Data.Random.Distribution.Triangular:triangularCDF from random-fu-0.2.6.2, A

Percentage Accurate: 98.9% → 98.9%

Time: 3.5s

Alternatives: 13

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.9%

   \[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)} \]
2. Add Preprocessing

Alternative 2: 88.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ \mathbf{if}\;t\_1 \leq 0.9999999925586256:\\ \;\;\;\;\frac{x}{\left(y - t\right) \cdot z} + 1\\ \mathbf{elif}\;t\_1 \leq 2:\\ \;\;\;\;1\\ \mathbf{elif}\;t\_1 \leq 10^{+69}:\\ \;\;\;\;-\frac{x}{y \cdot \left(y - z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\left(y - z\right) \cdot t} + 1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- 1.0 (/ x (* (- y z) (- y t))))))
   (if (<= t_1 0.9999999925586256)
     (+ (/ x (* (- y t) z)) 1.0)
     (if (<= t_1 2.0)
       1.0
       (if (<= t_1 1e+69)
         (- (/ x (* y (- y z))))
         (+ (/ x (* (- y z) t)) 1.0))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_1 <= 0.9999999925586256) {
		tmp = (x / ((y - t) * z)) + 1.0;
	} else if (t_1 <= 2.0) {
		tmp = 1.0;
	} else if (t_1 <= 1e+69) {
		tmp = -(x / (y * (y - z)));
	} else {
		tmp = (x / ((y - z) * t)) + 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 = 1.0d0 - (x / ((y - z) * (y - t)))
    if (t_1 <= 0.9999999925586256d0) then
        tmp = (x / ((y - t) * z)) + 1.0d0
    else if (t_1 <= 2.0d0) then
        tmp = 1.0d0
    else if (t_1 <= 1d+69) then
        tmp = -(x / (y * (y - z)))
    else
        tmp = (x / ((y - z) * t)) + 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_1 <= 0.9999999925586256) {
		tmp = (x / ((y - t) * z)) + 1.0;
	} else if (t_1 <= 2.0) {
		tmp = 1.0;
	} else if (t_1 <= 1e+69) {
		tmp = -(x / (y * (y - z)));
	} else {
		tmp = (x / ((y - z) * t)) + 1.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = 1.0 - (x / ((y - z) * (y - t)))
	tmp = 0
	if t_1 <= 0.9999999925586256:
		tmp = (x / ((y - t) * z)) + 1.0
	elif t_1 <= 2.0:
		tmp = 1.0
	elif t_1 <= 1e+69:
		tmp = -(x / (y * (y - z)))
	else:
		tmp = (x / ((y - z) * t)) + 1.0
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(1.0 - Float64(x / Float64(Float64(y - z) * Float64(y - t))))
	tmp = 0.0
	if (t_1 <= 0.9999999925586256)
		tmp = Float64(Float64(x / Float64(Float64(y - t) * z)) + 1.0);
	elseif (t_1 <= 2.0)
		tmp = 1.0;
	elseif (t_1 <= 1e+69)
		tmp = Float64(-Float64(x / Float64(y * Float64(y - z))));
	else
		tmp = Float64(Float64(x / Float64(Float64(y - z) * t)) + 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = 1.0 - (x / ((y - z) * (y - t)));
	tmp = 0.0;
	if (t_1 <= 0.9999999925586256)
		tmp = (x / ((y - t) * z)) + 1.0;
	elseif (t_1 <= 2.0)
		tmp = 1.0;
	elseif (t_1 <= 1e+69)
		tmp = -(x / (y * (y - z)));
	else
		tmp = (x / ((y - z) * t)) + 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(1.0 - N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 0.9999999925586256], N[(N[(x / N[(N[(y - t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision], If[LessEqual[t$95$1, 2.0], 1.0, If[LessEqual[t$95$1, 1e+69], (-N[(x / N[(y * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), N[(N[(x / N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 0.99999999255862559

   1. Initial program 96.5%

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

   2. Taylor expanded in z around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.4

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

   4. Applied rewrites 61.4%

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

   if 0.99999999255862559 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 2

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 99.5%

      \[\leadsto \color{blue}{1} \]

   if 2 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 1.0000000000000001e69

   1. Initial program 98.8%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. inv-pow N/A

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

      5. lower-pow.f64 N/A

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

      6. associate-/r* N/A

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

      7. lower-/.f64 N/A

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

      8. inv-pow N/A

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

      9. lower-pow.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 97.8

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

   4. Applied rewrites 97.8%

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

   5. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift--.f64 89.3

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

   7. Applied rewrites 89.3%

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

   8. Taylor expanded in y around inf

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

   10. Applied rewrites 34.1%

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

   if 1.0000000000000001e69 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))))

   1. Initial program 94.5%

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

   2. Taylor expanded in t around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.6

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

   4. Applied rewrites 61.6%

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

Alternative 3: 88.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ \mathbf{if}\;t\_1 \leq 0.9999999925586256:\\ \;\;\;\;\frac{x}{\left(y - t\right) \cdot z} + 1\\ \mathbf{elif}\;t\_1 \leq 2:\\ \;\;\;\;1\\ \mathbf{elif}\;t\_1 \leq 10^{+69}:\\ \;\;\;\;-\frac{x}{y \cdot \left(y - z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{t \cdot \left(y - z\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- 1.0 (/ x (* (- y z) (- y t))))))
   (if (<= t_1 0.9999999925586256)
     (+ (/ x (* (- y t) z)) 1.0)
     (if (<= t_1 2.0)
       1.0
       (if (<= t_1 1e+69) (- (/ x (* y (- y z)))) (/ x (* t (- y z))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_1 <= 0.9999999925586256) {
		tmp = (x / ((y - t) * z)) + 1.0;
	} else if (t_1 <= 2.0) {
		tmp = 1.0;
	} else if (t_1 <= 1e+69) {
		tmp = -(x / (y * (y - z)));
	} else {
		tmp = x / (t * (y - z));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 = 1.0d0 - (x / ((y - z) * (y - t)))
    if (t_1 <= 0.9999999925586256d0) then
        tmp = (x / ((y - t) * z)) + 1.0d0
    else if (t_1 <= 2.0d0) then
        tmp = 1.0d0
    else if (t_1 <= 1d+69) then
        tmp = -(x / (y * (y - z)))
    else
        tmp = x / (t * (y - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_1 <= 0.9999999925586256) {
		tmp = (x / ((y - t) * z)) + 1.0;
	} else if (t_1 <= 2.0) {
		tmp = 1.0;
	} else if (t_1 <= 1e+69) {
		tmp = -(x / (y * (y - z)));
	} else {
		tmp = x / (t * (y - z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = 1.0 - (x / ((y - z) * (y - t)))
	tmp = 0
	if t_1 <= 0.9999999925586256:
		tmp = (x / ((y - t) * z)) + 1.0
	elif t_1 <= 2.0:
		tmp = 1.0
	elif t_1 <= 1e+69:
		tmp = -(x / (y * (y - z)))
	else:
		tmp = x / (t * (y - z))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(1.0 - Float64(x / Float64(Float64(y - z) * Float64(y - t))))
	tmp = 0.0
	if (t_1 <= 0.9999999925586256)
		tmp = Float64(Float64(x / Float64(Float64(y - t) * z)) + 1.0);
	elseif (t_1 <= 2.0)
		tmp = 1.0;
	elseif (t_1 <= 1e+69)
		tmp = Float64(-Float64(x / Float64(y * Float64(y - z))));
	else
		tmp = Float64(x / Float64(t * Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = 1.0 - (x / ((y - z) * (y - t)));
	tmp = 0.0;
	if (t_1 <= 0.9999999925586256)
		tmp = (x / ((y - t) * z)) + 1.0;
	elseif (t_1 <= 2.0)
		tmp = 1.0;
	elseif (t_1 <= 1e+69)
		tmp = -(x / (y * (y - z)));
	else
		tmp = x / (t * (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(1.0 - N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 0.9999999925586256], N[(N[(x / N[(N[(y - t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision], If[LessEqual[t$95$1, 2.0], 1.0, If[LessEqual[t$95$1, 1e+69], (-N[(x / N[(y * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), N[(x / N[(t * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 0.99999999255862559

   1. Initial program 96.5%

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

   2. Taylor expanded in z around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.4

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

   4. Applied rewrites 61.4%

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

   if 0.99999999255862559 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 2

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 99.5%

      \[\leadsto \color{blue}{1} \]

   if 2 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 1.0000000000000001e69

   1. Initial program 98.8%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. inv-pow N/A

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

      5. lower-pow.f64 N/A

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

      6. associate-/r* N/A

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

      7. lower-/.f64 N/A

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

      8. inv-pow N/A

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

      9. lower-pow.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 97.8

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

   4. Applied rewrites 97.8%

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

   5. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift--.f64 89.3

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

   7. Applied rewrites 89.3%

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

   8. Taylor expanded in y around inf

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

   10. Applied rewrites 34.1%

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

   if 1.0000000000000001e69 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))))

   1. Initial program 94.5%

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

   2. Taylor expanded in t around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.6

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

   4. Applied rewrites 61.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lift--.f64 61.6

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

   7. Applied rewrites 61.6%

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

Alternative 4: 88.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ \mathbf{if}\;t\_1 \leq -5000:\\ \;\;\;\;\frac{x}{z \cdot \left(y - t\right)}\\ \mathbf{elif}\;t\_1 \leq 2:\\ \;\;\;\;1\\ \mathbf{elif}\;t\_1 \leq 10^{+69}:\\ \;\;\;\;-\frac{x}{y \cdot \left(y - z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{t \cdot \left(y - z\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- 1.0 (/ x (* (- y z) (- y t))))))
   (if (<= t_1 -5000.0)
     (/ x (* z (- y t)))
     (if (<= t_1 2.0)
       1.0
       (if (<= t_1 1e+69) (- (/ x (* y (- y z)))) (/ x (* t (- y z))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_1 <= -5000.0) {
		tmp = x / (z * (y - t));
	} else if (t_1 <= 2.0) {
		tmp = 1.0;
	} else if (t_1 <= 1e+69) {
		tmp = -(x / (y * (y - z)));
	} else {
		tmp = x / (t * (y - z));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 = 1.0d0 - (x / ((y - z) * (y - t)))
    if (t_1 <= (-5000.0d0)) then
        tmp = x / (z * (y - t))
    else if (t_1 <= 2.0d0) then
        tmp = 1.0d0
    else if (t_1 <= 1d+69) then
        tmp = -(x / (y * (y - z)))
    else
        tmp = x / (t * (y - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_1 <= -5000.0) {
		tmp = x / (z * (y - t));
	} else if (t_1 <= 2.0) {
		tmp = 1.0;
	} else if (t_1 <= 1e+69) {
		tmp = -(x / (y * (y - z)));
	} else {
		tmp = x / (t * (y - z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = 1.0 - (x / ((y - z) * (y - t)))
	tmp = 0
	if t_1 <= -5000.0:
		tmp = x / (z * (y - t))
	elif t_1 <= 2.0:
		tmp = 1.0
	elif t_1 <= 1e+69:
		tmp = -(x / (y * (y - z)))
	else:
		tmp = x / (t * (y - z))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(1.0 - Float64(x / Float64(Float64(y - z) * Float64(y - t))))
	tmp = 0.0
	if (t_1 <= -5000.0)
		tmp = Float64(x / Float64(z * Float64(y - t)));
	elseif (t_1 <= 2.0)
		tmp = 1.0;
	elseif (t_1 <= 1e+69)
		tmp = Float64(-Float64(x / Float64(y * Float64(y - z))));
	else
		tmp = Float64(x / Float64(t * Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = 1.0 - (x / ((y - z) * (y - t)));
	tmp = 0.0;
	if (t_1 <= -5000.0)
		tmp = x / (z * (y - t));
	elseif (t_1 <= 2.0)
		tmp = 1.0;
	elseif (t_1 <= 1e+69)
		tmp = -(x / (y * (y - z)));
	else
		tmp = x / (t * (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(1.0 - N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5000.0], N[(x / N[(z * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 2.0], 1.0, If[LessEqual[t$95$1, 1e+69], (-N[(x / N[(y * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), N[(x / N[(t * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < -5e3

   1. Initial program 96.3%

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

   2. Taylor expanded in z around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.4

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

   4. Applied rewrites 61.4%

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

   5. Taylor expanded in y around inf

      \[\leadsto \frac{x}{y \cdot z} + 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 27.8%

      \[\leadsto \frac{x}{y \cdot z} + 1 \]

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lift--.f64 60.6

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

   10. Applied rewrites 60.6%

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

   if -5e3 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 2

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 98.8%

      \[\leadsto \color{blue}{1} \]

   if 2 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 1.0000000000000001e69

   1. Initial program 98.8%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. inv-pow N/A

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

      5. lower-pow.f64 N/A

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

      6. associate-/r* N/A

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

      7. lower-/.f64 N/A

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

      8. inv-pow N/A

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

      9. lower-pow.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 97.8

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

   4. Applied rewrites 97.8%

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

   5. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift--.f64 89.3

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

   7. Applied rewrites 89.3%

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

   8. Taylor expanded in y around inf

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

   10. Applied rewrites 34.1%

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

   if 1.0000000000000001e69 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))))

   1. Initial program 94.5%

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

   2. Taylor expanded in t around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.6

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

   4. Applied rewrites 61.6%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lift--.f64 61.6

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

   7. Applied rewrites 61.6%

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

Alternative 5: 97.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -\frac{x}{\left(y - t\right) \cdot \left(y - z\right)}\\ t_2 := 1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ \mathbf{if}\;t\_2 \leq -10:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 2:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (/ x (* (- y t) (- y z)))))
        (t_2 (- 1.0 (/ x (* (- y z) (- y t))))))
   (if (<= t_2 -10.0) t_1 (if (<= t_2 2.0) 1.0 t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = -(x / ((y - t) * (y - z)));
	double t_2 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_2 <= -10.0) {
		tmp = t_1;
	} else if (t_2 <= 2.0) {
		tmp = 1.0;
	} 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) :: t_2
    real(8) :: tmp
    t_1 = -(x / ((y - t) * (y - z)))
    t_2 = 1.0d0 - (x / ((y - z) * (y - t)))
    if (t_2 <= (-10.0d0)) then
        tmp = t_1
    else if (t_2 <= 2.0d0) then
        tmp = 1.0d0
    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 = -(x / ((y - t) * (y - z)));
	double t_2 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_2 <= -10.0) {
		tmp = t_1;
	} else if (t_2 <= 2.0) {
		tmp = 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = -(x / ((y - t) * (y - z)))
	t_2 = 1.0 - (x / ((y - z) * (y - t)))
	tmp = 0
	if t_2 <= -10.0:
		tmp = t_1
	elif t_2 <= 2.0:
		tmp = 1.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(-Float64(x / Float64(Float64(y - t) * Float64(y - z))))
	t_2 = Float64(1.0 - Float64(x / Float64(Float64(y - z) * Float64(y - t))))
	tmp = 0.0
	if (t_2 <= -10.0)
		tmp = t_1;
	elseif (t_2 <= 2.0)
		tmp = 1.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = -(x / ((y - t) * (y - z)));
	t_2 = 1.0 - (x / ((y - z) * (y - t)));
	tmp = 0.0;
	if (t_2 <= -10.0)
		tmp = t_1;
	elseif (t_2 <= 2.0)
		tmp = 1.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = (-N[(x / N[(N[(y - t), $MachinePrecision] * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision])}, Block[{t$95$2 = N[(1.0 - N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -10.0], t$95$1, If[LessEqual[t$95$2, 2.0], 1.0, t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < -10 or 2 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))))

   1. Initial program 95.9%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. inv-pow N/A

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

      5. lower-pow.f64 N/A

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

      6. associate-/r* N/A

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

      7. lower-/.f64 N/A

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

      8. inv-pow N/A

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

      9. lower-pow.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 95.2

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

   4. Applied rewrites 95.2%

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

   5. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift--.f64 93.8

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

   7. Applied rewrites 93.8%

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

   if -10 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 2

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 99.0%

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

Alternative 6: 88.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ \mathbf{if}\;t\_1 \leq -5000:\\ \;\;\;\;\frac{x}{z \cdot \left(y - t\right)}\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+25}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{t \cdot \left(y - z\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- 1.0 (/ x (* (- y z) (- y t))))))
   (if (<= t_1 -5000.0)
     (/ x (* z (- y t)))
     (if (<= t_1 5e+25) 1.0 (/ x (* t (- y z)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_1 <= -5000.0) {
		tmp = x / (z * (y - t));
	} else if (t_1 <= 5e+25) {
		tmp = 1.0;
	} else {
		tmp = x / (t * (y - z));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 = 1.0d0 - (x / ((y - z) * (y - t)))
    if (t_1 <= (-5000.0d0)) then
        tmp = x / (z * (y - t))
    else if (t_1 <= 5d+25) then
        tmp = 1.0d0
    else
        tmp = x / (t * (y - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_1 <= -5000.0) {
		tmp = x / (z * (y - t));
	} else if (t_1 <= 5e+25) {
		tmp = 1.0;
	} else {
		tmp = x / (t * (y - z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = 1.0 - (x / ((y - z) * (y - t)))
	tmp = 0
	if t_1 <= -5000.0:
		tmp = x / (z * (y - t))
	elif t_1 <= 5e+25:
		tmp = 1.0
	else:
		tmp = x / (t * (y - z))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(1.0 - Float64(x / Float64(Float64(y - z) * Float64(y - t))))
	tmp = 0.0
	if (t_1 <= -5000.0)
		tmp = Float64(x / Float64(z * Float64(y - t)));
	elseif (t_1 <= 5e+25)
		tmp = 1.0;
	else
		tmp = Float64(x / Float64(t * Float64(y - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = 1.0 - (x / ((y - z) * (y - t)));
	tmp = 0.0;
	if (t_1 <= -5000.0)
		tmp = x / (z * (y - t));
	elseif (t_1 <= 5e+25)
		tmp = 1.0;
	else
		tmp = x / (t * (y - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(1.0 - N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5000.0], N[(x / N[(z * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 5e+25], 1.0, N[(x / N[(t * N[(y - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < -5e3

   1. Initial program 96.3%

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

   2. Taylor expanded in z around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.4

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

   4. Applied rewrites 61.4%

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

   5. Taylor expanded in y around inf

      \[\leadsto \frac{x}{y \cdot z} + 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 27.8%

      \[\leadsto \frac{x}{y \cdot z} + 1 \]

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lift--.f64 60.6

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

   10. Applied rewrites 60.6%

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

   if -5e3 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 5.00000000000000024e25

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 97.4%

      \[\leadsto \color{blue}{1} \]

   if 5.00000000000000024e25 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))))

   1. Initial program 95.2%

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

   2. Taylor expanded in t around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.9

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

   4. Applied rewrites 61.9%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lift--.f64 61.9

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

   7. Applied rewrites 61.9%

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

Alternative 7: 81.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-x}{y \cdot y}\\ t_2 := 1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ \mathbf{if}\;t\_2 \leq -10:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 10^{+21}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- x) (* y y))) (t_2 (- 1.0 (/ x (* (- y z) (- y t))))))
   (if (<= t_2 -10.0) t_1 (if (<= t_2 1e+21) 1.0 t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = -x / (y * y);
	double t_2 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_2 <= -10.0) {
		tmp = t_1;
	} else if (t_2 <= 1e+21) {
		tmp = 1.0;
	} 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) :: t_2
    real(8) :: tmp
    t_1 = -x / (y * y)
    t_2 = 1.0d0 - (x / ((y - z) * (y - t)))
    if (t_2 <= (-10.0d0)) then
        tmp = t_1
    else if (t_2 <= 1d+21) then
        tmp = 1.0d0
    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 = -x / (y * y);
	double t_2 = 1.0 - (x / ((y - z) * (y - t)));
	double tmp;
	if (t_2 <= -10.0) {
		tmp = t_1;
	} else if (t_2 <= 1e+21) {
		tmp = 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = -x / (y * y)
	t_2 = 1.0 - (x / ((y - z) * (y - t)))
	tmp = 0
	if t_2 <= -10.0:
		tmp = t_1
	elif t_2 <= 1e+21:
		tmp = 1.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(-x) / Float64(y * y))
	t_2 = Float64(1.0 - Float64(x / Float64(Float64(y - z) * Float64(y - t))))
	tmp = 0.0
	if (t_2 <= -10.0)
		tmp = t_1;
	elseif (t_2 <= 1e+21)
		tmp = 1.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = -x / (y * y);
	t_2 = 1.0 - (x / ((y - z) * (y - t)));
	tmp = 0.0;
	if (t_2 <= -10.0)
		tmp = t_1;
	elseif (t_2 <= 1e+21)
		tmp = 1.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[((-x) / N[(y * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(1.0 - N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -10.0], t$95$1, If[LessEqual[t$95$2, 1e+21], 1.0, t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < -10 or 1e21 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))))

   1. Initial program 95.9%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. lower--.f64 N/A

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

      4. inv-pow N/A

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

      5. lower-pow.f64 N/A

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

      6. associate-/r* N/A

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

      7. lower-/.f64 N/A

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

      8. inv-pow N/A

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

      9. lower-pow.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 95.1

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

   4. Applied rewrites 95.1%

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

   5. Taylor expanded in x around inf

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift--.f64 94.7

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

   7. Applied rewrites 94.7%

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

   8. Taylor expanded in y around inf

      \[\leadsto -1 \cdot \color{blue}{\frac{x}{{y}^{2}}} \]
   9. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \frac{-1 \cdot x}{{y}^{\color{blue}{2}}} \]

      2. mul-1-neg N/A

         \[\leadsto \frac{\mathsf{neg}\left(x\right)}{{y}^{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{\mathsf{neg}\left(x\right)}{{y}^{\color{blue}{2}}} \]

      4. lower-neg.f64 N/A

         \[\leadsto \frac{-x}{{y}^{2}} \]

      5. unpow2 N/A

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

      6. lower-*.f64 30.3

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

   10. Applied rewrites 30.3%

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

   if -10 < (-.f64 #s(literal 1 binary64) (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))) < 1e21

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 97.9%

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

Alternative 8: 89.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ t_2 := \frac{x}{z \cdot \left(y - t\right)}\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+20}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 20:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (* (- y z) (- y t)))) (t_2 (/ x (* z (- y t)))))
   (if (<= t_1 -5e+20) t_2 (if (<= t_1 20.0) 1.0 t_2))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / ((y - z) * (y - t));
	double t_2 = x / (z * (y - t));
	double tmp;
	if (t_1 <= -5e+20) {
		tmp = t_2;
	} else if (t_1 <= 20.0) {
		tmp = 1.0;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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) :: t_2
    real(8) :: tmp
    t_1 = x / ((y - z) * (y - t))
    t_2 = x / (z * (y - t))
    if (t_1 <= (-5d+20)) then
        tmp = t_2
    else if (t_1 <= 20.0d0) then
        tmp = 1.0d0
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / ((y - z) * (y - t));
	double t_2 = x / (z * (y - t));
	double tmp;
	if (t_1 <= -5e+20) {
		tmp = t_2;
	} else if (t_1 <= 20.0) {
		tmp = 1.0;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / ((y - z) * (y - t))
	t_2 = x / (z * (y - t))
	tmp = 0
	if t_1 <= -5e+20:
		tmp = t_2
	elif t_1 <= 20.0:
		tmp = 1.0
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(Float64(y - z) * Float64(y - t)))
	t_2 = Float64(x / Float64(z * Float64(y - t)))
	tmp = 0.0
	if (t_1 <= -5e+20)
		tmp = t_2;
	elseif (t_1 <= 20.0)
		tmp = 1.0;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / ((y - z) * (y - t));
	t_2 = x / (z * (y - t));
	tmp = 0.0;
	if (t_1 <= -5e+20)
		tmp = t_2;
	elseif (t_1 <= 20.0)
		tmp = 1.0;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x / N[(z * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+20], t$95$2, If[LessEqual[t$95$1, 20.0], 1.0, t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))) < -5e20 or 20 < (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))

   1. Initial program 95.9%

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

   2. Taylor expanded in z around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 62.7

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

   4. Applied rewrites 62.7%

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

   5. Taylor expanded in y around inf

      \[\leadsto \frac{x}{y \cdot z} + 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 28.8%

      \[\leadsto \frac{x}{y \cdot z} + 1 \]

   8. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lift--.f64 62.1

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

   10. Applied rewrites 62.1%

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

   if -5e20 < (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))) < 20

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 97.9%

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

Alternative 9: 84.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ t_2 := \frac{x}{t \cdot \left(-z\right)}\\ \mathbf{if}\;t\_1 \leq -1 \cdot 10^{+26}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 50000000000:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (* (- y z) (- y t)))) (t_2 (/ x (* t (- z)))))
   (if (<= t_1 -1e+26) t_2 (if (<= t_1 50000000000.0) 1.0 t_2))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / ((y - z) * (y - t));
	double t_2 = x / (t * -z);
	double tmp;
	if (t_1 <= -1e+26) {
		tmp = t_2;
	} else if (t_1 <= 50000000000.0) {
		tmp = 1.0;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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) :: t_2
    real(8) :: tmp
    t_1 = x / ((y - z) * (y - t))
    t_2 = x / (t * -z)
    if (t_1 <= (-1d+26)) then
        tmp = t_2
    else if (t_1 <= 50000000000.0d0) then
        tmp = 1.0d0
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / ((y - z) * (y - t));
	double t_2 = x / (t * -z);
	double tmp;
	if (t_1 <= -1e+26) {
		tmp = t_2;
	} else if (t_1 <= 50000000000.0) {
		tmp = 1.0;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / ((y - z) * (y - t))
	t_2 = x / (t * -z)
	tmp = 0
	if t_1 <= -1e+26:
		tmp = t_2
	elif t_1 <= 50000000000.0:
		tmp = 1.0
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(Float64(y - z) * Float64(y - t)))
	t_2 = Float64(x / Float64(t * Float64(-z)))
	tmp = 0.0
	if (t_1 <= -1e+26)
		tmp = t_2;
	elseif (t_1 <= 50000000000.0)
		tmp = 1.0;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / ((y - z) * (y - t));
	t_2 = x / (t * -z);
	tmp = 0.0;
	if (t_1 <= -1e+26)
		tmp = t_2;
	elseif (t_1 <= 50000000000.0)
		tmp = 1.0;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x / N[(t * (-z)), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -1e+26], t$95$2, If[LessEqual[t$95$1, 50000000000.0], 1.0, t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))) < -1.00000000000000005e26 or 5e10 < (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))

   1. Initial program 95.8%

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

   2. Taylor expanded in t around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.7

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

   4. Applied rewrites 61.7%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lift--.f64 61.7

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

   7. Applied rewrites 61.7%

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

   8. Taylor expanded in y around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 46.2

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

   10. Applied rewrites 46.2%

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

   if -1.00000000000000005e26 < (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))) < 5e10

   1. Initial program 100.0%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 96.9%

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

Alternative 10: 80.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\\ t_2 := \frac{x}{t \cdot y}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+72}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 4 \cdot 10^{-7}:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (* (- y z) (- y t)))) (t_2 (/ x (* t y))))
   (if (<= t_1 -2e+72) t_2 (if (<= t_1 4e-7) 1.0 t_2))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / ((y - z) * (y - t));
	double t_2 = x / (t * y);
	double tmp;
	if (t_1 <= -2e+72) {
		tmp = t_2;
	} else if (t_1 <= 4e-7) {
		tmp = 1.0;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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) :: t_2
    real(8) :: tmp
    t_1 = x / ((y - z) * (y - t))
    t_2 = x / (t * y)
    if (t_1 <= (-2d+72)) then
        tmp = t_2
    else if (t_1 <= 4d-7) then
        tmp = 1.0d0
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x / ((y - z) * (y - t));
	double t_2 = x / (t * y);
	double tmp;
	if (t_1 <= -2e+72) {
		tmp = t_2;
	} else if (t_1 <= 4e-7) {
		tmp = 1.0;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / ((y - z) * (y - t))
	t_2 = x / (t * y)
	tmp = 0
	if t_1 <= -2e+72:
		tmp = t_2
	elif t_1 <= 4e-7:
		tmp = 1.0
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(Float64(y - z) * Float64(y - t)))
	t_2 = Float64(x / Float64(t * y))
	tmp = 0.0
	if (t_1 <= -2e+72)
		tmp = t_2;
	elseif (t_1 <= 4e-7)
		tmp = 1.0;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / ((y - z) * (y - t));
	t_2 = x / (t * y);
	tmp = 0.0;
	if (t_1 <= -2e+72)
		tmp = t_2;
	elseif (t_1 <= 4e-7)
		tmp = 1.0;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(N[(y - z), $MachinePrecision] * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x / N[(t * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+72], t$95$2, If[LessEqual[t$95$1, 4e-7], 1.0, t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))) < -1.99999999999999989e72 or 3.9999999999999998e-7 < (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t)))

   1. Initial program 95.6%

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

   2. Taylor expanded in t around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 61.3

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

   4. Applied rewrites 61.3%

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

   5. Taylor expanded in x around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lift--.f64 59.7

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

   7. Applied rewrites 59.7%

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

   8. Taylor expanded in y around inf

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

   10. Applied rewrites 28.8%

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

   if -1.99999999999999989e72 < (/.f64 x (*.f64 (-.f64 y z) (-.f64 y t))) < 3.9999999999999998e-7

   1. Initial program 99.9%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 95.8%

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

Alternative 11: 89.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.8 \cdot 10^{-56}:\\ \;\;\;\;1 - \frac{x}{\left(y - z\right) \cdot y}\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-103}:\\ \;\;\;\;\frac{x}{\left(y - t\right) \cdot z} + 1\\ \mathbf{else}:\\ \;\;\;\;1 - \frac{x}{y \cdot \left(y - t\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -7.8e-56)
   (- 1.0 (/ x (* (- y z) y)))
   (if (<= y 2.25e-103)
     (+ (/ x (* (- y t) z)) 1.0)
     (- 1.0 (/ x (* y (- y t)))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -7.8e-56) {
		tmp = 1.0 - (x / ((y - z) * y));
	} else if (y <= 2.25e-103) {
		tmp = (x / ((y - t) * z)) + 1.0;
	} else {
		tmp = 1.0 - (x / (y * (y - t)));
	}
	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 (y <= (-7.8d-56)) then
        tmp = 1.0d0 - (x / ((y - z) * y))
    else if (y <= 2.25d-103) then
        tmp = (x / ((y - t) * z)) + 1.0d0
    else
        tmp = 1.0d0 - (x / (y * (y - t)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -7.8e-56) {
		tmp = 1.0 - (x / ((y - z) * y));
	} else if (y <= 2.25e-103) {
		tmp = (x / ((y - t) * z)) + 1.0;
	} else {
		tmp = 1.0 - (x / (y * (y - t)));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if y <= -7.8e-56:
		tmp = 1.0 - (x / ((y - z) * y))
	elif y <= 2.25e-103:
		tmp = (x / ((y - t) * z)) + 1.0
	else:
		tmp = 1.0 - (x / (y * (y - t)))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -7.8e-56)
		tmp = Float64(1.0 - Float64(x / Float64(Float64(y - z) * y)));
	elseif (y <= 2.25e-103)
		tmp = Float64(Float64(x / Float64(Float64(y - t) * z)) + 1.0);
	else
		tmp = Float64(1.0 - Float64(x / Float64(y * Float64(y - t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (y <= -7.8e-56)
		tmp = 1.0 - (x / ((y - z) * y));
	elseif (y <= 2.25e-103)
		tmp = (x / ((y - t) * z)) + 1.0;
	else
		tmp = 1.0 - (x / (y * (y - t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -7.8e-56], N[(1.0 - N[(x / N[(N[(y - z), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 2.25e-103], N[(N[(x / N[(N[(y - t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision], N[(1.0 - N[(x / N[(y * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -7.8e-56

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf

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

   4. Applied rewrites 93.3%

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

   if -7.8e-56 < y < 2.25e-103

   1. Initial program 97.2%

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

   2. Taylor expanded in z around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 85.6

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

   4. Applied rewrites 85.6%

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

   if 2.25e-103 < y

   1. Initial program 99.9%

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

   2. Taylor expanded in y around inf

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

   4. Applied rewrites 89.5%

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

Alternative 12: 89.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 1 - \frac{x}{y \cdot \left(y - t\right)}\\ \mathbf{if}\;y \leq -3.1 \cdot 10^{-56}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-103}:\\ \;\;\;\;\frac{x}{\left(y - t\right) \cdot z} + 1\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- 1.0 (/ x (* y (- y t))))))
   (if (<= y -3.1e-56)
     t_1
     (if (<= y 2.25e-103) (+ (/ x (* (- y t) z)) 1.0) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (x / (y * (y - t)));
	double tmp;
	if (y <= -3.1e-56) {
		tmp = t_1;
	} else if (y <= 2.25e-103) {
		tmp = (x / ((y - t) * z)) + 1.0;
	} 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 = 1.0d0 - (x / (y * (y - t)))
    if (y <= (-3.1d-56)) then
        tmp = t_1
    else if (y <= 2.25d-103) then
        tmp = (x / ((y - t) * z)) + 1.0d0
    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 = 1.0 - (x / (y * (y - t)));
	double tmp;
	if (y <= -3.1e-56) {
		tmp = t_1;
	} else if (y <= 2.25e-103) {
		tmp = (x / ((y - t) * z)) + 1.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = 1.0 - (x / (y * (y - t)))
	tmp = 0
	if y <= -3.1e-56:
		tmp = t_1
	elif y <= 2.25e-103:
		tmp = (x / ((y - t) * z)) + 1.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(1.0 - Float64(x / Float64(y * Float64(y - t))))
	tmp = 0.0
	if (y <= -3.1e-56)
		tmp = t_1;
	elseif (y <= 2.25e-103)
		tmp = Float64(Float64(x / Float64(Float64(y - t) * z)) + 1.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = 1.0 - (x / (y * (y - t)));
	tmp = 0.0;
	if (y <= -3.1e-56)
		tmp = t_1;
	elseif (y <= 2.25e-103)
		tmp = (x / ((y - t) * z)) + 1.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(1.0 - N[(x / N[(y * N[(y - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -3.1e-56], t$95$1, If[LessEqual[y, 2.25e-103], N[(N[(x / N[(N[(y - t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.09999999999999987e-56 or 2.25e-103 < y

   1. Initial program 99.9%

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

   2. Taylor expanded in y around inf

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

   4. Applied rewrites 91.2%

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

   if -3.09999999999999987e-56 < y < 2.25e-103

   1. Initial program 97.2%

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

   2. Taylor expanded in z around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 85.5

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

   4. Applied rewrites 85.5%

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

Alternative 13: 74.1% accurate, 26.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 1.0)

C

double code(double x, double y, double z, double t) {
	return 1.0;
}

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 = 1.0d0
end function

Java

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

Python

def code(x, y, z, t):
	return 1.0

Julia

function code(x, y, z, t)
	return 1.0
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := 1.0

Derivation

1. Initial program 98.9%

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

2. Taylor expanded in x around 0

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

4. Applied rewrites 74.1%

   \[\leadsto \color{blue}{1} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025105 
(FPCore (x y z t)
  :name "Data.Random.Distribution.Triangular:triangularCDF from random-fu-0.2.6.2, A"
  :precision binary64
  (- 1.0 (/ x (* (- y z) (- y t)))))