System.Random.MWC.Distributions:truncatedExp from mwc-random-0.13.3.2

Percentage Accurate: 60.6% → 94.4%

Time: 10.8s

Alternatives: 9

Speedup: 3.1×

• could not determine a ground truth

  1. x = 1.7890538508491432e+279
  2. y = 1.3543595275318269e+200
  3. z = 69584354958.75854
  4. t = -4.339805850971786e+31

Specification

Math

\[\begin{array}{l} \\ x - \frac{\log \left(\left(1 - y\right) + y \cdot e^{z}\right)}{t} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (- x (/ (log (+ (- 1.0 y) (* y (exp z)))) t)))

C

double code(double x, double y, double z, double t) {
	return x - (log(((1.0 - y) + (y * exp(z)))) / 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(((1.0d0 - y) + (y * exp(z)))) / t)
end function

Java

public static double code(double x, double y, double z, double t) {
	return x - (Math.log(((1.0 - y) + (y * Math.exp(z)))) / t);
}

Python

def code(x, y, z, t):
	return x - (math.log(((1.0 - y) + (y * math.exp(z)))) / t)

Julia

function code(x, y, z, t)
	return Float64(x - Float64(log(Float64(Float64(1.0 - y) + Float64(y * exp(z)))) / t))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x - (log(((1.0 - y) + (y * exp(z)))) / t);
end

Wolfram

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

Initial Program: 60.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \frac{\log \left(\left(1 - y\right) + y \cdot e^{z}\right)}{t} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (- x (/ (log (+ (- 1.0 y) (* y (exp z)))) t)))

C

double code(double x, double y, double z, double t) {
	return x - (log(((1.0 - y) + (y * exp(z)))) / 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(((1.0d0 - y) + (y * exp(z)))) / t)
end function

Java

public static double code(double x, double y, double z, double t) {
	return x - (Math.log(((1.0 - y) + (y * Math.exp(z)))) / t);
}

Python

def code(x, y, z, t):
	return x - (math.log(((1.0 - y) + (y * math.exp(z)))) / t)

Julia

function code(x, y, z, t)
	return Float64(x - Float64(log(Float64(Float64(1.0 - y) + Float64(y * exp(z)))) / t))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x - (log(((1.0 - y) + (y * exp(z)))) / t);
end

Wolfram

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

Alternative 1: 94.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.5 \cdot 10^{+49}:\\ \;\;\;\;x - \log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right) \cdot \frac{1}{t}\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{+161}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\log \left(\mathsf{fma}\left(z, y, 1\right)\right)}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -6.5e+49)
   (- x (* (log (fma (expm1 z) y 1.0)) (/ 1.0 t)))
   (if (<= y 6.2e+161)
     (fma (/ (expm1 z) (- t)) y x)
     (- x (/ (log (fma z y 1.0)) t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.5e+49) {
		tmp = x - (log(fma(expm1(z), y, 1.0)) * (1.0 / t));
	} else if (y <= 6.2e+161) {
		tmp = fma((expm1(z) / -t), y, x);
	} else {
		tmp = x - (log(fma(z, y, 1.0)) / t);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -6.5e+49)
		tmp = Float64(x - Float64(log(fma(expm1(z), y, 1.0)) * Float64(1.0 / t)));
	elseif (y <= 6.2e+161)
		tmp = fma(Float64(expm1(z) / Float64(-t)), y, x);
	else
		tmp = Float64(x - Float64(log(fma(z, y, 1.0)) / t));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -6.5e+49], N[(x - N[(N[Log[N[(N[(Exp[z] - 1), $MachinePrecision] * y + 1.0), $MachinePrecision]], $MachinePrecision] * N[(1.0 / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.2e+161], N[(N[(N[(Exp[z] - 1), $MachinePrecision] / (-t)), $MachinePrecision] * y + x), $MachinePrecision], N[(x - N[(N[Log[N[(z * y + 1.0), $MachinePrecision]], $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -6.5000000000000005e49

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-expm1.f64 81.4

         \[\leadsto x - \frac{\log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right)}{t} \]

   4. Applied rewrites 81.4%

      \[\leadsto x - \frac{\log \color{blue}{\left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right)}}{t} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{\log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right)}{t}} \]

      2. mult-flip N/A

         \[\leadsto x - \color{blue}{\log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right) \cdot \frac{1}{t}} \]

      3. lower-*.f64 N/A

         \[\leadsto x - \color{blue}{\log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right) \cdot \frac{1}{t}} \]

      4. lower-/.f64 81.4

         \[\leadsto x - \log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right) \cdot \color{blue}{\frac{1}{t}} \]

   6. Applied rewrites 81.4%

      \[\leadsto x - \color{blue}{\log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right) \cdot \frac{1}{t}} \]

   if -6.5000000000000005e49 < y < 6.20000000000000013e161

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. sub-div N/A

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

      5. frac-2neg N/A

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{neg}\left(\left(1 - e^{z}\right)\right)}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      6. sub-negate-rev N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{z} - 1}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      7. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{z} - 1}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      8. lower-expm1.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      9. lower-neg.f64 86.6

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right) \]

   4. Applied rewrites 86.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right)} \]

   if 6.20000000000000013e161 < y

   1. Initial program 60.6%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 70.4

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

   4. Applied rewrites 70.4%

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

Alternative 2: 94.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6.6 \cdot 10^{+49}:\\ \;\;\;\;x - \frac{\log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right)}{t}\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{+161}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\log \left(\mathsf{fma}\left(z, y, 1\right)\right)}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -6.6e+49)
   (- x (/ (log (fma (expm1 z) y 1.0)) t))
   (if (<= y 6.2e+161)
     (fma (/ (expm1 z) (- t)) y x)
     (- x (/ (log (fma z y 1.0)) t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -6.6e+49) {
		tmp = x - (log(fma(expm1(z), y, 1.0)) / t);
	} else if (y <= 6.2e+161) {
		tmp = fma((expm1(z) / -t), y, x);
	} else {
		tmp = x - (log(fma(z, y, 1.0)) / t);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -6.6e+49)
		tmp = Float64(x - Float64(log(fma(expm1(z), y, 1.0)) / t));
	elseif (y <= 6.2e+161)
		tmp = fma(Float64(expm1(z) / Float64(-t)), y, x);
	else
		tmp = Float64(x - Float64(log(fma(z, y, 1.0)) / t));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -6.6e+49], N[(x - N[(N[Log[N[(N[(Exp[z] - 1), $MachinePrecision] * y + 1.0), $MachinePrecision]], $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.2e+161], N[(N[(N[(Exp[z] - 1), $MachinePrecision] / (-t)), $MachinePrecision] * y + x), $MachinePrecision], N[(x - N[(N[Log[N[(z * y + 1.0), $MachinePrecision]], $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -6.5999999999999997e49

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. lower-expm1.f64 81.4

         \[\leadsto x - \frac{\log \left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right)}{t} \]

   4. Applied rewrites 81.4%

      \[\leadsto x - \frac{\log \color{blue}{\left(\mathsf{fma}\left(\mathsf{expm1}\left(z\right), y, 1\right)\right)}}{t} \]

   if -6.5999999999999997e49 < y < 6.20000000000000013e161

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. sub-div N/A

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

      5. frac-2neg N/A

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{neg}\left(\left(1 - e^{z}\right)\right)}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      6. sub-negate-rev N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{z} - 1}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      7. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{z} - 1}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      8. lower-expm1.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      9. lower-neg.f64 86.6

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right) \]

   4. Applied rewrites 86.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right)} \]

   if 6.20000000000000013e161 < y

   1. Initial program 60.6%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 70.4

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

   4. Applied rewrites 70.4%

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

Alternative 3: 88.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.25 \cdot 10^{+156}:\\ \;\;\;\;x - \frac{\log 1}{t}\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{+161}:\\ \;\;\;\;\mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\log \left(\mathsf{fma}\left(z, y, 1\right)\right)}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -3.25e+156)
   (- x (/ (log 1.0) t))
   (if (<= y 6.2e+161)
     (fma (/ (expm1 z) (- t)) y x)
     (- x (/ (log (fma z y 1.0)) t)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -3.25e+156) {
		tmp = x - (log(1.0) / t);
	} else if (y <= 6.2e+161) {
		tmp = fma((expm1(z) / -t), y, x);
	} else {
		tmp = x - (log(fma(z, y, 1.0)) / t);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -3.25e+156)
		tmp = Float64(x - Float64(log(1.0) / t));
	elseif (y <= 6.2e+161)
		tmp = fma(Float64(expm1(z) / Float64(-t)), y, x);
	else
		tmp = Float64(x - Float64(log(fma(z, y, 1.0)) / t));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -3.25e+156], N[(x - N[(N[Log[1.0], $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.2e+161], N[(N[(N[(Exp[z] - 1), $MachinePrecision] / (-t)), $MachinePrecision] * y + x), $MachinePrecision], N[(x - N[(N[Log[N[(z * y + 1.0), $MachinePrecision]], $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -3.25000000000000014e156

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

      \[\leadsto x - \frac{\log \color{blue}{1}}{t} \]
   3. Step-by-step derivation

   4. Applied rewrites 71.3%

      \[\leadsto x - \frac{\log \color{blue}{1}}{t} \]

   if -3.25000000000000014e156 < y < 6.20000000000000013e161

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

      4. sub-div N/A

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

      5. frac-2neg N/A

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{neg}\left(\left(1 - e^{z}\right)\right)}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      6. sub-negate-rev N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{z} - 1}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      7. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{z} - 1}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      8. lower-expm1.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{\mathsf{neg}\left(t\right)}, y, x\right) \]

      9. lower-neg.f64 86.6

         \[\leadsto \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right) \]

   4. Applied rewrites 86.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right)} \]

   if 6.20000000000000013e161 < y

   1. Initial program 60.6%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 70.4

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

   4. Applied rewrites 70.4%

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

Alternative 4: 88.3% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.25 \cdot 10^{+156}:\\ \;\;\;\;x - \frac{\log 1}{t}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\mathsf{expm1}\left(z\right) \cdot y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -3.25e+156) (- x (/ (log 1.0) t)) (- x (/ (* (expm1 z) y) t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -3.25e+156) {
		tmp = x - (log(1.0) / t);
	} else {
		tmp = x - ((expm1(z) * y) / t);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -3.25e+156) {
		tmp = x - (Math.log(1.0) / t);
	} else {
		tmp = x - ((Math.expm1(z) * y) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -3.25e+156:
		tmp = x - (math.log(1.0) / t)
	else:
		tmp = x - ((math.expm1(z) * y) / t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -3.25e+156)
		tmp = Float64(x - Float64(log(1.0) / t));
	else
		tmp = Float64(x - Float64(Float64(expm1(z) * y) / t));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -3.25e+156], N[(x - N[(N[Log[1.0], $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(N[(Exp[z] - 1), $MachinePrecision] * y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.25000000000000014e156

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

      \[\leadsto x - \frac{\log \color{blue}{1}}{t} \]
   3. Step-by-step derivation

   4. Applied rewrites 71.3%

      \[\leadsto x - \frac{\log \color{blue}{1}}{t} \]

   if -3.25000000000000014e156 < y

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower-expm1.f64 85.6

         \[\leadsto x - \frac{\mathsf{expm1}\left(z\right) \cdot y}{t} \]

   4. Applied rewrites 85.6%

      \[\leadsto x - \frac{\color{blue}{\mathsf{expm1}\left(z\right) \cdot y}}{t} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 86.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\log \left(\left(1 - y\right) + y \cdot e^{z}\right) \leq 0:\\ \;\;\;\;x - \frac{\mathsf{expm1}\left(z\right)}{t} \cdot y\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\log 1}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (log (+ (- 1.0 y) (* y (exp z)))) 0.0)
   (- x (* (/ (expm1 z) t) y))
   (- x (/ (log 1.0) t))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (log(((1.0 - y) + (y * exp(z)))) <= 0.0) {
		tmp = x - ((expm1(z) / t) * y);
	} else {
		tmp = x - (log(1.0) / t);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (Math.log(((1.0 - y) + (y * Math.exp(z)))) <= 0.0) {
		tmp = x - ((Math.expm1(z) / t) * y);
	} else {
		tmp = x - (Math.log(1.0) / t);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if math.log(((1.0 - y) + (y * math.exp(z)))) <= 0.0:
		tmp = x - ((math.expm1(z) / t) * y)
	else:
		tmp = x - (math.log(1.0) / t)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (log(Float64(Float64(1.0 - y) + Float64(y * exp(z)))) <= 0.0)
		tmp = Float64(x - Float64(Float64(expm1(z) / t) * y));
	else
		tmp = Float64(x - Float64(log(1.0) / t));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[Log[N[(N[(1.0 - y), $MachinePrecision] + N[(y * N[Exp[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 0.0], N[(x - N[(N[(N[(Exp[z] - 1), $MachinePrecision] / t), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[Log[1.0], $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (log.f64 (+.f64 (-.f64 #s(literal 1 binary64) y) (*.f64 y (exp.f64 z)))) < 0.0

   1. Initial program 60.6%

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

   2. Taylor expanded in z around 0

      \[\leadsto x - \frac{\color{blue}{y \cdot z}}{t} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x - \frac{z \cdot \color{blue}{y}}{t} \]

      2. lower-*.f64 74.0

         \[\leadsto x - \frac{z \cdot \color{blue}{y}}{t} \]

   4. Applied rewrites 74.0%

      \[\leadsto x - \frac{\color{blue}{z \cdot y}}{t} \]

   5. Taylor expanded in y around 0

      \[\leadsto x - \color{blue}{y \cdot \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x - \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right) \cdot \color{blue}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto x - \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right) \cdot \color{blue}{y} \]

   7. Applied rewrites 83.2%

      \[\leadsto x - \color{blue}{\mathsf{fma}\left(y, \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right) \cdot \mathsf{expm1}\left(z\right)}{t}, -0.5, \mathsf{fma}\left(\frac{{\left(\mathsf{expm1}\left(z\right)\right)}^{3}}{t}, 0.3333333333333333, \frac{{\left(\mathsf{expm1}\left(z\right)\right)}^{4} \cdot y}{t} \cdot -0.25\right) \cdot y\right), \frac{\mathsf{expm1}\left(z\right)}{t}\right) \cdot y} \]

   8. Taylor expanded in y around 0

      \[\leadsto x - \left(\frac{e^{z}}{t} - \frac{1}{t}\right) \cdot y \]
   9. Step-by-step derivation

      1. sub-div N/A

         \[\leadsto x - \frac{e^{z} - 1}{t} \cdot y \]

      2. lift-expm1.f64 N/A

         \[\leadsto x - \frac{\mathsf{expm1}\left(z\right)}{t} \cdot y \]

      3. lift-/.f64 86.6

         \[\leadsto x - \frac{\mathsf{expm1}\left(z\right)}{t} \cdot y \]

   10. Applied rewrites 86.6%

       \[\leadsto x - \frac{\mathsf{expm1}\left(z\right)}{t} \cdot y \]

   if 0.0 < (log.f64 (+.f64 (-.f64 #s(literal 1 binary64) y) (*.f64 y (exp.f64 z))))

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

      \[\leadsto x - \frac{\log \color{blue}{1}}{t} \]
   3. Step-by-step derivation

   4. Applied rewrites 71.3%

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

Alternative 6: 82.3% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.2 \cdot 10^{-9}:\\ \;\;\;\;x - \frac{\log 1}{t}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{z}{t} \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= z -8.2e-9) (- x (/ (log 1.0) t)) (- x (* (/ z t) y))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -8.2e-9) {
		tmp = x - (log(1.0) / t);
	} else {
		tmp = x - ((z / t) * 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, 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 <= (-8.2d-9)) then
        tmp = x - (log(1.0d0) / t)
    else
        tmp = x - ((z / t) * y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (z <= -8.2e-9) {
		tmp = x - (Math.log(1.0) / t);
	} else {
		tmp = x - ((z / t) * y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if z <= -8.2e-9:
		tmp = x - (math.log(1.0) / t)
	else:
		tmp = x - ((z / t) * y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (z <= -8.2e-9)
		tmp = Float64(x - Float64(log(1.0) / t));
	else
		tmp = Float64(x - Float64(Float64(z / t) * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (z <= -8.2e-9)
		tmp = x - (log(1.0) / t);
	else
		tmp = x - ((z / t) * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[z, -8.2e-9], N[(x - N[(N[Log[1.0], $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(z / t), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -8.2000000000000006e-9

   1. Initial program 60.6%

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

   2. Taylor expanded in y around 0

      \[\leadsto x - \frac{\log \color{blue}{1}}{t} \]
   3. Step-by-step derivation

   4. Applied rewrites 71.3%

      \[\leadsto x - \frac{\log \color{blue}{1}}{t} \]

   if -8.2000000000000006e-9 < z

   1. Initial program 60.6%

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

   2. Taylor expanded in z around 0

      \[\leadsto x - \frac{\color{blue}{y \cdot z}}{t} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x - \frac{z \cdot \color{blue}{y}}{t} \]

      2. lower-*.f64 74.0

         \[\leadsto x - \frac{z \cdot \color{blue}{y}}{t} \]

   4. Applied rewrites 74.0%

      \[\leadsto x - \frac{\color{blue}{z \cdot y}}{t} \]

   5. Taylor expanded in y around 0

      \[\leadsto x - \color{blue}{y \cdot \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x - \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right) \cdot \color{blue}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto x - \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right) \cdot \color{blue}{y} \]

   7. Applied rewrites 83.2%

      \[\leadsto x - \color{blue}{\mathsf{fma}\left(y, \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right) \cdot \mathsf{expm1}\left(z\right)}{t}, -0.5, \mathsf{fma}\left(\frac{{\left(\mathsf{expm1}\left(z\right)\right)}^{3}}{t}, 0.3333333333333333, \frac{{\left(\mathsf{expm1}\left(z\right)\right)}^{4} \cdot y}{t} \cdot -0.25\right) \cdot y\right), \frac{\mathsf{expm1}\left(z\right)}{t}\right) \cdot y} \]

   8. Taylor expanded in z around 0

      \[\leadsto x - \frac{z}{t} \cdot y \]
   9. Step-by-step derivation

      1. lower-/.f64 74.7

         \[\leadsto x - \frac{z}{t} \cdot y \]

   10. Applied rewrites 74.7%

       \[\leadsto x - \frac{z}{t} \cdot y \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 75.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\log \left(\left(1 - y\right) + y \cdot e^{z}\right) \leq -\infty:\\ \;\;\;\;x - \frac{z \cdot y}{t}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{-y}{t}, z, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (log (+ (- 1.0 y) (* y (exp z)))) (- INFINITY))
   (- x (/ (* z y) t))
   (fma (/ (- y) t) z x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (log(((1.0 - y) + (y * exp(z)))) <= -((double) INFINITY)) {
		tmp = x - ((z * y) / t);
	} else {
		tmp = fma((-y / t), z, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (log(Float64(Float64(1.0 - y) + Float64(y * exp(z)))) <= Float64(-Inf))
		tmp = Float64(x - Float64(Float64(z * y) / t));
	else
		tmp = fma(Float64(Float64(-y) / t), z, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[Log[N[(N[(1.0 - y), $MachinePrecision] + N[(y * N[Exp[z], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], (-Infinity)], N[(x - N[(N[(z * y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(N[((-y) / t), $MachinePrecision] * z + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (log.f64 (+.f64 (-.f64 #s(literal 1 binary64) y) (*.f64 y (exp.f64 z)))) < -inf.0

   1. Initial program 60.6%

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

   2. Taylor expanded in z around 0

      \[\leadsto x - \frac{\color{blue}{y \cdot z}}{t} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto x - \frac{z \cdot \color{blue}{y}}{t} \]

      2. lower-*.f64 74.0

         \[\leadsto x - \frac{z \cdot \color{blue}{y}}{t} \]

   4. Applied rewrites 74.0%

      \[\leadsto x - \frac{\color{blue}{z \cdot y}}{t} \]

   if -inf.0 < (log.f64 (+.f64 (-.f64 #s(literal 1 binary64) y) (*.f64 y (exp.f64 z))))

   1. Initial program 60.6%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

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

   4. Applied rewrites 61.4%

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

   5. Taylor expanded in z around 0

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

      1. associate-*r/ N/A

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

      2. mul-1-neg N/A

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

      3. lower-/.f64 N/A

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

      4. lower-neg.f64 72.7

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

   7. Applied rewrites 72.7%

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

Alternative 8: 74.7% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ x - \frac{z}{t} \cdot y \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 60.6%

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

2. Taylor expanded in z around 0

   \[\leadsto x - \frac{\color{blue}{y \cdot z}}{t} \]
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto x - \frac{z \cdot \color{blue}{y}}{t} \]

   2. lower-*.f64 74.0

      \[\leadsto x - \frac{z \cdot \color{blue}{y}}{t} \]

4. Applied rewrites 74.0%

   \[\leadsto x - \frac{\color{blue}{z \cdot y}}{t} \]

5. Taylor expanded in y around 0

   \[\leadsto x - \color{blue}{y \cdot \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right)} \]
6. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto x - \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right) \cdot \color{blue}{y} \]

   2. lower-*.f64 N/A

      \[\leadsto x - \left(\left(y \cdot \left(\frac{-1}{2} \cdot \frac{{\left(e^{z} - 1\right)}^{2}}{t} + y \cdot \left(\frac{-1}{4} \cdot \frac{y \cdot {\left(e^{z} - 1\right)}^{4}}{t} + \frac{1}{3} \cdot \frac{{\left(e^{z} - 1\right)}^{3}}{t}\right)\right) + \frac{e^{z}}{t}\right) - \frac{1}{t}\right) \cdot \color{blue}{y} \]

7. Applied rewrites 83.2%

   \[\leadsto x - \color{blue}{\mathsf{fma}\left(y, \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right) \cdot \mathsf{expm1}\left(z\right)}{t}, -0.5, \mathsf{fma}\left(\frac{{\left(\mathsf{expm1}\left(z\right)\right)}^{3}}{t}, 0.3333333333333333, \frac{{\left(\mathsf{expm1}\left(z\right)\right)}^{4} \cdot y}{t} \cdot -0.25\right) \cdot y\right), \frac{\mathsf{expm1}\left(z\right)}{t}\right) \cdot y} \]

8. Taylor expanded in z around 0

   \[\leadsto x - \frac{z}{t} \cdot y \]
9. Step-by-step derivation

   1. lower-/.f64 74.7

      \[\leadsto x - \frac{z}{t} \cdot y \]

10. Applied rewrites 74.7%

    \[\leadsto x - \frac{z}{t} \cdot y \]
11. Add Preprocessing

Alternative 9: 14.8% accurate, 3.8× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 60.6%

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

2. Taylor expanded in y around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. sub-div N/A

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

   5. frac-2neg N/A

      \[\leadsto \mathsf{fma}\left(\frac{\mathsf{neg}\left(\left(1 - e^{z}\right)\right)}{\mathsf{neg}\left(t\right)}, y, x\right) \]

   6. sub-negate-rev N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{z} - 1}{\mathsf{neg}\left(t\right)}, y, x\right) \]

   7. lower-/.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{z} - 1}{\mathsf{neg}\left(t\right)}, y, x\right) \]

   8. lower-expm1.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{\mathsf{neg}\left(t\right)}, y, x\right) \]

   9. lower-neg.f64 86.6

      \[\leadsto \mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right) \]

4. Applied rewrites 86.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{expm1}\left(z\right)}{-t}, y, x\right)} \]

5. Taylor expanded in x around 0

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

   1. associate-/l* N/A

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

   2. sub-div N/A

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

   3. associate-*r* N/A

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

   4. mul-1-neg N/A

      \[\leadsto \left(\mathsf{neg}\left(y\right)\right) \cdot \left(\frac{e^{z}}{t} - \frac{\color{blue}{1}}{t}\right) \]

   5. lower-*.f64 N/A

      \[\leadsto \left(\mathsf{neg}\left(y\right)\right) \cdot \left(\frac{e^{z}}{t} - \color{blue}{\frac{1}{t}}\right) \]

   6. lower-neg.f64 N/A

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

   7. sub-div N/A

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

   8. lower-/.f64 N/A

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

   9. lift-expm1.f64 21.0

      \[\leadsto \left(-y\right) \cdot \frac{\mathsf{expm1}\left(z\right)}{t} \]

7. Applied rewrites 21.0%

   \[\leadsto \left(-y\right) \cdot \color{blue}{\frac{\mathsf{expm1}\left(z\right)}{t}} \]

8. Taylor expanded in z around 0

   \[\leadsto -1 \cdot \frac{y \cdot z}{\color{blue}{t}} \]
9. Step-by-step derivation

   1. associate-*r/ N/A

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

   2. lower-/.f64 N/A

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

   3. associate-*r* N/A

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

   4. mul-1-neg N/A

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

   5. lift-neg.f64 N/A

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

   6. lower-*.f64 14.8

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

10. Applied rewrites 14.8%

    \[\leadsto \frac{\left(-y\right) \cdot z}{t} \]
11. Add Preprocessing

Reproduce

herbie shell --seed 2025134 
(FPCore (x y z t)
  :name "System.Random.MWC.Distributions:truncatedExp from mwc-random-0.13.3.2"
  :precision binary64
  (- x (/ (log (+ (- 1.0 y) (* y (exp z)))) t)))