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

Percentage Accurate: 88.6% → 96.1%

Time: 4.5s

Alternatives: 17

Speedup: 0.3×

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: 88.6% 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: 96.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot z - x\\ t_2 := \frac{x + \frac{y \cdot z - x}{t\_1}}{x + 1}\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{+294}:\\ \;\;\;\;\frac{\mathsf{fma}\left(1 - \frac{x}{z \cdot y}, z \cdot \frac{y}{t\_1}, x\right)}{x + 1}\\ \mathbf{elif}\;t\_2 \leq 10^{+159}:\\ \;\;\;\;\frac{x + \frac{\mathsf{fma}\left(z, y, -x\right)}{t\_1}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{x}{1 + x} + \frac{y}{t \cdot \left(1 + x\right)}\right) - \frac{x}{t \cdot \left(z \cdot \left(1 + x\right)\right)}\\ \end{array} \end{array} \]

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+294)
     (/ (fma (- 1.0 (/ x (* z y))) (* z (/ y t_1)) x) (+ x 1.0))
     (if (<= t_2 1e+159)
       (/ (+ x (/ (fma z y (- x)) t_1)) (+ x 1.0))
       (-
        (+ (/ x (+ 1.0 x)) (/ y (* t (+ 1.0 x))))
        (/ x (* t (* z (+ 1.0 x)))))))))

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+294) {
		tmp = fma((1.0 - (x / (z * y))), (z * (y / t_1)), x) / (x + 1.0);
	} else if (t_2 <= 1e+159) {
		tmp = (x + (fma(z, y, -x) / t_1)) / (x + 1.0);
	} else {
		tmp = ((x / (1.0 + x)) + (y / (t * (1.0 + x)))) - (x / (t * (z * (1.0 + x))));
	}
	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+294)
		tmp = Float64(fma(Float64(1.0 - Float64(x / Float64(z * y))), Float64(z * Float64(y / t_1)), x) / Float64(x + 1.0));
	elseif (t_2 <= 1e+159)
		tmp = Float64(Float64(x + Float64(fma(z, y, Float64(-x)) / t_1)) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(Float64(x / Float64(1.0 + x)) + Float64(y / Float64(t * Float64(1.0 + x)))) - Float64(x / Float64(t * Float64(z * Float64(1.0 + x)))));
	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+294], N[(N[(N[(1.0 - N[(x / N[(z * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(z * N[(y / t$95$1), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+159], N[(N[(x + N[(N[(z * y + (-x)), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision] + N[(y / N[(t * N[(1.0 + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(x / N[(t * N[(z * N[(1.0 + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $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))) < -2.00000000000000013e294

   1. Initial program 88.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-/.f64 N/A

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

      4. lift--.f64 N/A

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

      5. sub-to-mult N/A

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

      6. associate-/l* N/A

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

      7. lower-fma.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lower-/.f64 N/A

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

      10. lift-*.f64 N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. lift-*.f64 N/A

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

      14. *-commutative N/A

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

      15. associate-/l* N/A

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

      16. lower-*.f64 N/A

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

      17. lower-/.f64 72.3

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

   3. Applied rewrites 72.3%

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

   if -2.00000000000000013e294 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 9.9999999999999993e158

   1. Initial program 88.6%

      \[\frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1} \]
   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 88.6

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

   3. Applied rewrites 88.6%

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

   if 9.9999999999999993e158 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 88.6%

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

   2. Taylor expanded in t around inf

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. lower-*.f64 N/A

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

      11. lower-+.f64 59.9

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

   4. Applied rewrites 59.9%

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

Alternative 2: 96.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot z - x\\ t_2 := \frac{x + \frac{y \cdot z - x}{t\_1}}{x + 1}\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{+294}:\\ \;\;\;\;\frac{\mathsf{fma}\left(1 - \frac{x}{z \cdot y}, z \cdot \frac{y}{t\_1}, x\right)}{x + 1}\\ \mathbf{elif}\;t\_2 \leq 10^{+159}:\\ \;\;\;\;\frac{x + \frac{\mathsf{fma}\left(z, y, -x\right)}{t\_1}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{y}{t} + x}{x - -1}\\ \end{array} \end{array} \]

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+294)
     (/ (fma (- 1.0 (/ x (* z y))) (* z (/ y t_1)) x) (+ x 1.0))
     (if (<= t_2 1e+159)
       (/ (+ x (/ (fma z y (- x)) t_1)) (+ x 1.0))
       (/ (+ (/ y t) x) (- 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+294) {
		tmp = fma((1.0 - (x / (z * y))), (z * (y / t_1)), x) / (x + 1.0);
	} else if (t_2 <= 1e+159) {
		tmp = (x + (fma(z, y, -x) / t_1)) / (x + 1.0);
	} else {
		tmp = ((y / t) + x) / (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+294)
		tmp = Float64(fma(Float64(1.0 - Float64(x / Float64(z * y))), Float64(z * Float64(y / t_1)), x) / Float64(x + 1.0));
	elseif (t_2 <= 1e+159)
		tmp = Float64(Float64(x + Float64(fma(z, y, Float64(-x)) / t_1)) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(Float64(y / t) + x) / 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+294], N[(N[(N[(1.0 - N[(x / N[(z * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(z * N[(y / t$95$1), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+159], N[(N[(x + N[(N[(z * y + (-x)), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(y / t), $MachinePrecision] + x), $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))) < -2.00000000000000013e294

   1. Initial program 88.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-/.f64 N/A

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

      4. lift--.f64 N/A

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

      5. sub-to-mult N/A

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

      6. associate-/l* N/A

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

      7. lower-fma.f64 N/A

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

      8. lower--.f64 N/A

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

      9. lower-/.f64 N/A

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

      10. lift-*.f64 N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. lift-*.f64 N/A

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

      14. *-commutative N/A

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

      15. associate-/l* N/A

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

      16. lower-*.f64 N/A

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

      17. lower-/.f64 72.3

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

   3. Applied rewrites 72.3%

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

   if -2.00000000000000013e294 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 9.9999999999999993e158

   1. Initial program 88.6%

      \[\frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1} \]
   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 88.6

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

   3. Applied rewrites 88.6%

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

   if 9.9999999999999993e158 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

Alternative 3: 95.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot z - x\\ t_2 := \frac{x + \frac{y \cdot z - x}{t\_1}}{x + 1}\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{+294}:\\ \;\;\;\;\frac{z}{x - -1} \cdot \frac{y}{t\_1}\\ \mathbf{elif}\;t\_2 \leq 10^{+159}:\\ \;\;\;\;\frac{x + \frac{\mathsf{fma}\left(z, y, -x\right)}{t\_1}}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{y}{t} + x}{x - -1}\\ \end{array} \end{array} \]

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+294)
     (* (/ z (- x -1.0)) (/ y t_1))
     (if (<= t_2 1e+159)
       (/ (+ x (/ (fma z y (- x)) t_1)) (+ x 1.0))
       (/ (+ (/ y t) x) (- 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+294) {
		tmp = (z / (x - -1.0)) * (y / t_1);
	} else if (t_2 <= 1e+159) {
		tmp = (x + (fma(z, y, -x) / t_1)) / (x + 1.0);
	} else {
		tmp = ((y / t) + x) / (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+294)
		tmp = Float64(Float64(z / Float64(x - -1.0)) * Float64(y / t_1));
	elseif (t_2 <= 1e+159)
		tmp = Float64(Float64(x + Float64(fma(z, y, Float64(-x)) / t_1)) / Float64(x + 1.0));
	else
		tmp = Float64(Float64(Float64(y / t) + x) / 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+294], N[(N[(z / N[(x - -1.0), $MachinePrecision]), $MachinePrecision] * N[(y / t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+159], N[(N[(x + N[(N[(z * y + (-x)), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(y / t), $MachinePrecision] + x), $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))) < -2.00000000000000013e294

   1. Initial program 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-+.f64 N/A

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

      8. times-frac N/A

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

      9. lower-*.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. add-flip N/A

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

      13. metadata-eval N/A

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

      14. lower--.f64 N/A

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

      15. lower-/.f64 29.1

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

   6. Applied rewrites 29.1%

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

   if -2.00000000000000013e294 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 9.9999999999999993e158

   1. Initial program 88.6%

      \[\frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1} \]
   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 88.6

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

   3. Applied rewrites 88.6%

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

   if 9.9999999999999993e158 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

Alternative 4: 95.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot z - x\\ t_2 := \frac{x + \frac{y \cdot z - x}{t\_1}}{x + 1}\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{+294}:\\ \;\;\;\;\frac{z}{x - -1} \cdot \frac{y}{t\_1}\\ \mathbf{elif}\;t\_2 \leq 10^{+159}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{y}{t} + x}{x - -1}\\ \end{array} \end{array} \]

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+294)
     (* (/ z (- x -1.0)) (/ y t_1))
     (if (<= t_2 1e+159) t_2 (/ (+ (/ y t) x) (- 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+294) {
		tmp = (z / (x - -1.0)) * (y / t_1);
	} else if (t_2 <= 1e+159) {
		tmp = t_2;
	} else {
		tmp = ((y / t) + x) / (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) :: t_2
    real(8) :: tmp
    t_1 = (t * z) - x
    t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0d0)
    if (t_2 <= (-2d+294)) then
        tmp = (z / (x - (-1.0d0))) * (y / t_1)
    else if (t_2 <= 1d+159) then
        tmp = t_2
    else
        tmp = ((y / t) + x) / (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 = (t * z) - x;
	double t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0);
	double tmp;
	if (t_2 <= -2e+294) {
		tmp = (z / (x - -1.0)) * (y / t_1);
	} else if (t_2 <= 1e+159) {
		tmp = t_2;
	} else {
		tmp = ((y / t) + x) / (x - -1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (t * z) - x
	t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0)
	tmp = 0
	if t_2 <= -2e+294:
		tmp = (z / (x - -1.0)) * (y / t_1)
	elif t_2 <= 1e+159:
		tmp = t_2
	else:
		tmp = ((y / t) + x) / (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+294)
		tmp = Float64(Float64(z / Float64(x - -1.0)) * Float64(y / t_1));
	elseif (t_2 <= 1e+159)
		tmp = t_2;
	else
		tmp = Float64(Float64(Float64(y / t) + x) / Float64(x - -1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (t * z) - x;
	t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0);
	tmp = 0.0;
	if (t_2 <= -2e+294)
		tmp = (z / (x - -1.0)) * (y / t_1);
	elseif (t_2 <= 1e+159)
		tmp = t_2;
	else
		tmp = ((y / t) + x) / (x - -1.0);
	end
	tmp_2 = 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+294], N[(N[(z / N[(x - -1.0), $MachinePrecision]), $MachinePrecision] * N[(y / t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 1e+159], t$95$2, N[(N[(N[(y / t), $MachinePrecision] + x), $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))) < -2.00000000000000013e294

   1. Initial program 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-+.f64 N/A

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

      8. times-frac N/A

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

      9. lower-*.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. add-flip N/A

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

      13. metadata-eval N/A

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

      14. lower--.f64 N/A

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

      15. lower-/.f64 29.1

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

   6. Applied rewrites 29.1%

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

   if -2.00000000000000013e294 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 9.9999999999999993e158

   1. Initial program 88.6%

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

   if 9.9999999999999993e158 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

Alternative 5: 94.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot z - x\\ t_2 := x + \frac{y \cdot z - x}{t\_1}\\ t_3 := \frac{t\_2}{x + 1}\\ \mathbf{if}\;t\_3 \leq -2 \cdot 10^{+294}:\\ \;\;\;\;\frac{z}{x - -1} \cdot \frac{y}{t\_1}\\ \mathbf{elif}\;t\_3 \leq 0.0001:\\ \;\;\;\;\frac{t\_2}{1}\\ \mathbf{elif}\;t\_3 \leq 2:\\ \;\;\;\;\frac{x - \frac{x}{t\_1}}{x - -1}\\ \mathbf{elif}\;t\_3 \leq \infty:\\ \;\;\;\;\frac{z}{\left(x - -1\right) \cdot t\_1} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{y}{t} + x}{x - -1}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (* t z) x))
        (t_2 (+ x (/ (- (* y z) x) t_1)))
        (t_3 (/ t_2 (+ x 1.0))))
   (if (<= t_3 -2e+294)
     (* (/ z (- x -1.0)) (/ y t_1))
     (if (<= t_3 0.0001)
       (/ t_2 1.0)
       (if (<= t_3 2.0)
         (/ (- x (/ x t_1)) (- x -1.0))
         (if (<= t_3 INFINITY)
           (* (/ z (* (- x -1.0) t_1)) y)
           (/ (+ (/ y t) x) (- 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);
	double t_3 = t_2 / (x + 1.0);
	double tmp;
	if (t_3 <= -2e+294) {
		tmp = (z / (x - -1.0)) * (y / t_1);
	} else if (t_3 <= 0.0001) {
		tmp = t_2 / 1.0;
	} else if (t_3 <= 2.0) {
		tmp = (x - (x / t_1)) / (x - -1.0);
	} else if (t_3 <= ((double) INFINITY)) {
		tmp = (z / ((x - -1.0) * t_1)) * y;
	} else {
		tmp = ((y / t) + x) / (x - -1.0);
	}
	return tmp;
}

Java

public static 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);
	double t_3 = t_2 / (x + 1.0);
	double tmp;
	if (t_3 <= -2e+294) {
		tmp = (z / (x - -1.0)) * (y / t_1);
	} else if (t_3 <= 0.0001) {
		tmp = t_2 / 1.0;
	} else if (t_3 <= 2.0) {
		tmp = (x - (x / t_1)) / (x - -1.0);
	} else if (t_3 <= Double.POSITIVE_INFINITY) {
		tmp = (z / ((x - -1.0) * t_1)) * y;
	} else {
		tmp = ((y / t) + x) / (x - -1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (t * z) - x
	t_2 = x + (((y * z) - x) / t_1)
	t_3 = t_2 / (x + 1.0)
	tmp = 0
	if t_3 <= -2e+294:
		tmp = (z / (x - -1.0)) * (y / t_1)
	elif t_3 <= 0.0001:
		tmp = t_2 / 1.0
	elif t_3 <= 2.0:
		tmp = (x - (x / t_1)) / (x - -1.0)
	elif t_3 <= math.inf:
		tmp = (z / ((x - -1.0) * t_1)) * y
	else:
		tmp = ((y / t) + x) / (x - -1.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(t * z) - x)
	t_2 = Float64(x + Float64(Float64(Float64(y * z) - x) / t_1))
	t_3 = Float64(t_2 / Float64(x + 1.0))
	tmp = 0.0
	if (t_3 <= -2e+294)
		tmp = Float64(Float64(z / Float64(x - -1.0)) * Float64(y / t_1));
	elseif (t_3 <= 0.0001)
		tmp = Float64(t_2 / 1.0);
	elseif (t_3 <= 2.0)
		tmp = Float64(Float64(x - Float64(x / t_1)) / Float64(x - -1.0));
	elseif (t_3 <= Inf)
		tmp = Float64(Float64(z / Float64(Float64(x - -1.0) * t_1)) * y);
	else
		tmp = Float64(Float64(Float64(y / t) + x) / Float64(x - -1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (t * z) - x;
	t_2 = x + (((y * z) - x) / t_1);
	t_3 = t_2 / (x + 1.0);
	tmp = 0.0;
	if (t_3 <= -2e+294)
		tmp = (z / (x - -1.0)) * (y / t_1);
	elseif (t_3 <= 0.0001)
		tmp = t_2 / 1.0;
	elseif (t_3 <= 2.0)
		tmp = (x - (x / t_1)) / (x - -1.0);
	elseif (t_3 <= Inf)
		tmp = (z / ((x - -1.0) * t_1)) * y;
	else
		tmp = ((y / t) + x) / (x - -1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$3, -2e+294], N[(N[(z / N[(x - -1.0), $MachinePrecision]), $MachinePrecision] * N[(y / t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 0.0001], N[(t$95$2 / 1.0), $MachinePrecision], If[LessEqual[t$95$3, 2.0], N[(N[(x - N[(x / t$95$1), $MachinePrecision]), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, Infinity], N[(N[(z / N[(N[(x - -1.0), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision], N[(N[(N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]]]]]]]]

Derivation

1. Split input into 5 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -2.00000000000000013e294

   1. Initial program 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-+.f64 N/A

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

      8. times-frac N/A

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

      9. lower-*.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. add-flip N/A

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

      13. metadata-eval N/A

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

      14. lower--.f64 N/A

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

      15. lower-/.f64 29.1

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

   6. Applied rewrites 29.1%

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

   if -2.00000000000000013e294 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.00000000000000005e-4

   1. Initial program 88.6%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 45.4%

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

   if 1.00000000000000005e-4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2

   1. Initial program 88.6%

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

   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 67.3

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

   4. Applied rewrites 67.3%

      \[\leadsto \frac{\color{blue}{x - \frac{x}{t \cdot z - x}}}{x + 1} \]
   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 67.3

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

   6. Applied rewrites 67.3%

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

   if 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 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 31.3

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

      7. lift-+.f64 N/A

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

      8. +-commutative N/A

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

      9. add-flip N/A

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

      10. metadata-eval N/A

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

      11. lower--.f64 31.3

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

   6. Applied rewrites 31.3%

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

   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 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

      \[\leadsto \color{blue}{\frac{\frac{y}{t} + x}{x - -1}} \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 6: 93.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{y}{t} + x}{x - -1}\\ t_2 := t \cdot z - x\\ t_3 := \frac{z}{\left(x - -1\right) \cdot t\_2} \cdot y\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -2:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 0.0001:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 2:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x - -1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ (/ y t) x) (- x -1.0)))
        (t_2 (- (* t z) x))
        (t_3 (* (/ z (* (- x -1.0) t_2)) y))
        (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
   (if (<= t_4 -2.0)
     t_3
     (if (<= t_4 0.0001)
       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 = ((y / t) + x) / (x - -1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / ((x - -1.0) * t_2)) * y;
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -2.0) {
		tmp = t_3;
	} else if (t_4 <= 0.0001) {
		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 = ((y / t) + x) / (x - -1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / ((x - -1.0) * t_2)) * y;
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -2.0) {
		tmp = t_3;
	} else if (t_4 <= 0.0001) {
		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 = ((y / t) + x) / (x - -1.0)
	t_2 = (t * z) - x
	t_3 = (z / ((x - -1.0) * t_2)) * y
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -2.0:
		tmp = t_3
	elif t_4 <= 0.0001:
		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(Float64(y / t) + x) / Float64(x - -1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(z / Float64(Float64(x - -1.0) * t_2)) * y)
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -2.0)
		tmp = t_3;
	elseif (t_4 <= 0.0001)
		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 = ((y / t) + x) / (x - -1.0);
	t_2 = (t * z) - x;
	t_3 = (z / ((x - -1.0) * t_2)) * y;
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -2.0)
		tmp = t_3;
	elseif (t_4 <= 0.0001)
		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[(N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z / N[(N[(x - -1.0), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision] * y), $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, -2.0], t$95$3, If[LessEqual[t$95$4, 0.0001], 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))) < -2 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 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 31.3

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

      7. lift-+.f64 N/A

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

      8. +-commutative N/A

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

      9. add-flip N/A

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

      10. metadata-eval N/A

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

      11. lower--.f64 31.3

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

   6. Applied rewrites 31.3%

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

   if -2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.00000000000000005e-4 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 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

   if 1.00000000000000005e-4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2

   1. Initial program 88.6%

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

   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 67.3

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

   4. Applied rewrites 67.3%

      \[\leadsto \frac{\color{blue}{x - \frac{x}{t \cdot z - x}}}{x + 1} \]
   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 67.3

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

   6. Applied rewrites 67.3%

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

Alternative 7: 93.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{y}{t} + x}{x - -1}\\ t_2 := t \cdot z - x\\ t_3 := \frac{z}{\left(x - -1\right) \cdot t\_2} \cdot y\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -2:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 0.99995:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 2:\\ \;\;\;\;\frac{-1 - x}{-1 - x}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ (/ y t) x) (- x -1.0)))
        (t_2 (- (* t z) x))
        (t_3 (* (/ z (* (- x -1.0) t_2)) y))
        (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
   (if (<= t_4 -2.0)
     t_3
     (if (<= t_4 0.99995)
       t_1
       (if (<= t_4 2.0)
         (/ (- -1.0 x) (- -1.0 x))
         (if (<= t_4 INFINITY) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = ((y / t) + x) / (x - -1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / ((x - -1.0) * t_2)) * y;
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -2.0) {
		tmp = t_3;
	} else if (t_4 <= 0.99995) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (-1.0 - x) / (-1.0 - x);
	} 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 = ((y / t) + x) / (x - -1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / ((x - -1.0) * t_2)) * y;
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -2.0) {
		tmp = t_3;
	} else if (t_4 <= 0.99995) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (-1.0 - x) / (-1.0 - x);
	} 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 = ((y / t) + x) / (x - -1.0)
	t_2 = (t * z) - x
	t_3 = (z / ((x - -1.0) * t_2)) * y
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -2.0:
		tmp = t_3
	elif t_4 <= 0.99995:
		tmp = t_1
	elif t_4 <= 2.0:
		tmp = (-1.0 - x) / (-1.0 - x)
	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(Float64(y / t) + x) / Float64(x - -1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(z / Float64(Float64(x - -1.0) * t_2)) * y)
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -2.0)
		tmp = t_3;
	elseif (t_4 <= 0.99995)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = Float64(Float64(-1.0 - x) / Float64(-1.0 - x));
	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 = ((y / t) + x) / (x - -1.0);
	t_2 = (t * z) - x;
	t_3 = (z / ((x - -1.0) * t_2)) * y;
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -2.0)
		tmp = t_3;
	elseif (t_4 <= 0.99995)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = (-1.0 - x) / (-1.0 - x);
	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[(N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z / N[(N[(x - -1.0), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision] * y), $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, -2.0], t$95$3, If[LessEqual[t$95$4, 0.99995], t$95$1, If[LessEqual[t$95$4, 2.0], N[(N[(-1.0 - x), $MachinePrecision] / N[(-1.0 - x), $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))) < -2 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 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 31.3

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

      7. lift-+.f64 N/A

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

      8. +-commutative N/A

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

      9. add-flip N/A

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

      10. metadata-eval N/A

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

      11. lower--.f64 31.3

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

   6. Applied rewrites 31.3%

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

   if -2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.999950000000000006 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 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

   if 0.999950000000000006 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2

   1. Initial program 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 77.6

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

   4. Applied rewrites 77.6%

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

   6. Applied rewrites 77.4%

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

   7. Taylor expanded in z around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 54.2

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

   9. Applied rewrites 54.2%

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

       1. lift-*.f64 N/A

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

       2. lift-/.f64 N/A

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

       3. frac-2neg N/A

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

       4. metadata-eval N/A

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

       5. mult-flip-rev N/A

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

       6. lower-/.f64 N/A

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

       7. lift-*.f64 N/A

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

       8. mul-1-neg N/A

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

       9. lift-+.f64 N/A

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

       10. distribute-neg-in N/A

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

       11. metadata-eval N/A

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

       12. sub-flip N/A

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

       13. lower--.f64 N/A

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

       14. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(x - -1\right)\right)\right)} \]

       15. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite<=}\left(sub-negate-rev, \left(-1 - x\right)\right)} \]

       16. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite=>}\left(lower--.f64, \left(-1 - x\right)\right)} \]

   11. Applied rewrites 54.3%

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

Alternative 8: 90.5% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{y}{t} + x}{x - -1}\\ t_2 := t \cdot z - x\\ t_3 := z \cdot \frac{y}{\left(x - -1\right) \cdot t\_2}\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -2:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 0.99995:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 2:\\ \;\;\;\;\frac{-1 - x}{-1 - x}\\ \mathbf{elif}\;t\_4 \leq 10^{+159}:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ (/ y t) x) (- x -1.0)))
        (t_2 (- (* t z) x))
        (t_3 (* z (/ y (* (- x -1.0) t_2))))
        (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
   (if (<= t_4 -2.0)
     t_3
     (if (<= t_4 0.99995)
       t_1
       (if (<= t_4 2.0)
         (/ (- -1.0 x) (- -1.0 x))
         (if (<= t_4 1e+159) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = ((y / t) + x) / (x - -1.0);
	double t_2 = (t * z) - x;
	double t_3 = z * (y / ((x - -1.0) * t_2));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -2.0) {
		tmp = t_3;
	} else if (t_4 <= 0.99995) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (-1.0 - x) / (-1.0 - x);
	} else if (t_4 <= 1e+159) {
		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 = ((y / t) + x) / (x - (-1.0d0))
    t_2 = (t * z) - x
    t_3 = z * (y / ((x - (-1.0d0)) * t_2))
    t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0d0)
    if (t_4 <= (-2.0d0)) then
        tmp = t_3
    else if (t_4 <= 0.99995d0) then
        tmp = t_1
    else if (t_4 <= 2.0d0) then
        tmp = ((-1.0d0) - x) / ((-1.0d0) - x)
    else if (t_4 <= 1d+159) 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 = ((y / t) + x) / (x - -1.0);
	double t_2 = (t * z) - x;
	double t_3 = z * (y / ((x - -1.0) * t_2));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -2.0) {
		tmp = t_3;
	} else if (t_4 <= 0.99995) {
		tmp = t_1;
	} else if (t_4 <= 2.0) {
		tmp = (-1.0 - x) / (-1.0 - x);
	} else if (t_4 <= 1e+159) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = ((y / t) + x) / (x - -1.0)
	t_2 = (t * z) - x
	t_3 = z * (y / ((x - -1.0) * t_2))
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -2.0:
		tmp = t_3
	elif t_4 <= 0.99995:
		tmp = t_1
	elif t_4 <= 2.0:
		tmp = (-1.0 - x) / (-1.0 - x)
	elif t_4 <= 1e+159:
		tmp = t_3
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(y / t) + x) / Float64(x - -1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(z * Float64(y / Float64(Float64(x - -1.0) * 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 <= -2.0)
		tmp = t_3;
	elseif (t_4 <= 0.99995)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = Float64(Float64(-1.0 - x) / Float64(-1.0 - x));
	elseif (t_4 <= 1e+159)
		tmp = t_3;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = ((y / t) + x) / (x - -1.0);
	t_2 = (t * z) - x;
	t_3 = z * (y / ((x - -1.0) * t_2));
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -2.0)
		tmp = t_3;
	elseif (t_4 <= 0.99995)
		tmp = t_1;
	elseif (t_4 <= 2.0)
		tmp = (-1.0 - x) / (-1.0 - x);
	elseif (t_4 <= 1e+159)
		tmp = t_3;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(z * N[(y / N[(N[(x - -1.0), $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, -2.0], t$95$3, If[LessEqual[t$95$4, 0.99995], t$95$1, If[LessEqual[t$95$4, 2.0], N[(N[(-1.0 - x), $MachinePrecision] / N[(-1.0 - x), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, 1e+159], 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))) < -2 or 2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 9.9999999999999993e158

   1. Initial program 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. associate-/l* N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 28.3

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

      7. lift-+.f64 N/A

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

      8. +-commutative N/A

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

      9. add-flip N/A

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

      10. metadata-eval N/A

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

      11. lower--.f64 28.3

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

   6. Applied rewrites 28.3%

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

   if -2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.999950000000000006 or 9.9999999999999993e158 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

   if 0.999950000000000006 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2

   1. Initial program 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 77.6

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

   4. Applied rewrites 77.6%

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

   6. Applied rewrites 77.4%

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

   7. Taylor expanded in z around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 54.2

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

   9. Applied rewrites 54.2%

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

       1. lift-*.f64 N/A

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

       2. lift-/.f64 N/A

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

       3. frac-2neg N/A

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

       4. metadata-eval N/A

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

       5. mult-flip-rev N/A

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

       6. lower-/.f64 N/A

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

       7. lift-*.f64 N/A

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

       8. mul-1-neg N/A

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

       9. lift-+.f64 N/A

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

       10. distribute-neg-in N/A

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

       11. metadata-eval N/A

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

       12. sub-flip N/A

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

       13. lower--.f64 N/A

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

       14. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(x - -1\right)\right)\right)} \]

       15. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite<=}\left(sub-negate-rev, \left(-1 - x\right)\right)} \]

       16. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite=>}\left(lower--.f64, \left(-1 - x\right)\right)} \]

   11. Applied rewrites 54.3%

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

Alternative 9: 87.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{y}{t} + x}{x - -1}\\ t_2 := t \cdot z - x\\ t_3 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_3 \leq -2:\\ \;\;\;\;z \cdot \frac{y}{t\_2}\\ \mathbf{elif}\;t\_3 \leq 0.99995:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_3 \leq 1.000005:\\ \;\;\;\;\frac{-1 - x}{-1 - x}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ (/ y t) x) (- x -1.0)))
        (t_2 (- (* t z) x))
        (t_3 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
   (if (<= t_3 -2.0)
     (* z (/ y t_2))
     (if (<= t_3 0.99995)
       t_1
       (if (<= t_3 1.000005) (/ (- -1.0 x) (- -1.0 x)) t_1)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = ((y / t) + x) / (x - -1.0);
	double t_2 = (t * z) - x;
	double t_3 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_3 <= -2.0) {
		tmp = z * (y / t_2);
	} else if (t_3 <= 0.99995) {
		tmp = t_1;
	} else if (t_3 <= 1.000005) {
		tmp = (-1.0 - x) / (-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) :: t_3
    real(8) :: tmp
    t_1 = ((y / t) + x) / (x - (-1.0d0))
    t_2 = (t * z) - x
    t_3 = (x + (((y * z) - x) / t_2)) / (x + 1.0d0)
    if (t_3 <= (-2.0d0)) then
        tmp = z * (y / t_2)
    else if (t_3 <= 0.99995d0) then
        tmp = t_1
    else if (t_3 <= 1.000005d0) then
        tmp = ((-1.0d0) - x) / ((-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 = ((y / t) + x) / (x - -1.0);
	double t_2 = (t * z) - x;
	double t_3 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_3 <= -2.0) {
		tmp = z * (y / t_2);
	} else if (t_3 <= 0.99995) {
		tmp = t_1;
	} else if (t_3 <= 1.000005) {
		tmp = (-1.0 - x) / (-1.0 - x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = ((y / t) + x) / (x - -1.0)
	t_2 = (t * z) - x
	t_3 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_3 <= -2.0:
		tmp = z * (y / t_2)
	elif t_3 <= 0.99995:
		tmp = t_1
	elif t_3 <= 1.000005:
		tmp = (-1.0 - x) / (-1.0 - x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(y / t) + x) / Float64(x - -1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_3 <= -2.0)
		tmp = Float64(z * Float64(y / t_2));
	elseif (t_3 <= 0.99995)
		tmp = t_1;
	elseif (t_3 <= 1.000005)
		tmp = Float64(Float64(-1.0 - x) / Float64(-1.0 - x));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = ((y / t) + x) / (x - -1.0);
	t_2 = (t * z) - x;
	t_3 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_3 <= -2.0)
		tmp = z * (y / t_2);
	elseif (t_3 <= 0.99995)
		tmp = t_1;
	elseif (t_3 <= 1.000005)
		tmp = (-1.0 - x) / (-1.0 - x);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(y / t), $MachinePrecision] + x), $MachinePrecision] / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = 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$3, -2.0], N[(z * N[(y / t$95$2), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 0.99995], t$95$1, If[LessEqual[t$95$3, 1.000005], N[(N[(-1.0 - x), $MachinePrecision] / N[(-1.0 - x), $MachinePrecision]), $MachinePrecision], 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))) < -2

   1. Initial program 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-+.f64 N/A

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

      8. times-frac N/A

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

      9. lower-*.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. add-flip N/A

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

      13. metadata-eval N/A

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

      14. lower--.f64 N/A

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

      15. lower-/.f64 29.1

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

   6. Applied rewrites 29.1%

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

   7. Taylor expanded in x around 0

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

   9. Applied rewrites 25.0%

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

   if -2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.999950000000000006 or 1.00000500000000003 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

   if 0.999950000000000006 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.00000500000000003

   1. Initial program 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 77.6

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

   4. Applied rewrites 77.6%

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

   6. Applied rewrites 77.4%

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

   7. Taylor expanded in z around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 54.2

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

   9. Applied rewrites 54.2%

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

       1. lift-*.f64 N/A

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

       2. lift-/.f64 N/A

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

       3. frac-2neg N/A

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

       4. metadata-eval N/A

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

       5. mult-flip-rev N/A

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

       6. lower-/.f64 N/A

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

       7. lift-*.f64 N/A

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

       8. mul-1-neg N/A

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

       9. lift-+.f64 N/A

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

       10. distribute-neg-in N/A

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

       11. metadata-eval N/A

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

       12. sub-flip N/A

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

       13. lower--.f64 N/A

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

       14. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(x - -1\right)\right)\right)} \]

       15. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite<=}\left(sub-negate-rev, \left(-1 - x\right)\right)} \]

       16. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite=>}\left(lower--.f64, \left(-1 - x\right)\right)} \]

   11. Applied rewrites 54.3%

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

Alternative 10: 83.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot z - x\\ t_2 := \frac{x + \frac{y \cdot z - x}{t\_1}}{x + 1}\\ t_3 := \frac{-1 - x}{-1 - x}\\ \mathbf{if}\;t\_2 \leq -2:\\ \;\;\;\;z \cdot \frac{y}{t\_1}\\ \mathbf{elif}\;t\_2 \leq 0.0001:\\ \;\;\;\;\frac{\frac{y}{t} + x}{1}\\ \mathbf{elif}\;t\_2 \leq 200000:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_2 \leq \infty:\\ \;\;\;\;\frac{y}{t \cdot \left(1 + x\right)}\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

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)))
        (t_3 (/ (- -1.0 x) (- -1.0 x))))
   (if (<= t_2 -2.0)
     (* z (/ y t_1))
     (if (<= t_2 0.0001)
       (/ (+ (/ y t) x) 1.0)
       (if (<= t_2 200000.0)
         t_3
         (if (<= t_2 INFINITY) (/ y (* t (+ 1.0 x))) t_3))))))

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 t_3 = (-1.0 - x) / (-1.0 - x);
	double tmp;
	if (t_2 <= -2.0) {
		tmp = z * (y / t_1);
	} else if (t_2 <= 0.0001) {
		tmp = ((y / t) + x) / 1.0;
	} else if (t_2 <= 200000.0) {
		tmp = t_3;
	} else if (t_2 <= ((double) INFINITY)) {
		tmp = y / (t * (1.0 + x));
	} else {
		tmp = t_3;
	}
	return tmp;
}

Java

public static 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 t_3 = (-1.0 - x) / (-1.0 - x);
	double tmp;
	if (t_2 <= -2.0) {
		tmp = z * (y / t_1);
	} else if (t_2 <= 0.0001) {
		tmp = ((y / t) + x) / 1.0;
	} else if (t_2 <= 200000.0) {
		tmp = t_3;
	} else if (t_2 <= Double.POSITIVE_INFINITY) {
		tmp = y / (t * (1.0 + x));
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (t * z) - x
	t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0)
	t_3 = (-1.0 - x) / (-1.0 - x)
	tmp = 0
	if t_2 <= -2.0:
		tmp = z * (y / t_1)
	elif t_2 <= 0.0001:
		tmp = ((y / t) + x) / 1.0
	elif t_2 <= 200000.0:
		tmp = t_3
	elif t_2 <= math.inf:
		tmp = y / (t * (1.0 + x))
	else:
		tmp = t_3
	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))
	t_3 = Float64(Float64(-1.0 - x) / Float64(-1.0 - x))
	tmp = 0.0
	if (t_2 <= -2.0)
		tmp = Float64(z * Float64(y / t_1));
	elseif (t_2 <= 0.0001)
		tmp = Float64(Float64(Float64(y / t) + x) / 1.0);
	elseif (t_2 <= 200000.0)
		tmp = t_3;
	elseif (t_2 <= Inf)
		tmp = Float64(y / Float64(t * Float64(1.0 + x)));
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (t * z) - x;
	t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0);
	t_3 = (-1.0 - x) / (-1.0 - x);
	tmp = 0.0;
	if (t_2 <= -2.0)
		tmp = z * (y / t_1);
	elseif (t_2 <= 0.0001)
		tmp = ((y / t) + x) / 1.0;
	elseif (t_2 <= 200000.0)
		tmp = t_3;
	elseif (t_2 <= Inf)
		tmp = y / (t * (1.0 + x));
	else
		tmp = t_3;
	end
	tmp_2 = 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]}, Block[{t$95$3 = N[(N[(-1.0 - x), $MachinePrecision] / N[(-1.0 - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -2.0], N[(z * N[(y / t$95$1), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 0.0001], N[(N[(N[(y / t), $MachinePrecision] + x), $MachinePrecision] / 1.0), $MachinePrecision], If[LessEqual[t$95$2, 200000.0], t$95$3, If[LessEqual[t$95$2, Infinity], N[(y / N[(t * N[(1.0 + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$3]]]]]]]

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))) < -2

   1. Initial program 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-+.f64 N/A

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

      8. times-frac N/A

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

      9. lower-*.f64 N/A

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

      10. lower-/.f64 N/A

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

      11. lift-+.f64 N/A

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

      12. add-flip N/A

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

      13. metadata-eval N/A

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

      14. lower--.f64 N/A

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

      15. lower-/.f64 29.1

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

   6. Applied rewrites 29.1%

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

   7. Taylor expanded in x around 0

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

   9. Applied rewrites 25.0%

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

   if -2 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.00000000000000005e-4

   1. Initial program 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

   7. Taylor expanded in x around 0

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

   9. Applied rewrites 34.3%

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

   if 1.00000000000000005e-4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2e5 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 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 77.6

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

   4. Applied rewrites 77.6%

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

   6. Applied rewrites 77.4%

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

   7. Taylor expanded in z around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 54.2

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

   9. Applied rewrites 54.2%

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

       1. lift-*.f64 N/A

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

       2. lift-/.f64 N/A

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

       3. frac-2neg N/A

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

       4. metadata-eval N/A

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

       5. mult-flip-rev N/A

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

       6. lower-/.f64 N/A

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

       7. lift-*.f64 N/A

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

       8. mul-1-neg N/A

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

       9. lift-+.f64 N/A

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

       10. distribute-neg-in N/A

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

       11. metadata-eval N/A

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

       12. sub-flip N/A

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

       13. lower--.f64 N/A

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

       14. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(x - -1\right)\right)\right)} \]

       15. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite<=}\left(sub-negate-rev, \left(-1 - x\right)\right)} \]

       16. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite=>}\left(lower--.f64, \left(-1 - x\right)\right)} \]

   11. Applied rewrites 54.3%

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

   if 2e5 < (/.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 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

   5. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 26.6

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

   7. Applied rewrites 26.6%

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

Alternative 11: 81.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ t_2 := \frac{-1 - x}{-1 - x}\\ \mathbf{if}\;t\_1 \leq 0.0001:\\ \;\;\;\;\frac{\frac{y}{t} + x}{1}\\ \mathbf{elif}\;t\_1 \leq 200000:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;\frac{y}{t \cdot \left(1 + x\right)}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0)))
        (t_2 (/ (- -1.0 x) (- -1.0 x))))
   (if (<= t_1 0.0001)
     (/ (+ (/ y t) x) 1.0)
     (if (<= t_1 200000.0)
       t_2
       (if (<= t_1 INFINITY) (/ y (* t (+ 1.0 x))) t_2)))))

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 t_2 = (-1.0 - x) / (-1.0 - x);
	double tmp;
	if (t_1 <= 0.0001) {
		tmp = ((y / t) + x) / 1.0;
	} else if (t_1 <= 200000.0) {
		tmp = t_2;
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = y / (t * (1.0 + x));
	} else {
		tmp = t_2;
	}
	return tmp;
}

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 t_2 = (-1.0 - x) / (-1.0 - x);
	double tmp;
	if (t_1 <= 0.0001) {
		tmp = ((y / t) + x) / 1.0;
	} else if (t_1 <= 200000.0) {
		tmp = t_2;
	} else if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = y / (t * (1.0 + x));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	t_2 = (-1.0 - x) / (-1.0 - x)
	tmp = 0
	if t_1 <= 0.0001:
		tmp = ((y / t) + x) / 1.0
	elif t_1 <= 200000.0:
		tmp = t_2
	elif t_1 <= math.inf:
		tmp = y / (t * (1.0 + x))
	else:
		tmp = t_2
	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))
	t_2 = Float64(Float64(-1.0 - x) / Float64(-1.0 - x))
	tmp = 0.0
	if (t_1 <= 0.0001)
		tmp = Float64(Float64(Float64(y / t) + x) / 1.0);
	elseif (t_1 <= 200000.0)
		tmp = t_2;
	elseif (t_1 <= Inf)
		tmp = Float64(y / Float64(t * Float64(1.0 + x)));
	else
		tmp = t_2;
	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);
	t_2 = (-1.0 - x) / (-1.0 - x);
	tmp = 0.0;
	if (t_1 <= 0.0001)
		tmp = ((y / t) + x) / 1.0;
	elseif (t_1 <= 200000.0)
		tmp = t_2;
	elseif (t_1 <= Inf)
		tmp = y / (t * (1.0 + x));
	else
		tmp = t_2;
	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]}, Block[{t$95$2 = N[(N[(-1.0 - x), $MachinePrecision] / N[(-1.0 - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 0.0001], N[(N[(N[(y / t), $MachinePrecision] + x), $MachinePrecision] / 1.0), $MachinePrecision], If[LessEqual[t$95$1, 200000.0], t$95$2, If[LessEqual[t$95$1, Infinity], N[(y / N[(t * N[(1.0 + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

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))) < 1.00000000000000005e-4

   1. Initial program 88.6%

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

   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 70.6

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

   4. Applied rewrites 70.6%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 70.6

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

      4. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(lift-+.f64, \left(x + 1\right)\right)} \]

      5. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite=>}\left(add-flip, \left(x - \left(\mathsf{neg}\left(1\right)\right)\right)\right)} \]

      6. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{x - \mathsf{Rewrite=>}\left(metadata-eval, -1\right)} \]

      7. lower-+.f64 N/A

         \[\leadsto \frac{\frac{y}{t} + \color{blue}{x}}{\mathsf{Rewrite<=}\left(lift--.f64, \left(x - -1\right)\right)} \]

   6. Applied rewrites 70.6%

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

   7. Taylor expanded in x around 0

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

   9. Applied rewrites 34.3%

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

   if 1.00000000000000005e-4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2e5 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 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 77.6

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

   4. Applied rewrites 77.6%

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

   6. Applied rewrites 77.4%

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

   7. Taylor expanded in z around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 54.2

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

   9. Applied rewrites 54.2%

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

       1. lift-*.f64 N/A

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

       2. lift-/.f64 N/A

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

       3. frac-2neg N/A

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

       4. metadata-eval N/A

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

       5. mult-flip-rev N/A

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

       6. lower-/.f64 N/A

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

       7. lift-*.f64 N/A

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

       8. mul-1-neg N/A

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

       9. lift-+.f64 N/A

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

       10. distribute-neg-in N/A

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

       11. metadata-eval N/A

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

       12. sub-flip N/A

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

       13. lower--.f64 N/A

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

       14. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(x - -1\right)\right)\right)} \]

       15. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite<=}\left(sub-negate-rev, \left(-1 - x\right)\right)} \]

       16. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite=>}\left(lower--.f64, \left(-1 - x\right)\right)} \]

   11. Applied rewrites 54.3%

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

   if 2e5 < (/.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 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

   5. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 26.6

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

   7. Applied rewrites 26.6%

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

Alternative 12: 76.0% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{-1 - x}{-1 - x}\\ t_2 := \frac{y}{t \cdot \left(1 + x\right)}\\ t_3 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_3 \leq -2 \cdot 10^{-114}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_3 \leq 0.99995:\\ \;\;\;\;\frac{x}{x - -1}\\ \mathbf{elif}\;t\_3 \leq 200000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_3 \leq \infty:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- -1.0 x) (- -1.0 x)))
        (t_2 (/ y (* t (+ 1.0 x))))
        (t_3 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
   (if (<= t_3 -2e-114)
     t_2
     (if (<= t_3 0.99995)
       (/ x (- x -1.0))
       (if (<= t_3 200000.0) t_1 (if (<= t_3 INFINITY) t_2 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (-1.0 - x) / (-1.0 - x);
	double t_2 = y / (t * (1.0 + x));
	double t_3 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_3 <= -2e-114) {
		tmp = t_2;
	} else if (t_3 <= 0.99995) {
		tmp = x / (x - -1.0);
	} else if (t_3 <= 200000.0) {
		tmp = t_1;
	} else if (t_3 <= ((double) INFINITY)) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (-1.0 - x) / (-1.0 - x);
	double t_2 = y / (t * (1.0 + x));
	double t_3 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_3 <= -2e-114) {
		tmp = t_2;
	} else if (t_3 <= 0.99995) {
		tmp = x / (x - -1.0);
	} else if (t_3 <= 200000.0) {
		tmp = t_1;
	} else if (t_3 <= Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (-1.0 - x) / (-1.0 - x)
	t_2 = y / (t * (1.0 + x))
	t_3 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_3 <= -2e-114:
		tmp = t_2
	elif t_3 <= 0.99995:
		tmp = x / (x - -1.0)
	elif t_3 <= 200000.0:
		tmp = t_1
	elif t_3 <= math.inf:
		tmp = t_2
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(-1.0 - x) / Float64(-1.0 - x))
	t_2 = Float64(y / Float64(t * Float64(1.0 + x)))
	t_3 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_3 <= -2e-114)
		tmp = t_2;
	elseif (t_3 <= 0.99995)
		tmp = Float64(x / Float64(x - -1.0));
	elseif (t_3 <= 200000.0)
		tmp = t_1;
	elseif (t_3 <= Inf)
		tmp = t_2;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (-1.0 - x) / (-1.0 - x);
	t_2 = y / (t * (1.0 + x));
	t_3 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_3 <= -2e-114)
		tmp = t_2;
	elseif (t_3 <= 0.99995)
		tmp = x / (x - -1.0);
	elseif (t_3 <= 200000.0)
		tmp = t_1;
	elseif (t_3 <= Inf)
		tmp = t_2;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(-1.0 - x), $MachinePrecision] / N[(-1.0 - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(y / N[(t * N[(1.0 + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = 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$3, -2e-114], t$95$2, If[LessEqual[t$95$3, 0.99995], N[(x / N[(x - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, 200000.0], t$95$1, If[LessEqual[t$95$3, Infinity], t$95$2, 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))) < -2.0000000000000001e-114 or 2e5 < (/.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 88.6%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower--.f64 N/A

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

      6. lower-*.f64 27.9

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

   4. Applied rewrites 27.9%

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

   5. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. lower-+.f64 26.6

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

   7. Applied rewrites 26.6%

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

   if -2.0000000000000001e-114 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.999950000000000006

   1. Initial program 88.6%

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

   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 56.5

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

   4. Applied rewrites 56.5%

      \[\leadsto \color{blue}{\frac{x}{1 + x}} \]
   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 56.5

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

   6. Applied rewrites 56.5%

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

   if 0.999950000000000006 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2e5 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 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 77.6

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

   4. Applied rewrites 77.6%

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

   6. Applied rewrites 77.4%

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

   7. Taylor expanded in z around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 54.2

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

   9. Applied rewrites 54.2%

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

       1. lift-*.f64 N/A

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

       2. lift-/.f64 N/A

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

       3. frac-2neg N/A

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

       4. metadata-eval N/A

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

       5. mult-flip-rev N/A

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

       6. lower-/.f64 N/A

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

       7. lift-*.f64 N/A

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

       8. mul-1-neg N/A

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

       9. lift-+.f64 N/A

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

       10. distribute-neg-in N/A

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

       11. metadata-eval N/A

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

       12. sub-flip N/A

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

       13. lower--.f64 N/A

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

       14. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(x - -1\right)\right)\right)} \]

       15. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite<=}\left(sub-negate-rev, \left(-1 - x\right)\right)} \]

       16. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite=>}\left(lower--.f64, \left(-1 - x\right)\right)} \]

   11. Applied rewrites 54.3%

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

Alternative 13: 74.4% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ t_2 := \frac{-1 - x}{-1 - x}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{-114}:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{elif}\;t\_1 \leq 0.99995:\\ \;\;\;\;\frac{x}{x - -1}\\ \mathbf{elif}\;t\_1 \leq 200000:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0)))
        (t_2 (/ (- -1.0 x) (- -1.0 x))))
   (if (<= t_1 -2e-114)
     (/ y t)
     (if (<= t_1 0.99995)
       (/ x (- x -1.0))
       (if (<= t_1 200000.0) t_2 (if (<= t_1 INFINITY) (/ y t) t_2))))))

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 t_2 = (-1.0 - x) / (-1.0 - x);
	double tmp;
	if (t_1 <= -2e-114) {
		tmp = y / t;
	} else if (t_1 <= 0.99995) {
		tmp = x / (x - -1.0);
	} else if (t_1 <= 200000.0) {
		tmp = t_2;
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = y / t;
	} else {
		tmp = t_2;
	}
	return tmp;
}

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 t_2 = (-1.0 - x) / (-1.0 - x);
	double tmp;
	if (t_1 <= -2e-114) {
		tmp = y / t;
	} else if (t_1 <= 0.99995) {
		tmp = x / (x - -1.0);
	} else if (t_1 <= 200000.0) {
		tmp = t_2;
	} else if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = y / t;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	t_2 = (-1.0 - x) / (-1.0 - x)
	tmp = 0
	if t_1 <= -2e-114:
		tmp = y / t
	elif t_1 <= 0.99995:
		tmp = x / (x - -1.0)
	elif t_1 <= 200000.0:
		tmp = t_2
	elif t_1 <= math.inf:
		tmp = y / t
	else:
		tmp = t_2
	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))
	t_2 = Float64(Float64(-1.0 - x) / Float64(-1.0 - x))
	tmp = 0.0
	if (t_1 <= -2e-114)
		tmp = Float64(y / t);
	elseif (t_1 <= 0.99995)
		tmp = Float64(x / Float64(x - -1.0));
	elseif (t_1 <= 200000.0)
		tmp = t_2;
	elseif (t_1 <= Inf)
		tmp = Float64(y / t);
	else
		tmp = t_2;
	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);
	t_2 = (-1.0 - x) / (-1.0 - x);
	tmp = 0.0;
	if (t_1 <= -2e-114)
		tmp = y / t;
	elseif (t_1 <= 0.99995)
		tmp = x / (x - -1.0);
	elseif (t_1 <= 200000.0)
		tmp = t_2;
	elseif (t_1 <= Inf)
		tmp = y / t;
	else
		tmp = t_2;
	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]}, Block[{t$95$2 = N[(N[(-1.0 - x), $MachinePrecision] / N[(-1.0 - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e-114], N[(y / t), $MachinePrecision], If[LessEqual[t$95$1, 0.99995], N[(x / N[(x - -1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 200000.0], t$95$2, If[LessEqual[t$95$1, Infinity], N[(y / t), $MachinePrecision], t$95$2]]]]]]

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))) < -2.0000000000000001e-114 or 2e5 < (/.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 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 24.8

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

   4. Applied rewrites 24.8%

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

   if -2.0000000000000001e-114 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 0.999950000000000006

   1. Initial program 88.6%

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

   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 56.5

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

   4. Applied rewrites 56.5%

      \[\leadsto \color{blue}{\frac{x}{1 + x}} \]
   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 56.5

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

   6. Applied rewrites 56.5%

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

   if 0.999950000000000006 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 2e5 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 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 77.6

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

   4. Applied rewrites 77.6%

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

   6. Applied rewrites 77.4%

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

   7. Taylor expanded in z around 0

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 54.2

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

   9. Applied rewrites 54.2%

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

       1. lift-*.f64 N/A

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

       2. lift-/.f64 N/A

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

       3. frac-2neg N/A

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

       4. metadata-eval N/A

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

       5. mult-flip-rev N/A

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

       6. lower-/.f64 N/A

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

       7. lift-*.f64 N/A

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

       8. mul-1-neg N/A

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

       9. lift-+.f64 N/A

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

       10. distribute-neg-in N/A

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

       11. metadata-eval N/A

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

       12. sub-flip N/A

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

       13. lower--.f64 N/A

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

       14. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(x - -1\right)\right)\right)} \]

       15. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite<=}\left(sub-negate-rev, \left(-1 - x\right)\right)} \]

       16. lower--.f64 N/A

           \[\leadsto \frac{-1 - \color{blue}{x}}{\mathsf{Rewrite=>}\left(lower--.f64, \left(-1 - x\right)\right)} \]

   11. Applied rewrites 54.3%

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

Alternative 14: 67.9% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{x - -1}\\ \mathbf{if}\;x \leq -3.4 \cdot 10^{-29}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 2100:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ x (- x -1.0))))
   (if (<= x -3.4e-29) t_1 (if (<= x 2100.0) (/ 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 <= -3.4e-29) {
		tmp = t_1;
	} else if (x <= 2100.0) {
		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 <= (-3.4d-29)) then
        tmp = t_1
    else if (x <= 2100.0d0) 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 <= -3.4e-29) {
		tmp = t_1;
	} else if (x <= 2100.0) {
		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 <= -3.4e-29:
		tmp = t_1
	elif x <= 2100.0:
		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 <= -3.4e-29)
		tmp = t_1;
	elseif (x <= 2100.0)
		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 <= -3.4e-29)
		tmp = t_1;
	elseif (x <= 2100.0)
		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, -3.4e-29], t$95$1, If[LessEqual[x, 2100.0], N[(y / t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.39999999999999972e-29 or 2100 < x

   1. Initial program 88.6%

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

   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 56.5

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

   4. Applied rewrites 56.5%

      \[\leadsto \color{blue}{\frac{x}{1 + x}} \]
   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 56.5

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

   6. Applied rewrites 56.5%

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

   if -3.39999999999999972e-29 < x < 2100

   1. Initial program 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 24.8

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

   4. Applied rewrites 24.8%

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

Alternative 15: 67.5% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := 1 - \frac{1}{x}\\ \mathbf{if}\;x \leq -0.13:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;x \leq 2100:\\ \;\;\;\;\frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- 1.0 (/ 1.0 x))))
   (if (<= x -0.13) t_1 (if (<= x 2100.0) (/ 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 <= -0.13) {
		tmp = t_1;
	} else if (x <= 2100.0) {
		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 <= (-0.13d0)) then
        tmp = t_1
    else if (x <= 2100.0d0) 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 <= -0.13) {
		tmp = t_1;
	} else if (x <= 2100.0) {
		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 <= -0.13:
		tmp = t_1
	elif x <= 2100.0:
		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 <= -0.13)
		tmp = t_1;
	elseif (x <= 2100.0)
		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 <= -0.13)
		tmp = t_1;
	elseif (x <= 2100.0)
		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, -0.13], t$95$1, If[LessEqual[x, 2100.0], N[(y / t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if x < -0.13 or 2100 < x

   1. Initial program 88.6%

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

   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 56.5

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

   4. Applied rewrites 56.5%

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

   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 46.2

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

   7. Applied rewrites 46.2%

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

   if -0.13 < x < 2100

   1. Initial program 88.6%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 24.8

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

   4. Applied rewrites 24.8%

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

Alternative 16: 24.8% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ \frac{y}{t} \end{array} \]

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = y / t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 88.6%

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

2. Taylor expanded in x around 0

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

   1. lower-/.f64 24.8

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

4. Applied rewrites 24.8%

   \[\leadsto \color{blue}{\frac{y}{t}} \]
5. Add Preprocessing

Alternative 17: 3.3% accurate, 5.6× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (-1.0d0) / x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 88.6%

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

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 56.5

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

4. Applied rewrites 56.5%

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

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 46.2

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

7. Applied rewrites 46.2%

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

8. Taylor expanded in x around 0

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

   1. lower-/.f64 3.3

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

10. Applied rewrites 3.3%

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

Reproduce

herbie shell --seed 2025150 
(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)))