1. mul-1-neg N/A

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

      2. lift-neg.f64 82.7

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

   8. Applied rewrites 82.7%

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

   if -1.05000000000000005e135 < x < 1.10000000000000004e26

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

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

      1. associate--r+ N/A

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

      2. lower--.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lift-log.f64 92.2

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

   5. Applied rewrites 92.2%

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

Alternative 5: 88.6% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.1 \cdot 10^{+135}:\\ \;\;\;\;\log y \cdot x - y\\ \mathbf{elif}\;x \leq 4.5 \cdot 10^{+106}:\\ \;\;\;\;\left(\log t - y\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(\log y + \frac{-y}{x}\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= x -2.1e+135)
   (- (* (log y) x) y)
   (if (<= x 4.5e+106) (- (- (log t) y) z) (* (+ (log y) (/ (- y) x)) x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -2.1e+135) {
		tmp = (log(y) * x) - y;
	} else if (x <= 4.5e+106) {
		tmp = (log(t) - y) - z;
	} else {
		tmp = (log(y) + (-y / x)) * 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, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-2.1d+135)) then
        tmp = (log(y) * x) - y
    else if (x <= 4.5d+106) then
        tmp = (log(t) - y) - z
    else
        tmp = (log(y) + (-y / x)) * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -2.1e+135) {
		tmp = (Math.log(y) * x) - y;
	} else if (x <= 4.5e+106) {
		tmp = (Math.log(t) - y) - z;
	} else {
		tmp = (Math.log(y) + (-y / x)) * x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -2.1e+135:
		tmp = (math.log(y) * x) - y
	elif x <= 4.5e+106:
		tmp = (math.log(t) - y) - z
	else:
		tmp = (math.log(y) + (-y / x)) * x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -2.1e+135)
		tmp = Float64(Float64(log(y) * x) - y);
	elseif (x <= 4.5e+106)
		tmp = Float64(Float64(log(t) - y) - z);
	else
		tmp = Float64(Float64(log(y) + Float64(Float64(-y) / x)) * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -2.1e+135)
		tmp = (log(y) * x) - y;
	elseif (x <= 4.5e+106)
		tmp = (log(t) - y) - z;
	else
		tmp = (log(y) + (-y / x)) * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -2.1e+135], N[(N[(N[Log[y], $MachinePrecision] * x), $MachinePrecision] - y), $MachinePrecision], If[LessEqual[x, 4.5e+106], N[(N[(N[Log[t], $MachinePrecision] - y), $MachinePrecision] - z), $MachinePrecision], N[(N[(N[Log[y], $MachinePrecision] + N[((-y) / x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.1000000000000001e135

   1. Initial program 99.7%

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

   3. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lift-log.f64 87.8

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

   5. Applied rewrites 87.8%

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

   6. Taylor expanded in x around inf

      \[\leadsto x \cdot \log y - y \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \log y \cdot x - y \]

      2. lower-*.f64 N/A

         \[\leadsto \log y \cdot x - y \]

      3. lift-log.f64 87.8

         \[\leadsto \log y \cdot x - y \]

   8. Applied rewrites 87.8%

      \[\leadsto \log y \cdot x - y \]

   if -2.1000000000000001e135 < x < 4.4999999999999997e106

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

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

      1. associate--r+ N/A

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

      2. lower--.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lift-log.f64 89.7

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

   5. Applied rewrites 89.7%

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

   if 4.4999999999999997e106 < x

   1. Initial program 99.7%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. associate--l+ N/A

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

      4. div-add-rev N/A

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

      5. div-sub N/A

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

      6. lower-+.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. associate--r+ N/A

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

      10. lower--.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lift-log.f64 99.7

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

   5. Applied rewrites 99.7%

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

   6. Taylor expanded in y around inf

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

      1. mul-1-neg N/A

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

      2. lift-neg.f64 84.8

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

   8. Applied rewrites 84.8%

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

Alternative 6: 65.7% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log y \cdot x\\ t_2 := \mathsf{fma}\left(\frac{-z}{x}, x, -y\right)\\ \mathbf{if}\;x \leq -3.2 \cdot 10^{+135}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq -2.6 \cdot 10^{-91}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;x \leq 5 \cdot 10^{-56}:\\ \;\;\;\;\log t - y\\ \mathbf{elif}\;x \leq 4.6 \cdot 10^{+170}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (log y) x)) (t_2 (fma (/ (- z) x) x (- y))))
   (if (<= x -3.2e+135)
     t_1
     (if (<= x -2.6e-91)
       t_2
       (if (<= x 5e-56) (- (log t) y) (if (<= x 4.6e+170) t_2 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = log(y) * x;
	double t_2 = fma((-z / x), x, -y);
	double tmp;
	if (x <= -3.2e+135) {
		tmp = t_1;
	} else if (x <= -2.6e-91) {
		tmp = t_2;
	} else if (x <= 5e-56) {
		tmp = log(t) - y;
	} else if (x <= 4.6e+170) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(log(y) * x)
	t_2 = fma(Float64(Float64(-z) / x), x, Float64(-y))
	tmp = 0.0
	if (x <= -3.2e+135)
		tmp = t_1;
	elseif (x <= -2.6e-91)
		tmp = t_2;
	elseif (x <= 5e-56)
		tmp = Float64(log(t) - y);
	elseif (x <= 4.6e+170)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[Log[y], $MachinePrecision] * x), $MachinePrecision]}, Block[{t$95$2 = N[(N[((-z) / x), $MachinePrecision] * x + (-y)), $MachinePrecision]}, If[LessEqual[x, -3.2e+135], t$95$1, If[LessEqual[x, -2.6e-91], t$95$2, If[LessEqual[x, 5e-56], N[(N[Log[t], $MachinePrecision] - y), $MachinePrecision], If[LessEqual[x, 4.6e+170], t$95$2, t$95$1]]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -3.19999999999999975e135 or 4.6000000000000001e170 < x

   1. Initial program 99.7%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \log y \cdot \color{blue}{x} \]

      2. lower-*.f64 N/A

         \[\leadsto \log y \cdot \color{blue}{x} \]

      3. lift-log.f64 76.0

         \[\leadsto \log y \cdot x \]

   5. Applied rewrites 76.0%

      \[\leadsto \color{blue}{\log y \cdot x} \]

   if -3.19999999999999975e135 < x < -2.60000000000000014e-91 or 4.99999999999999997e-56 < x < 4.6000000000000001e170

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. associate--l+ N/A

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

      4. div-add-rev N/A

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

      5. div-sub N/A

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

      6. lower-+.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. associate--r+ N/A

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

      10. lower--.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lift-log.f64 96.0

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

   5. Applied rewrites 96.0%

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

   6. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. sub-div N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 N/A

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

      10. lift-log.f64 N/A

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

      11. mul-1-neg N/A

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

      12. lift-neg.f64 97.7

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

   8. Applied rewrites 97.7%

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

   9. Taylor expanded in z around inf

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

       1. associate-*r/ N/A

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

       2. lower-/.f64 N/A

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

       3. mul-1-neg N/A

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

       4. lower-neg.f64 62.7

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

   11. Applied rewrites 62.7%

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

   if -2.60000000000000014e-91 < x < 4.99999999999999997e-56

   1. Initial program 100.0%

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

   3. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lift-log.f64 62.0

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

   5. Applied rewrites 62.0%

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

   6. Taylor expanded in x around 0

      \[\leadsto \log t - y \]
   7. Step-by-step derivation

      1. lift-log.f64 62.0

         \[\leadsto \log t - y \]

   8. Applied rewrites 62.0%

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

Alternative 7: 88.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log y \cdot x - y\\ \mathbf{if}\;x \leq -2.1 \cdot 10^{+135}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 4.5 \cdot 10^{+106}:\\ \;\;\;\;\left(\log t - y\right) - z\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (* (log y) x) y)))
   (if (<= x -2.1e+135) t_1 (if (<= x 4.5e+106) (- (- (log t) y) z) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (log(y) * x) - y;
	double tmp;
	if (x <= -2.1e+135) {
		tmp = t_1;
	} else if (x <= 4.5e+106) {
		tmp = (log(t) - y) - z;
	} else {
		tmp = t_1;
	}
	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, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (log(y) * x) - y
    if (x <= (-2.1d+135)) then
        tmp = t_1
    else if (x <= 4.5d+106) then
        tmp = (log(t) - y) - z
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (Math.log(y) * x) - y;
	double tmp;
	if (x <= -2.1e+135) {
		tmp = t_1;
	} else if (x <= 4.5e+106) {
		tmp = (Math.log(t) - y) - z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (math.log(y) * x) - y
	tmp = 0
	if x <= -2.1e+135:
		tmp = t_1
	elif x <= 4.5e+106:
		tmp = (math.log(t) - y) - z
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(log(y) * x) - y)
	tmp = 0.0
	if (x <= -2.1e+135)
		tmp = t_1;
	elseif (x <= 4.5e+106)
		tmp = Float64(Float64(log(t) - y) - z);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (log(y) * x) - y;
	tmp = 0.0;
	if (x <= -2.1e+135)
		tmp = t_1;
	elseif (x <= 4.5e+106)
		tmp = (log(t) - y) - z;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[Log[y], $MachinePrecision] * x), $MachinePrecision] - y), $MachinePrecision]}, If[LessEqual[x, -2.1e+135], t$95$1, If[LessEqual[x, 4.5e+106], N[(N[(N[Log[t], $MachinePrecision] - y), $MachinePrecision] - z), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -2.1000000000000001e135 or 4.4999999999999997e106 < x

   1. Initial program 99.7%

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

   3. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lift-log.f64 86.2

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

   5. Applied rewrites 86.2%

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

   6. Taylor expanded in x around inf

      \[\leadsto x \cdot \log y - y \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \log y \cdot x - y \]

      2. lower-*.f64 N/A

         \[\leadsto \log y \cdot x - y \]

      3. lift-log.f64 86.2

         \[\leadsto \log y \cdot x - y \]

   8. Applied rewrites 86.2%

      \[\leadsto \log y \cdot x - y \]

   if -2.1000000000000001e135 < x < 4.4999999999999997e106

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

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

      1. associate--r+ N/A

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

      2. lower--.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lift-log.f64 89.7

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

   5. Applied rewrites 89.7%

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

Alternative 8: 83.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \log y \cdot x\\ \mathbf{if}\;x \leq -3.2 \cdot 10^{+135}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 4.6 \cdot 10^{+170}:\\ \;\;\;\;\left(\log t - y\right) - z\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (log y) x)))
   (if (<= x -3.2e+135) t_1 (if (<= x 4.6e+170) (- (- (log t) y) z) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = log(y) * x;
	double tmp;
	if (x <= -3.2e+135) {
		tmp = t_1;
	} else if (x <= 4.6e+170) {
		tmp = (log(t) - y) - z;
	} else {
		tmp = t_1;
	}
	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, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = log(y) * x
    if (x <= (-3.2d+135)) then
        tmp = t_1
    else if (x <= 4.6d+170) then
        tmp = (log(t) - y) - z
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = Math.log(y) * x;
	double tmp;
	if (x <= -3.2e+135) {
		tmp = t_1;
	} else if (x <= 4.6e+170) {
		tmp = (Math.log(t) - y) - z;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = math.log(y) * x
	tmp = 0
	if x <= -3.2e+135:
		tmp = t_1
	elif x <= 4.6e+170:
		tmp = (math.log(t) - y) - z
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(log(y) * x)
	tmp = 0.0
	if (x <= -3.2e+135)
		tmp = t_1;
	elseif (x <= 4.6e+170)
		tmp = Float64(Float64(log(t) - y) - z);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = log(y) * x;
	tmp = 0.0;
	if (x <= -3.2e+135)
		tmp = t_1;
	elseif (x <= 4.6e+170)
		tmp = (log(t) - y) - z;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[Log[y], $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -3.2e+135], t$95$1, If[LessEqual[x, 4.6e+170], N[(N[(N[Log[t], $MachinePrecision] - y), $MachinePrecision] - z), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.19999999999999975e135 or 4.6000000000000001e170 < x

   1. Initial program 99.7%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \log y \cdot \color{blue}{x} \]

      2. lower-*.f64 N/A

         \[\leadsto \log y \cdot \color{blue}{x} \]

      3. lift-log.f64 76.0

         \[\leadsto \log y \cdot x \]

   5. Applied rewrites 76.0%

      \[\leadsto \color{blue}{\log y \cdot x} \]

   if -3.19999999999999975e135 < x < 4.6000000000000001e170

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

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

      1. associate--r+ N/A

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

      2. lower--.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lift-log.f64 86.5

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

   5. Applied rewrites 86.5%

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

Alternative 9: 62.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.5 \cdot 10^{+69}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{+19}:\\ \;\;\;\;\log t - y\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{+174}:\\ \;\;\;\;\mathsf{fma}\left(\frac{-z}{x}, x, -y\right)\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -6.5e+69)
   (- z)
   (if (<= z 9.5e+19)
     (- (log t) y)
     (if (<= z 6.2e+174) (fma (/ (- z) x) x (- y)) (- z)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -6.5e+69) {
		tmp = -z;
	} else if (z <= 9.5e+19) {
		tmp = log(t) - y;
	} else if (z <= 6.2e+174) {
		tmp = fma((-z / x), x, -y);
	} else {
		tmp = -z;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -6.5e+69)
		tmp = Float64(-z);
	elseif (z <= 9.5e+19)
		tmp = Float64(log(t) - y);
	elseif (z <= 6.2e+174)
		tmp = fma(Float64(Float64(-z) / x), x, Float64(-y));
	else
		tmp = Float64(-z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -6.5e+69], (-z), If[LessEqual[z, 9.5e+19], N[(N[Log[t], $MachinePrecision] - y), $MachinePrecision], If[LessEqual[z, 6.2e+174], N[(N[((-z) / x), $MachinePrecision] * x + (-y)), $MachinePrecision], (-z)]]]

Derivation

1. Split input into 3 regimes

2. if z < -6.5000000000000001e69 or 6.2e174 < z

   1. Initial program 99.9%

      \[\left(\left(x \cdot \log y - y\right) - z\right) + \log t \]
   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 70.2

         \[\leadsto -z \]

   5. Applied rewrites 70.2%

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

   if -6.5000000000000001e69 < z < 9.5e19

   1. Initial program 99.8%

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

   3. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-log.f64 N/A

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

      6. lift-log.f64 95.9

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

   5. Applied rewrites 95.9%

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

   6. Taylor expanded in x around 0

      \[\leadsto \log t - y \]
   7. Step-by-step derivation

      1. lift-log.f64 59.5

         \[\leadsto \log t - y \]

   8. Applied rewrites 59.5%

      \[\leadsto \log t - y \]

   if 9.5e19 < z < 6.2e174

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. associate--l+ N/A

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

      4. div-add-rev N/A

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

      5. div-sub N/A

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

      6. lower-+.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. associate--r+ N/A

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

      10. lower--.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lift-log.f64 78.5

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

   5. Applied rewrites 78.5%

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

   6. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. sub-div N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 N/A

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

      10. lift-log.f64 N/A

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

      11. mul-1-neg N/A

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

      12. lift-neg.f64 84.2

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

   8. Applied rewrites 84.2%

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

   9. Taylor expanded in z around inf

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

       1. associate-*r/ N/A

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

       2. lower-/.f64 N/A

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

       3. mul-1-neg N/A

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

       4. lower-neg.f64 55.4

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

   11. Applied rewrites 55.4%

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

Alternative 10: 52.4% accurate, 6.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.3 \cdot 10^{+170}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 6.2 \cdot 10^{+174}:\\ \;\;\;\;\mathsf{fma}\left(\frac{-z}{x}, x, -y\right)\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -4.3e+170)
   (- z)
   (if (<= z 6.2e+174) (fma (/ (- z) x) x (- y)) (- z))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -4.3e+170) {
		tmp = -z;
	} else if (z <= 6.2e+174) {
		tmp = fma((-z / x), x, -y);
	} else {
		tmp = -z;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -4.3e+170)
		tmp = Float64(-z);
	elseif (z <= 6.2e+174)
		tmp = fma(Float64(Float64(-z) / x), x, Float64(-y));
	else
		tmp = Float64(-z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -4.3e+170], (-z), If[LessEqual[z, 6.2e+174], N[(N[((-z) / x), $MachinePrecision] * x + (-y)), $MachinePrecision], (-z)]]

Derivation

1. Split input into 2 regimes

2. if z < -4.2999999999999999e170 or 6.2e174 < z

   1. Initial program 100.0%

      \[\left(\left(x \cdot \log y - y\right) - z\right) + \log t \]
   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 78.5

         \[\leadsto -z \]

   5. Applied rewrites 78.5%

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

   if -4.2999999999999999e170 < z < 6.2e174

   1. Initial program 99.8%

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

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. associate--l+ N/A

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

      4. div-add-rev N/A

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

      5. div-sub N/A

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

      6. lower-+.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. associate--r+ N/A

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

      10. lower--.f64 N/A

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

      11. lower--.f64 N/A

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

      12. lift-log.f64 84.4

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

   5. Applied rewrites 84.4%

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

   6. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. associate--l+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. sub-div N/A

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

      8. lower-/.f64 N/A

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

      9. lower--.f64 N/A

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

      10. lift-log.f64 N/A

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

      11. mul-1-neg N/A

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

      12. lift-neg.f64 94.6

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

   8. Applied rewrites 94.6%

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

   9. Taylor expanded in z around inf

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

       1. associate-*r/ N/A

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

       2. lower-/.f64 N/A

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

       3. mul-1-neg N/A

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

       4. lower-neg.f64 44.9

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

   11. Applied rewrites 44.9%

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

Alternative 11: 49.5% accurate, 14.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.5 \cdot 10^{+69}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{+35}:\\ \;\;\;\;-y\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -6.5e+69) (- z) (if (<= z 1.4e+35) (- y) (- z))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -6.5e+69) {
		tmp = -z;
	} else if (z <= 1.4e+35) {
		tmp = -y;
	} 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, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (z <= (-6.5d+69)) then
        tmp = -z
    else if (z <= 1.4d+35) then
        tmp = -y
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -6.5e+69) {
		tmp = -z;
	} else if (z <= 1.4e+35) {
		tmp = -y;
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -6.5e+69:
		tmp = -z
	elif z <= 1.4e+35:
		tmp = -y
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -6.5e+69)
		tmp = Float64(-z);
	elseif (z <= 1.4e+35)
		tmp = Float64(-y);
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -6.5e+69)
		tmp = -z;
	elseif (z <= 1.4e+35)
		tmp = -y;
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -6.5e+69], (-z), If[LessEqual[z, 1.4e+35], (-y), (-z)]]

Derivation

1. Split input into 2 regimes

2. if z < -6.5000000000000001e69 or 1.39999999999999999e35 < z

   1. Initial program 99.9%

      \[\left(\left(x \cdot \log y - y\right) - z\right) + \log t \]
   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 64.5

         \[\leadsto -z \]

   5. Applied rewrites 64.5%

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

   if -6.5000000000000001e69 < z < 1.39999999999999999e35

   1. Initial program 99.8%

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

   3. Taylor expanded in y around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 38.6

         \[\leadsto -y \]

   5. Applied rewrites 38.6%

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

Alternative 12: 30.0% accurate, 71.7× speedup

Math

\[\begin{array}{l} \\ -y \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

3. Taylor expanded in y around inf

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 30.0

      \[\leadsto -y \]

5. Applied rewrites 30.0%

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

Reproduce

herbie shell --seed 2025088 
(FPCore (x y z t)
  :name "Numeric.SpecFunctions:incompleteGamma from math-functions-0.1.5.2, A"
  :precision binary64
  (+ (- (- (* x (log y)) y) z) (log t)))