Diagrams.Trail:splitAtParam from diagrams-lib-1.3.0.3, A

Percentage Accurate: 89.4% → 98.0%

Time: 8.3s

Alternatives: 17

Speedup: 0.5×

Specification

Math

\[\begin{array}{l} \\ \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 89.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1} \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.0% accurate, 0.3× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in y around inf

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

      1. lower-*.f64 N/A

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

      2. lower-fma.f64 N/A

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

   6. Applied rewrites 95.7%

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

   if +inf.0 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

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

Alternative 2: 96.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot z - x\\ t_2 := \frac{x + \frac{y \cdot z - x}{t\_1}}{x + 1}\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{+97}:\\ \;\;\;\;y \cdot \mathsf{fma}\left(-1, \frac{-1}{y}, \frac{\frac{z}{t\_1}}{x - -1}\right)\\ \mathbf{elif}\;t\_2 \leq 5 \cdot 10^{+121}:\\ \;\;\;\;\frac{x + \frac{\mathsf{fma}\left(z, y, -x\right)}{t\_1}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (* t z) x)) (t_2 (/ (+ x (/ (- (* y z) x) t_1)) (+ x 1.0))))
   (if (<= t_2 -2e+97)
     (* y (fma -1.0 (/ -1.0 y) (/ (/ z t_1) (- x -1.0))))
     (if (<= t_2 5e+121)
       (/ (+ x (/ (fma z y (- x)) t_1)) (+ x 1.0))
       (/ (+ x (/ y t)) (+ x 1.0))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (t * z) - x;
	double t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0);
	double tmp;
	if (t_2 <= -2e+97) {
		tmp = y * fma(-1.0, (-1.0 / y), ((z / t_1) / (x - -1.0)));
	} else if (t_2 <= 5e+121) {
		tmp = (x + (fma(z, y, -x) / t_1)) / (x + 1.0);
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(t * z) - x)
	t_2 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_1)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_2 <= -2e+97)
		tmp = Float64(y * fma(-1.0, Float64(-1.0 / y), Float64(Float64(z / t_1) / Float64(x - -1.0))));
	elseif (t_2 <= 5e+121)
		tmp = Float64(Float64(x + Float64(fma(z, y, Float64(-x)) / t_1)) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -2e+97], N[(y * N[(-1.0 * N[(-1.0 / y), $MachinePrecision] + N[(N[(z / t$95$1), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 5e+121], N[(N[(x + N[(N[(z * y + (-x)), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -2.0000000000000001e97

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in y around inf

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

      1. lower-*.f64 N/A

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

      2. lower-fma.f64 N/A

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

   6. Applied rewrites 95.7%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. associate-/r* N/A

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

      7. lower-/.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. add-flip N/A

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

      10. metadata-eval N/A

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

      11. lift--.f64 96.6

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

   8. Applied rewrites 96.6%

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

   9. Taylor expanded in x around inf

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

   11. Applied rewrites 77.4%

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

   if -2.0000000000000001e97 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 5.00000000000000007e121

   1. Initial program 89.4%

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

      1. lift--.f64 N/A

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

      2. sub-flip N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. lower-neg.f64 89.4

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

   3. Applied rewrites 89.4%

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

   if 5.00000000000000007e121 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

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

Alternative 3: 95.8% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \frac{y}{t}\\ t_2 := t \cdot z - x\\ t_3 := y \cdot \frac{z}{\left(1 + x\right) \cdot t\_2}\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -1 \cdot 10^{+18}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 5 \cdot 10^{-20}:\\ \;\;\;\;\frac{t\_1 - \frac{x}{t \cdot z}}{1}\\ \mathbf{elif}\;t\_4 \leq 2:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x - -1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;\frac{t\_1}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ x (/ y t)))
        (t_2 (- (* t z) x))
        (t_3 (* y (/ z (* (+ 1.0 x) t_2))))
        (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
   (if (<= t_4 -1e+18)
     t_3
     (if (<= t_4 5e-20)
       (/ (- t_1 (/ x (* t z))) 1.0)
       (if (<= t_4 2.0)
         (/ (- x (/ x t_2)) (- x -1.0))
         (if (<= t_4 INFINITY) t_3 (/ t_1 (+ x 1.0))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x + (y / t);
	double t_2 = (t * z) - x;
	double t_3 = y * (z / ((1.0 + x) * t_2));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+18) {
		tmp = t_3;
	} else if (t_4 <= 5e-20) {
		tmp = (t_1 - (x / (t * z))) / 1.0;
	} else if (t_4 <= 2.0) {
		tmp = (x - (x / t_2)) / (x - -1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = t_1 / (x + 1.0);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x + (y / t);
	double t_2 = (t * z) - x;
	double t_3 = y * (z / ((1.0 + x) * t_2));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+18) {
		tmp = t_3;
	} else if (t_4 <= 5e-20) {
		tmp = (t_1 - (x / (t * z))) / 1.0;
	} else if (t_4 <= 2.0) {
		tmp = (x - (x / t_2)) / (x - -1.0);
	} else if (t_4 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = t_1 / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x + (y / t)
	t_2 = (t * z) - x
	t_3 = y * (z / ((1.0 + x) * t_2))
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -1e+18:
		tmp = t_3
	elif t_4 <= 5e-20:
		tmp = (t_1 - (x / (t * z))) / 1.0
	elif t_4 <= 2.0:
		tmp = (x - (x / t_2)) / (x - -1.0)
	elif t_4 <= math.inf:
		tmp = t_3
	else:
		tmp = t_1 / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x + Float64(y / t))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(y * Float64(z / Float64(Float64(1.0 + x) * t_2)))
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -1e+18)
		tmp = t_3;
	elseif (t_4 <= 5e-20)
		tmp = Float64(Float64(t_1 - Float64(x / Float64(t * z))) / 1.0);
	elseif (t_4 <= 2.0)
		tmp = Float64(Float64(x - Float64(x / t_2)) / Float64(x - -1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = Float64(t_1 / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x + (y / t);
	t_2 = (t * z) - x;
	t_3 = y * (z / ((1.0 + x) * t_2));
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -1e+18)
		tmp = t_3;
	elseif (t_4 <= 5e-20)
		tmp = (t_1 - (x / (t * z))) / 1.0;
	elseif (t_4 <= 2.0)
		tmp = (x - (x / t_2)) / (x - -1.0);
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1 / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(y * N[(z / N[(N[(1.0 + x), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, -1e+18], t$95$3, If[LessEqual[t$95$4, 5e-20], N[(N[(t$95$1 - N[(x / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 1.0), $MachinePrecision], If[LessEqual[t$95$4, 2.0], N[(N[(x - N[(x / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, Infinity], t$95$3, N[(t$95$1 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1e18 or 2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in y around inf

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

      1. lower-*.f64 N/A

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

      2. lower-fma.f64 N/A

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

   6. Applied rewrites 95.7%

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

   7. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower--.f64 N/A

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

      5. lower-*.f64 32.0

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

   9. Applied rewrites 32.0%

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

   if -1e18 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4.9999999999999999e-20

   1. Initial program 89.4%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

         \[\leadsto \frac{x - \color{blue}{\frac{x}{t \cdot z - x}}}{x + 1} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{x - \frac{x}{\color{blue}{t \cdot z - x}}}{x + 1} \]

      3. lower--.f64 N/A

         \[\leadsto \frac{x - \frac{x}{t \cdot z - \color{blue}{x}}}{x + 1} \]

      4. lower-*.f64 66.3

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

   4. Applied rewrites 66.3%

      \[\leadsto \frac{\color{blue}{x - \frac{x}{t \cdot z - x}}}{x + 1} \]

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 22.6%

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

   8. Taylor expanded in t around inf

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 36.6

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

   10. Applied rewrites 36.6%

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

   if 4.9999999999999999e-20 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2

   1. Initial program 89.4%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

         \[\leadsto \frac{x - \color{blue}{\frac{x}{t \cdot z - x}}}{x + 1} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{x - \frac{x}{\color{blue}{t \cdot z - x}}}{x + 1} \]

      3. lower--.f64 N/A

         \[\leadsto \frac{x - \frac{x}{t \cdot z - \color{blue}{x}}}{x + 1} \]

      4. lower-*.f64 66.3

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

   4. Applied rewrites 66.3%

      \[\leadsto \frac{\color{blue}{x - \frac{x}{t \cdot z - x}}}{x + 1} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x - \frac{x}{t \cdot z - x}}{\color{blue}{x + 1}} \]

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lift--.f64 66.3

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

   6. Applied rewrites 66.3%

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

   if +inf.0 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

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

Alternative 4: 93.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot z - x\\ \mathbf{if}\;\frac{x + \frac{t\_1}{t \cdot z - x}}{x + 1} \leq 5 \cdot 10^{+121}:\\ \;\;\;\;\frac{x + \frac{t\_1}{\mathsf{fma}\left(z, t, -x\right)}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (* y z) x)))
   (if (<= (/ (+ x (/ t_1 (- (* t z) x))) (+ x 1.0)) 5e+121)
     (/ (+ x (/ t_1 (fma z t (- x)))) (+ x 1.0))
     (/ (+ x (/ y t)) (+ x 1.0)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y * z) - x;
	double tmp;
	if (((x + (t_1 / ((t * z) - x))) / (x + 1.0)) <= 5e+121) {
		tmp = (x + (t_1 / fma(z, t, -x))) / (x + 1.0);
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y * z) - x)
	tmp = 0.0
	if (Float64(Float64(x + Float64(t_1 / Float64(Float64(t * z) - x))) / Float64(x + 1.0)) <= 5e+121)
		tmp = Float64(Float64(x + Float64(t_1 / fma(z, t, Float64(-x)))) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision]}, If[LessEqual[N[(N[(x + N[(t$95$1 / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], 5e+121], N[(N[(x + N[(t$95$1 / N[(z * t + (-x)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 5.00000000000000007e121

   1. Initial program 89.4%

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

      1. lift--.f64 N/A

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

      2. sub-flip N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. lower-neg.f64 89.4

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

   3. Applied rewrites 89.4%

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

   if 5.00000000000000007e121 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

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

Alternative 5: 93.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_1 \leq 5 \cdot 10^{+121}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
   (if (<= t_1 5e+121) t_1 (/ (+ x (/ y t)) (+ x 1.0)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= 5e+121) {
		tmp = t_1;
	} else {
		tmp = (x + (y / t)) / (x + 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 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_1 <= 5d+121) then
        tmp = t_1
    else
        tmp = (x + (y / t)) / (x + 1.0d0)
    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) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= 5e+121) {
		tmp = t_1;
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_1 <= 5e+121:
		tmp = t_1
	else:
		tmp = (x + (y / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_1 <= 5e+121)
		tmp = t_1;
	else
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_1 <= 5e+121)
		tmp = t_1;
	else
		tmp = (x + (y / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 5e+121], t$95$1, N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 5.00000000000000007e121

   1. Initial program 89.4%

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

   if 5.00000000000000007e121 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

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

Alternative 6: 93.0% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := t \cdot z - x\\ t_3 := y \cdot \frac{z}{\left(1 + x\right) \cdot t\_2}\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -1 \cdot 10^{+18}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 3.8 \cdot 10^{-37}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 2:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x - -1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
        (t_2 (- (* t z) x))
        (t_3 (* y (/ z (* (+ 1.0 x) t_2))))
        (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
   (if (<= t_4 -1e+18)
     t_3
     (if (<= t_4 3.8e-37)
       t_1
       (if (<= t_4 2.0)
         (/ (- x (/ x t_2)) (- x -1.0))
         (if (<= t_4 INFINITY) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = y * (z / ((1.0 + x) * t_2));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+18) {
		tmp = t_3;
	} else if (t_4 <= 3.8e-37) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (x - (x / t_2)) / (x - -1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = y * (z / ((1.0 + x) * t_2));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+18) {
		tmp = t_3;
	} else if (t_4 <= 3.8e-37) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (x - (x / t_2)) / (x - -1.0);
	} else if (t_4 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (t * z) - x
	t_3 = y * (z / ((1.0 + x) * t_2))
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -1e+18:
		tmp = t_3
	elif t_4 <= 3.8e-37:
		tmp = t_1
	elif t_4 <= 2.0:
		tmp = (x - (x / t_2)) / (x - -1.0)
	elif t_4 <= math.inf:
		tmp = t_3
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(y * Float64(z / Float64(Float64(1.0 + x) * t_2)))
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -1e+18)
		tmp = t_3;
	elseif (t_4 <= 3.8e-37)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = Float64(Float64(x - Float64(x / t_2)) / Float64(x - -1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (t * z) - x;
	t_3 = y * (z / ((1.0 + x) * t_2));
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -1e+18)
		tmp = t_3;
	elseif (t_4 <= 3.8e-37)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = (x - (x / t_2)) / (x - -1.0);
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(y * N[(z / N[(N[(1.0 + x), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, -1e+18], t$95$3, If[LessEqual[t$95$4, 3.8e-37], t$95$1, If[LessEqual[t$95$4, 2.0], N[(N[(x - N[(x / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, Infinity], t$95$3, t$95$1]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1e18 or 2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in y around inf

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

      1. lower-*.f64 N/A

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

      2. lower-fma.f64 N/A

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

   6. Applied rewrites 95.7%

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

   7. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower--.f64 N/A

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

      5. lower-*.f64 32.0

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

   9. Applied rewrites 32.0%

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

   if -1e18 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 3.8000000000000004e-37 or +inf.0 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

      \[\leadsto \frac{\color{blue}{x + \frac{y}{t}}}{x + 1} \]

   if 3.8000000000000004e-37 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2

   1. Initial program 89.4%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

         \[\leadsto \frac{x - \color{blue}{\frac{x}{t \cdot z - x}}}{x + 1} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{x - \frac{x}{\color{blue}{t \cdot z - x}}}{x + 1} \]

      3. lower--.f64 N/A

         \[\leadsto \frac{x - \frac{x}{t \cdot z - \color{blue}{x}}}{x + 1} \]

      4. lower-*.f64 66.3

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

   4. Applied rewrites 66.3%

      \[\leadsto \frac{\color{blue}{x - \frac{x}{t \cdot z - x}}}{x + 1} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x - \frac{x}{t \cdot z - x}}{\color{blue}{x + 1}} \]

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lift--.f64 66.3

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

   6. Applied rewrites 66.3%

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

Alternative 7: 89.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := t \cdot z - x\\ t_3 := \frac{y \cdot z}{t\_2 \cdot \left(x - -1\right)}\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -1 \cdot 10^{+18}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 3.8 \cdot 10^{-37}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 2:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x - -1}\\ \mathbf{elif}\;t\_4 \leq 5 \cdot 10^{+121}:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
        (t_2 (- (* t z) x))
        (t_3 (/ (* y z) (* t_2 (- x -1.0))))
        (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
   (if (<= t_4 -1e+18)
     t_3
     (if (<= t_4 3.8e-37)
       t_1
       (if (<= t_4 2.0)
         (/ (- x (/ x t_2)) (- x -1.0))
         (if (<= t_4 5e+121) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (y * z) / (t_2 * (x - -1.0));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+18) {
		tmp = t_3;
	} else if (t_4 <= 3.8e-37) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (x - (x / t_2)) / (x - -1.0);
	} else if (t_4 <= 5e+121) {
		tmp = t_3;
	} 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) :: t_3
    real(8) :: t_4
    real(8) :: tmp
    t_1 = (x + (y / t)) / (x + 1.0d0)
    t_2 = (t * z) - x
    t_3 = (y * z) / (t_2 * (x - (-1.0d0)))
    t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0d0)
    if (t_4 <= (-1d+18)) then
        tmp = t_3
    else if (t_4 <= 3.8d-37) then
        tmp = t_1
    else if (t_4 <= 2.0d0) then
        tmp = (x - (x / t_2)) / (x - (-1.0d0))
    else if (t_4 <= 5d+121) then
        tmp = t_3
    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)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (y * z) / (t_2 * (x - -1.0));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+18) {
		tmp = t_3;
	} else if (t_4 <= 3.8e-37) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (x - (x / t_2)) / (x - -1.0);
	} else if (t_4 <= 5e+121) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (t * z) - x
	t_3 = (y * z) / (t_2 * (x - -1.0))
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -1e+18:
		tmp = t_3
	elif t_4 <= 3.8e-37:
		tmp = t_1
	elif t_4 <= 2.0:
		tmp = (x - (x / t_2)) / (x - -1.0)
	elif t_4 <= 5e+121:
		tmp = t_3
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(y * z) / Float64(t_2 * Float64(x - -1.0)))
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -1e+18)
		tmp = t_3;
	elseif (t_4 <= 3.8e-37)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = Float64(Float64(x - Float64(x / t_2)) / Float64(x - -1.0));
	elseif (t_4 <= 5e+121)
		tmp = t_3;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (t * z) - x;
	t_3 = (y * z) / (t_2 * (x - -1.0));
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -1e+18)
		tmp = t_3;
	elseif (t_4 <= 3.8e-37)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = (x - (x / t_2)) / (x - -1.0);
	elseif (t_4 <= 5e+121)
		tmp = t_3;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(y * z), $MachinePrecision] / N[(t$95$2 * N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, -1e+18], t$95$3, If[LessEqual[t$95$4, 3.8e-37], t$95$1, If[LessEqual[t$95$4, 2.0], N[(N[(x - N[(x / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, 5e+121], t$95$3, t$95$1]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1e18 or 2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 5.00000000000000007e121

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. add-to-fraction N/A

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

      5. associate-/l/ N/A

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

      6. lower-/.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-fma.f64 N/A

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

      9. lift-*.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lower-*.f64 60.3

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

      13. lift-+.f64 N/A

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

      14. add-flip N/A

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

      15. lower--.f64 N/A

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

      16. metadata-eval 60.3

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

   3. Applied rewrites 60.3%

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

   4. Taylor expanded in t around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 10.7

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

   6. Applied rewrites 10.7%

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

   7. Taylor expanded in x around 0

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

      1. lower-*.f64 28.8

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

   9. Applied rewrites 28.8%

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

   if -1e18 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 3.8000000000000004e-37 or 5.00000000000000007e121 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

      \[\leadsto \frac{\color{blue}{x + \frac{y}{t}}}{x + 1} \]

   if 3.8000000000000004e-37 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2

   1. Initial program 89.4%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

         \[\leadsto \frac{x - \color{blue}{\frac{x}{t \cdot z - x}}}{x + 1} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{x - \frac{x}{\color{blue}{t \cdot z - x}}}{x + 1} \]

      3. lower--.f64 N/A

         \[\leadsto \frac{x - \frac{x}{t \cdot z - \color{blue}{x}}}{x + 1} \]

      4. lower-*.f64 66.3

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

   4. Applied rewrites 66.3%

      \[\leadsto \frac{\color{blue}{x - \frac{x}{t \cdot z - x}}}{x + 1} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{x - \frac{x}{t \cdot z - x}}{\color{blue}{x + 1}} \]

      2. add-flip N/A

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

      3. metadata-eval N/A

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

      4. lift--.f64 66.3

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

   6. Applied rewrites 66.3%

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

Alternative 8: 89.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := t \cdot z - x\\ t_3 := \frac{y \cdot z}{t\_2 \cdot \left(x - -1\right)}\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -1 \cdot 10^{+18}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 0.9999999999991216:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 2:\\ \;\;\;\;\frac{x \cdot \left(1 + \frac{1}{x}\right)}{x + 1}\\ \mathbf{elif}\;t\_4 \leq 5 \cdot 10^{+121}:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
        (t_2 (- (* t z) x))
        (t_3 (/ (* y z) (* t_2 (- x -1.0))))
        (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
   (if (<= t_4 -1e+18)
     t_3
     (if (<= t_4 0.9999999999991216)
       t_1
       (if (<= t_4 2.0)
         (/ (* x (+ 1.0 (/ 1.0 x))) (+ x 1.0))
         (if (<= t_4 5e+121) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (y * z) / (t_2 * (x - -1.0));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+18) {
		tmp = t_3;
	} else if (t_4 <= 0.9999999999991216) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (x * (1.0 + (1.0 / x))) / (x + 1.0);
	} else if (t_4 <= 5e+121) {
		tmp = t_3;
	} 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) :: t_3
    real(8) :: t_4
    real(8) :: tmp
    t_1 = (x + (y / t)) / (x + 1.0d0)
    t_2 = (t * z) - x
    t_3 = (y * z) / (t_2 * (x - (-1.0d0)))
    t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0d0)
    if (t_4 <= (-1d+18)) then
        tmp = t_3
    else if (t_4 <= 0.9999999999991216d0) then
        tmp = t_1
    else if (t_4 <= 2.0d0) then
        tmp = (x * (1.0d0 + (1.0d0 / x))) / (x + 1.0d0)
    else if (t_4 <= 5d+121) then
        tmp = t_3
    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)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (y * z) / (t_2 * (x - -1.0));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+18) {
		tmp = t_3;
	} else if (t_4 <= 0.9999999999991216) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (x * (1.0 + (1.0 / x))) / (x + 1.0);
	} else if (t_4 <= 5e+121) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (t * z) - x
	t_3 = (y * z) / (t_2 * (x - -1.0))
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -1e+18:
		tmp = t_3
	elif t_4 <= 0.9999999999991216:
		tmp = t_1
	elif t_4 <= 2.0:
		tmp = (x * (1.0 + (1.0 / x))) / (x + 1.0)
	elif t_4 <= 5e+121:
		tmp = t_3
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(y * z) / Float64(t_2 * Float64(x - -1.0)))
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -1e+18)
		tmp = t_3;
	elseif (t_4 <= 0.9999999999991216)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = Float64(Float64(x * Float64(1.0 + Float64(1.0 / x))) / Float64(x + 1.0));
	elseif (t_4 <= 5e+121)
		tmp = t_3;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (t * z) - x;
	t_3 = (y * z) / (t_2 * (x - -1.0));
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -1e+18)
		tmp = t_3;
	elseif (t_4 <= 0.9999999999991216)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = (x * (1.0 + (1.0 / x))) / (x + 1.0);
	elseif (t_4 <= 5e+121)
		tmp = t_3;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(y * z), $MachinePrecision] / N[(t$95$2 * N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, -1e+18], t$95$3, If[LessEqual[t$95$4, 0.9999999999991216], t$95$1, If[LessEqual[t$95$4, 2.0], N[(N[(x * N[(1.0 + N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, 5e+121], t$95$3, t$95$1]]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1e18 or 2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 5.00000000000000007e121

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. lift-/.f64 N/A

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

      4. add-to-fraction N/A

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

      5. associate-/l/ N/A

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

      6. lower-/.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-fma.f64 N/A

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

      9. lift-*.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lower-*.f64 60.3

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

      13. lift-+.f64 N/A

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

      14. add-flip N/A

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

      15. lower--.f64 N/A

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

      16. metadata-eval 60.3

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

   3. Applied rewrites 60.3%

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

   4. Taylor expanded in t around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 10.7

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

   6. Applied rewrites 10.7%

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

   7. Taylor expanded in x around 0

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

      1. lower-*.f64 28.8

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

   9. Applied rewrites 28.8%

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

   if -1e18 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.99999999999912159 or 5.00000000000000007e121 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

      \[\leadsto \frac{\color{blue}{x + \frac{y}{t}}}{x + 1} \]

   if 0.99999999999912159 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2

   1. Initial program 89.4%

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

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 53.4

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

   4. Applied rewrites 53.4%

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

Alternative 9: 85.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_2 \leq 0.9999999999991216:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 100000:\\ \;\;\;\;\frac{x \cdot \left(1 + \frac{1}{x}\right)}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
        (t_2 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
   (if (<= t_2 0.9999999999991216)
     t_1
     (if (<= t_2 100000.0) (/ (* x (+ 1.0 (/ 1.0 x))) (+ x 1.0)) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= 0.9999999999991216) {
		tmp = t_1;
	} else if (t_2 <= 100000.0) {
		tmp = (x * (1.0 + (1.0 / x))) / (x + 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)) / (x + 1.0d0)
    t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_2 <= 0.9999999999991216d0) then
        tmp = t_1
    else if (t_2 <= 100000.0d0) then
        tmp = (x * (1.0d0 + (1.0d0 / x))) / (x + 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)) / (x + 1.0);
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= 0.9999999999991216) {
		tmp = t_1;
	} else if (t_2 <= 100000.0) {
		tmp = (x * (1.0 + (1.0 / x))) / (x + 1.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_2 <= 0.9999999999991216:
		tmp = t_1
	elif t_2 <= 100000.0:
		tmp = (x * (1.0 + (1.0 / x))) / (x + 1.0)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_2 <= 0.9999999999991216)
		tmp = t_1;
	elseif (t_2 <= 100000.0)
		tmp = Float64(Float64(x * Float64(1.0 + Float64(1.0 / x))) / Float64(x + 1.0));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_2 <= 0.9999999999991216)
		tmp = t_1;
	elseif (t_2 <= 100000.0)
		tmp = (x * (1.0 + (1.0 / x))) / (x + 1.0);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, 0.9999999999991216], t$95$1, If[LessEqual[t$95$2, 100000.0], N[(N[(x * N[(1.0 + N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.99999999999912159 or 1e5 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

      \[\leadsto \frac{\color{blue}{x + \frac{y}{t}}}{x + 1} \]

   if 0.99999999999912159 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1e5

   1. Initial program 89.4%

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

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 53.4

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

   4. Applied rewrites 53.4%

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

Alternative 10: 85.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_2 \leq 0.9999999999991216:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 100000:\\ \;\;\;\;\left(-1 \cdot \left(1 + x\right)\right) \cdot \frac{1}{-1 - x}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
        (t_2 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
   (if (<= t_2 0.9999999999991216)
     t_1
     (if (<= t_2 100000.0) (* (* -1.0 (+ 1.0 x)) (/ 1.0 (- -1.0 x))) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= 0.9999999999991216) {
		tmp = t_1;
	} else if (t_2 <= 100000.0) {
		tmp = (-1.0 * (1.0 + x)) * (1.0 / (-1.0 - x));
	} 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)) / (x + 1.0d0)
    t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_2 <= 0.9999999999991216d0) then
        tmp = t_1
    else if (t_2 <= 100000.0d0) then
        tmp = ((-1.0d0) * (1.0d0 + x)) * (1.0d0 / ((-1.0d0) - x))
    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)) / (x + 1.0);
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= 0.9999999999991216) {
		tmp = t_1;
	} else if (t_2 <= 100000.0) {
		tmp = (-1.0 * (1.0 + x)) * (1.0 / (-1.0 - x));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_2 <= 0.9999999999991216:
		tmp = t_1
	elif t_2 <= 100000.0:
		tmp = (-1.0 * (1.0 + x)) * (1.0 / (-1.0 - x))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_2 <= 0.9999999999991216)
		tmp = t_1;
	elseif (t_2 <= 100000.0)
		tmp = Float64(Float64(-1.0 * Float64(1.0 + x)) * Float64(1.0 / Float64(-1.0 - x)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_2 <= 0.9999999999991216)
		tmp = t_1;
	elseif (t_2 <= 100000.0)
		tmp = (-1.0 * (1.0 + x)) * (1.0 / (-1.0 - x));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, 0.9999999999991216], t$95$1, If[LessEqual[t$95$2, 100000.0], N[(N[(-1.0 * N[(1.0 + x), $MachinePrecision]), $MachinePrecision] * N[(1.0 / N[(-1.0 - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.99999999999912159 or 1e5 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

      \[\leadsto \frac{\color{blue}{x + \frac{y}{t}}}{x + 1} \]

   if 0.99999999999912159 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1e5

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in z around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 53.4

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

   6. Applied rewrites 53.4%

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

Alternative 11: 85.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_2 \leq 0.9999999999991216:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 100000:\\ \;\;\;\;y \cdot \frac{1}{y}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
        (t_2 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
   (if (<= t_2 0.9999999999991216)
     t_1
     (if (<= t_2 100000.0) (* y (/ 1.0 y)) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= 0.9999999999991216) {
		tmp = t_1;
	} else if (t_2 <= 100000.0) {
		tmp = y * (1.0 / y);
	} 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)) / (x + 1.0d0)
    t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_2 <= 0.9999999999991216d0) then
        tmp = t_1
    else if (t_2 <= 100000.0d0) then
        tmp = y * (1.0d0 / y)
    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)) / (x + 1.0);
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= 0.9999999999991216) {
		tmp = t_1;
	} else if (t_2 <= 100000.0) {
		tmp = y * (1.0 / y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_2 <= 0.9999999999991216:
		tmp = t_1
	elif t_2 <= 100000.0:
		tmp = y * (1.0 / y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_2 <= 0.9999999999991216)
		tmp = t_1;
	elseif (t_2 <= 100000.0)
		tmp = Float64(y * Float64(1.0 / y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_2 <= 0.9999999999991216)
		tmp = t_1;
	elseif (t_2 <= 100000.0)
		tmp = y * (1.0 / y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, 0.9999999999991216], t$95$1, If[LessEqual[t$95$2, 100000.0], N[(y * N[(1.0 / y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.99999999999912159 or 1e5 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{x + 1} \]

      2. lower-/.f64 70.5

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{x + 1} \]

   4. Applied rewrites 70.5%

      \[\leadsto \frac{\color{blue}{x + \frac{y}{t}}}{x + 1} \]

   if 0.99999999999912159 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1e5

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in y around inf

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

      1. lower-*.f64 N/A

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

      2. lower-fma.f64 N/A

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

   6. Applied rewrites 95.7%

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

   7. Taylor expanded in x around inf

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

      1. lower-/.f64 53.3

         \[\leadsto y \cdot \frac{1}{y} \]

   9. Applied rewrites 53.3%

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

Alternative 12: 80.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_1 \leq 5 \cdot 10^{-13}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{1}\\ \mathbf{elif}\;t\_1 \leq 100000:\\ \;\;\;\;y \cdot \frac{1}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
   (if (<= t_1 5e-13)
     (/ (+ x (/ y t)) 1.0)
     (if (<= t_1 100000.0) (* y (/ 1.0 y)) (/ y t)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= 5e-13) {
		tmp = (x + (y / t)) / 1.0;
	} else if (t_1 <= 100000.0) {
		tmp = y * (1.0 / y);
	} else {
		tmp = 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) :: t_1
    real(8) :: tmp
    t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_1 <= 5d-13) then
        tmp = (x + (y / t)) / 1.0d0
    else if (t_1 <= 100000.0d0) then
        tmp = y * (1.0d0 / y)
    else
        tmp = y / t
    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) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= 5e-13) {
		tmp = (x + (y / t)) / 1.0;
	} else if (t_1 <= 100000.0) {
		tmp = y * (1.0 / y);
	} else {
		tmp = y / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_1 <= 5e-13:
		tmp = (x + (y / t)) / 1.0
	elif t_1 <= 100000.0:
		tmp = y * (1.0 / y)
	else:
		tmp = y / t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_1 <= 5e-13)
		tmp = Float64(Float64(x + Float64(y / t)) / 1.0);
	elseif (t_1 <= 100000.0)
		tmp = Float64(y * Float64(1.0 / y));
	else
		tmp = Float64(y / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_1 <= 5e-13)
		tmp = (x + (y / t)) / 1.0;
	elseif (t_1 <= 100000.0)
		tmp = y * (1.0 / y);
	else
		tmp = y / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 5e-13], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / 1.0), $MachinePrecision], If[LessEqual[t$95$1, 100000.0], N[(y * N[(1.0 / y), $MachinePrecision]), $MachinePrecision], N[(y / t), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4.9999999999999999e-13

   1. Initial program 89.4%

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

   2. Taylor expanded in y around 0

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

      1. lower--.f64 N/A

         \[\leadsto \frac{x - \color{blue}{\frac{x}{t \cdot z - x}}}{x + 1} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{x - \frac{x}{\color{blue}{t \cdot z - x}}}{x + 1} \]

      3. lower--.f64 N/A

         \[\leadsto \frac{x - \frac{x}{t \cdot z - \color{blue}{x}}}{x + 1} \]

      4. lower-*.f64 66.3

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

   4. Applied rewrites 66.3%

      \[\leadsto \frac{\color{blue}{x - \frac{x}{t \cdot z - x}}}{x + 1} \]

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 22.6%

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

   8. Taylor expanded in z around inf

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

      1. lower-+.f64 N/A

         \[\leadsto \frac{x + \color{blue}{\frac{y}{t}}}{1} \]

      2. lower-/.f64 34.3

         \[\leadsto \frac{x + \frac{y}{\color{blue}{t}}}{1} \]

   10. Applied rewrites 34.3%

       \[\leadsto \frac{\color{blue}{x + \frac{y}{t}}}{1} \]

   if 4.9999999999999999e-13 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1e5

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in y around inf

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

      1. lower-*.f64 N/A

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

      2. lower-fma.f64 N/A

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

   6. Applied rewrites 95.7%

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

   7. Taylor expanded in x around inf

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

      1. lower-/.f64 53.3

         \[\leadsto y \cdot \frac{1}{y} \]

   9. Applied rewrites 53.3%

      \[\leadsto y \cdot \frac{1}{\color{blue}{y}} \]

   if 1e5 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 24.6

         \[\leadsto \frac{y}{\color{blue}{t}} \]

   4. Applied rewrites 24.6%

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

Alternative 13: 74.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{-113}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{elif}\;t\_1 \leq 0.9999999999991216:\\ \;\;\;\;\frac{x}{x - -1}\\ \mathbf{elif}\;t\_1 \leq 100000:\\ \;\;\;\;y \cdot \frac{1}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
   (if (<= t_1 -2e-113)
     (/ y t)
     (if (<= t_1 0.9999999999991216)
       (/ x (- x -1.0))
       (if (<= t_1 100000.0) (* y (/ 1.0 y)) (/ y t))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= -2e-113) {
		tmp = y / t;
	} else if (t_1 <= 0.9999999999991216) {
		tmp = x / (x - -1.0);
	} else if (t_1 <= 100000.0) {
		tmp = y * (1.0 / y);
	} else {
		tmp = 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) :: t_1
    real(8) :: tmp
    t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_1 <= (-2d-113)) then
        tmp = y / t
    else if (t_1 <= 0.9999999999991216d0) then
        tmp = x / (x - (-1.0d0))
    else if (t_1 <= 100000.0d0) then
        tmp = y * (1.0d0 / y)
    else
        tmp = y / t
    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) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= -2e-113) {
		tmp = y / t;
	} else if (t_1 <= 0.9999999999991216) {
		tmp = x / (x - -1.0);
	} else if (t_1 <= 100000.0) {
		tmp = y * (1.0 / y);
	} else {
		tmp = y / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_1 <= -2e-113:
		tmp = y / t
	elif t_1 <= 0.9999999999991216:
		tmp = x / (x - -1.0)
	elif t_1 <= 100000.0:
		tmp = y * (1.0 / y)
	else:
		tmp = y / t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_1 <= -2e-113)
		tmp = Float64(y / t);
	elseif (t_1 <= 0.9999999999991216)
		tmp = Float64(x / Float64(x - -1.0));
	elseif (t_1 <= 100000.0)
		tmp = Float64(y * Float64(1.0 / y));
	else
		tmp = Float64(y / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_1 <= -2e-113)
		tmp = y / t;
	elseif (t_1 <= 0.9999999999991216)
		tmp = x / (x - -1.0);
	elseif (t_1 <= 100000.0)
		tmp = y * (1.0 / y);
	else
		tmp = y / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e-113], N[(y / t), $MachinePrecision], If[LessEqual[t$95$1, 0.9999999999991216], N[(x / N[(x - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 100000.0], N[(y * N[(1.0 / y), $MachinePrecision]), $MachinePrecision], N[(y / t), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1.99999999999999996e-113 or 1e5 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 24.6

         \[\leadsto \frac{y}{\color{blue}{t}} \]

   4. Applied rewrites 24.6%

      \[\leadsto \color{blue}{\frac{y}{t}} \]

   if -1.99999999999999996e-113 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.99999999999912159

   1. Initial program 89.4%

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

   2. Taylor expanded in t around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + x}} \]

      2. lower-+.f64 56.0

         \[\leadsto \frac{x}{1 + \color{blue}{x}} \]

   4. Applied rewrites 56.0%

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

      1. lift-+.f64 N/A

         \[\leadsto \frac{x}{1 + \color{blue}{x}} \]

      2. +-commutative N/A

         \[\leadsto \frac{x}{x + \color{blue}{1}} \]

      3. add-flip N/A

         \[\leadsto \frac{x}{x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{x}{x - -1} \]

      5. lift--.f64 56.0

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

   6. Applied rewrites 56.0%

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

   if 0.99999999999912159 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1e5

   1. Initial program 89.4%

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

      1. lift-/.f64 N/A

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

      2. frac-2neg N/A

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

      3. mult-flip N/A

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

      4. lower-*.f64 N/A

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in y around inf

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

      1. lower-*.f64 N/A

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

      2. lower-fma.f64 N/A

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

   6. Applied rewrites 95.7%

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

   7. Taylor expanded in x around inf

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

      1. lower-/.f64 53.3

         \[\leadsto y \cdot \frac{1}{y} \]

   9. Applied rewrites 53.3%

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

Alternative 14: 67.7% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x - -1}\\ \mathbf{if}\;x \leq -5.8 \cdot 10^{-120}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 2.05 \cdot 10^{-43}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (- x -1.0))))
   (if (<= x -5.8e-120) t_1 (if (<= x 2.05e-43) (/ y t) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x / (x - -1.0);
	double tmp;
	if (x <= -5.8e-120) {
		tmp = t_1;
	} else if (x <= 2.05e-43) {
		tmp = y / t;
	} 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 = x / (x - (-1.0d0))
    if (x <= (-5.8d-120)) then
        tmp = t_1
    else if (x <= 2.05d-43) then
        tmp = y / t
    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 / (x - -1.0);
	double tmp;
	if (x <= -5.8e-120) {
		tmp = t_1;
	} else if (x <= 2.05e-43) {
		tmp = y / t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x / (x - -1.0)
	tmp = 0
	if x <= -5.8e-120:
		tmp = t_1
	elif x <= 2.05e-43:
		tmp = y / t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x / Float64(x - -1.0))
	tmp = 0.0
	if (x <= -5.8e-120)
		tmp = t_1;
	elseif (x <= 2.05e-43)
		tmp = Float64(y / t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x / (x - -1.0);
	tmp = 0.0;
	if (x <= -5.8e-120)
		tmp = t_1;
	elseif (x <= 2.05e-43)
		tmp = y / t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -5.8e-120], t$95$1, If[LessEqual[x, 2.05e-43], N[(y / t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -5.8e-120 or 2.0499999999999999e-43 < x

   1. Initial program 89.4%

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

   2. Taylor expanded in t around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + x}} \]

      2. lower-+.f64 56.0

         \[\leadsto \frac{x}{1 + \color{blue}{x}} \]

   4. Applied rewrites 56.0%

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

      1. lift-+.f64 N/A

         \[\leadsto \frac{x}{1 + \color{blue}{x}} \]

      2. +-commutative N/A

         \[\leadsto \frac{x}{x + \color{blue}{1}} \]

      3. add-flip N/A

         \[\leadsto \frac{x}{x - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{x}{x - -1} \]

      5. lift--.f64 56.0

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

   6. Applied rewrites 56.0%

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

   if -5.8e-120 < x < 2.0499999999999999e-43

   1. Initial program 89.4%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 24.6

         \[\leadsto \frac{y}{\color{blue}{t}} \]

   4. Applied rewrites 24.6%

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

Alternative 15: 66.4% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 1 - \frac{1}{x}\\ \mathbf{if}\;x \leq -2.45 \cdot 10^{-29}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 4 \cdot 10^{-12}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- 1.0 (/ 1.0 x))))
   (if (<= x -2.45e-29) t_1 (if (<= x 4e-12) (/ y t) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = 1.0 - (1.0 / x);
	double tmp;
	if (x <= -2.45e-29) {
		tmp = t_1;
	} else if (x <= 4e-12) {
		tmp = y / t;
	} 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 - (1.0d0 / x)
    if (x <= (-2.45d-29)) then
        tmp = t_1
    else if (x <= 4d-12) then
        tmp = y / t
    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 - (1.0 / x);
	double tmp;
	if (x <= -2.45e-29) {
		tmp = t_1;
	} else if (x <= 4e-12) {
		tmp = y / t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = 1.0 - (1.0 / x)
	tmp = 0
	if x <= -2.45e-29:
		tmp = t_1
	elif x <= 4e-12:
		tmp = y / t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(1.0 - Float64(1.0 / x))
	tmp = 0.0
	if (x <= -2.45e-29)
		tmp = t_1;
	elseif (x <= 4e-12)
		tmp = Float64(y / t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = 1.0 - (1.0 / x);
	tmp = 0.0;
	if (x <= -2.45e-29)
		tmp = t_1;
	elseif (x <= 4e-12)
		tmp = y / t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(1.0 - N[(1.0 / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.45e-29], t$95$1, If[LessEqual[x, 4e-12], N[(y / t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -2.4499999999999999e-29 or 3.99999999999999992e-12 < x

   1. Initial program 89.4%

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

   2. Taylor expanded in t around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + x}} \]

      2. lower-+.f64 56.0

         \[\leadsto \frac{x}{1 + \color{blue}{x}} \]

   4. Applied rewrites 56.0%

      \[\leadsto \color{blue}{\frac{x}{1 + x}} \]

   5. Taylor expanded in x around inf

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 45.7

         \[\leadsto 1 - \frac{1}{x} \]

   7. Applied rewrites 45.7%

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

   if -2.4499999999999999e-29 < x < 3.99999999999999992e-12

   1. Initial program 89.4%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 24.6

         \[\leadsto \frac{y}{\color{blue}{t}} \]

   4. Applied rewrites 24.6%

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

Alternative 16: 27.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{-113}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{-95}:\\ \;\;\;\;\frac{x}{1}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
   (if (<= t_1 -2e-113) (/ y t) (if (<= t_1 2e-95) (/ x 1.0) (/ y t)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= -2e-113) {
		tmp = y / t;
	} else if (t_1 <= 2e-95) {
		tmp = x / 1.0;
	} else {
		tmp = 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) :: t_1
    real(8) :: tmp
    t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_1 <= (-2d-113)) then
        tmp = y / t
    else if (t_1 <= 2d-95) then
        tmp = x / 1.0d0
    else
        tmp = y / t
    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) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= -2e-113) {
		tmp = y / t;
	} else if (t_1 <= 2e-95) {
		tmp = x / 1.0;
	} else {
		tmp = y / t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_1 <= -2e-113:
		tmp = y / t
	elif t_1 <= 2e-95:
		tmp = x / 1.0
	else:
		tmp = y / t
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_1 <= -2e-113)
		tmp = Float64(y / t);
	elseif (t_1 <= 2e-95)
		tmp = Float64(x / 1.0);
	else
		tmp = Float64(y / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_1 <= -2e-113)
		tmp = y / t;
	elseif (t_1 <= 2e-95)
		tmp = x / 1.0;
	else
		tmp = y / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e-113], N[(y / t), $MachinePrecision], If[LessEqual[t$95$1, 2e-95], N[(x / 1.0), $MachinePrecision], N[(y / t), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1.99999999999999996e-113 or 1.99999999999999998e-95 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.4%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 24.6

         \[\leadsto \frac{y}{\color{blue}{t}} \]

   4. Applied rewrites 24.6%

      \[\leadsto \color{blue}{\frac{y}{t}} \]

   if -1.99999999999999996e-113 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.99999999999999998e-95

   1. Initial program 89.4%

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

   2. Taylor expanded in t around inf

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{x}{\color{blue}{1 + x}} \]

      2. lower-+.f64 56.0

         \[\leadsto \frac{x}{1 + \color{blue}{x}} \]

   4. Applied rewrites 56.0%

      \[\leadsto \color{blue}{\frac{x}{1 + x}} \]

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 12.9%

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

Alternative 17: 12.9% accurate, 5.6× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := N[(x / 1.0), $MachinePrecision]

Derivation

1. Initial program 89.4%

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

2. Taylor expanded in t around inf

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

   1. lower-/.f64 N/A

      \[\leadsto \frac{x}{\color{blue}{1 + x}} \]

   2. lower-+.f64 56.0

      \[\leadsto \frac{x}{1 + \color{blue}{x}} \]

4. Applied rewrites 56.0%

   \[\leadsto \color{blue}{\frac{x}{1 + x}} \]

5. Taylor expanded in x around 0

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

7. Applied rewrites 12.9%

   \[\leadsto \frac{x}{1} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025147 
(FPCore (x y z t)
  :name "Diagrams.Trail:splitAtParam  from diagrams-lib-1.3.0.3, A"
  :precision binary64
  (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0)))