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

Percentage Accurate: 89.1% → 96.2%

Time: 4.3s

Alternatives: 16

Speedup: 0.3×

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 89.1% accurate, 1.0× speedup

Math

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

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: 96.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := y \cdot z - x\\ t_2 := t \cdot z - x\\ t_3 := \frac{z}{t\_2} \cdot \frac{y}{x - -1}\\ t_4 := \frac{x + \frac{t\_1}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -1 \cdot 10^{+19}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 10^{-15}:\\ \;\;\;\;\frac{x + \frac{t\_1}{\mathsf{fma}\left(z, t, -x\right)}}{1}\\ \mathbf{elif}\;t\_4 \leq 4:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x + 1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (- (* y z) x))
       (t_2 (- (* t z) x))
       (t_3 (* (/ z t_2) (/ y (- x -1.0))))
       (t_4 (/ (+ x (/ t_1 t_2)) (+ x 1.0))))
  (if (<= t_4 -1e+19)
    t_3
    (if (<= t_4 1e-15)
      (/ (+ x (/ t_1 (fma z t (- x)))) 1.0)
      (if (<= t_4 4.0)
        (/ (- x (/ x t_2)) (+ x 1.0))
        (if (<= t_4 INFINITY) t_3 (/ (+ x (/ y t)) (+ x 1.0))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y * z) - x;
	double t_2 = (t * z) - x;
	double t_3 = (z / t_2) * (y / (x - -1.0));
	double t_4 = (x + (t_1 / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1e+19) {
		tmp = t_3;
	} else if (t_4 <= 1e-15) {
		tmp = (x + (t_1 / fma(z, t, -x))) / 1.0;
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y * z) - x)
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(z / t_2) * Float64(y / Float64(x - -1.0)))
	t_4 = Float64(Float64(x + Float64(t_1 / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -1e+19)
		tmp = t_3;
	elseif (t_4 <= 1e-15)
		tmp = Float64(Float64(x + Float64(t_1 / fma(z, t, Float64(-x)))) / 1.0);
	elseif (t_4 <= 4.0)
		tmp = Float64(Float64(x - Float64(x / t_2)) / Float64(x + 1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z / t$95$2), $MachinePrecision] * N[(y / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x + N[(t$95$1 / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, -1e+19], t$95$3, If[LessEqual[t$95$4, 1e-15], N[(N[(x + N[(t$95$1 / N[(z * t + (-x)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 1.0), $MachinePrecision], If[LessEqual[t$95$4, 4.0], N[(N[(x - N[(x / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, Infinity], t$95$3, N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1e19 or 4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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. lift-*.f64 N/A

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

      4. times-frac N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. lower-/.f64 31.8%

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

      12. lift-+.f64 N/A

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

      13. add-flip N/A

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

      14. metadata-eval N/A

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

      15. lower--.f64 31.8%

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

   6. Applied rewrites 31.8%

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

   if -1e19 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.0000000000000001e-15

   1. Initial program 89.1%

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

      1. lift--.f64 N/A

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

      2. sub-flip N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. lower-neg.f64 89.1%

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

   3. Applied rewrites 89.1%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 45.4%

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

   if 1.0000000000000001e-15 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   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 67.6%

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

   4. Applied rewrites 67.6%

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

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

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

Alternative 2: 96.0% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := y \cdot z - x\\ t_2 := t \cdot z - x\\ t_3 := \frac{z}{t\_2} \cdot \frac{y}{x - -1}\\ t_4 := \frac{x + \frac{t\_1}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -1000000000:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 2 \cdot 10^{-23}:\\ \;\;\;\;\frac{x + \frac{t\_1}{t \cdot z}}{x + 1}\\ \mathbf{elif}\;t\_4 \leq 4:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x + 1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (- (* y z) x))
       (t_2 (- (* t z) x))
       (t_3 (* (/ z t_2) (/ y (- x -1.0))))
       (t_4 (/ (+ x (/ t_1 t_2)) (+ x 1.0))))
  (if (<= t_4 -1000000000.0)
    t_3
    (if (<= t_4 2e-23)
      (/ (+ x (/ t_1 (* t z))) (+ x 1.0))
      (if (<= t_4 4.0)
        (/ (- x (/ x t_2)) (+ x 1.0))
        (if (<= t_4 INFINITY) t_3 (/ (+ x (/ y t)) (+ x 1.0))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y * z) - x;
	double t_2 = (t * z) - x;
	double t_3 = (z / t_2) * (y / (x - -1.0));
	double t_4 = (x + (t_1 / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1000000000.0) {
		tmp = t_3;
	} else if (t_4 <= 2e-23) {
		tmp = (x + (t_1 / (t * z))) / (x + 1.0);
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (y * z) - x;
	double t_2 = (t * z) - x;
	double t_3 = (z / t_2) * (y / (x - -1.0));
	double t_4 = (x + (t_1 / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1000000000.0) {
		tmp = t_3;
	} else if (t_4 <= 2e-23) {
		tmp = (x + (t_1 / (t * z))) / (x + 1.0);
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (y * z) - x
	t_2 = (t * z) - x
	t_3 = (z / t_2) * (y / (x - -1.0))
	t_4 = (x + (t_1 / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -1000000000.0:
		tmp = t_3
	elif t_4 <= 2e-23:
		tmp = (x + (t_1 / (t * z))) / (x + 1.0)
	elif t_4 <= 4.0:
		tmp = (x - (x / t_2)) / (x + 1.0)
	elif t_4 <= math.inf:
		tmp = t_3
	else:
		tmp = (x + (y / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y * z) - x)
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(z / t_2) * Float64(y / Float64(x - -1.0)))
	t_4 = Float64(Float64(x + Float64(t_1 / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -1000000000.0)
		tmp = t_3;
	elseif (t_4 <= 2e-23)
		tmp = Float64(Float64(x + Float64(t_1 / Float64(t * z))) / Float64(x + 1.0));
	elseif (t_4 <= 4.0)
		tmp = Float64(Float64(x - Float64(x / t_2)) / Float64(x + 1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (y * z) - x;
	t_2 = (t * z) - x;
	t_3 = (z / t_2) * (y / (x - -1.0));
	t_4 = (x + (t_1 / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -1000000000.0)
		tmp = t_3;
	elseif (t_4 <= 2e-23)
		tmp = (x + (t_1 / (t * z))) / (x + 1.0);
	elseif (t_4 <= 4.0)
		tmp = (x - (x / t_2)) / (x + 1.0);
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = (x + (y / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z / t$95$2), $MachinePrecision] * N[(y / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x + N[(t$95$1 / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, -1000000000.0], t$95$3, If[LessEqual[t$95$4, 2e-23], N[(N[(x + N[(t$95$1 / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, 4.0], N[(N[(x - N[(x / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, Infinity], t$95$3, N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1e9 or 4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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. lift-*.f64 N/A

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

      4. times-frac N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. lower-/.f64 31.8%

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

      12. lift-+.f64 N/A

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

      13. add-flip N/A

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

      14. metadata-eval N/A

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

      15. lower--.f64 31.8%

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

   6. Applied rewrites 31.8%

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

   if -1e9 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.9999999999999999e-23

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 51.0%

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

   4. Applied rewrites 51.0%

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

   if 1.9999999999999999e-23 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   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 67.6%

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

   4. Applied rewrites 67.6%

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

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

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

Alternative 3: 95.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := y \cdot z - x\\ t_2 := t \cdot z - x\\ t_3 := \frac{z}{t\_2} \cdot \frac{y}{x - -1}\\ t_4 := \frac{x + \frac{t\_1}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -1000000000:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 2 \cdot 10^{-23}:\\ \;\;\;\;\frac{x + \frac{t\_1}{t \cdot z}}{1}\\ \mathbf{elif}\;t\_4 \leq 4:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x + 1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (- (* y z) x))
       (t_2 (- (* t z) x))
       (t_3 (* (/ z t_2) (/ y (- x -1.0))))
       (t_4 (/ (+ x (/ t_1 t_2)) (+ x 1.0))))
  (if (<= t_4 -1000000000.0)
    t_3
    (if (<= t_4 2e-23)
      (/ (+ x (/ t_1 (* t z))) 1.0)
      (if (<= t_4 4.0)
        (/ (- x (/ x t_2)) (+ x 1.0))
        (if (<= t_4 INFINITY) t_3 (/ (+ x (/ y t)) (+ x 1.0))))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y * z) - x;
	double t_2 = (t * z) - x;
	double t_3 = (z / t_2) * (y / (x - -1.0));
	double t_4 = (x + (t_1 / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1000000000.0) {
		tmp = t_3;
	} else if (t_4 <= 2e-23) {
		tmp = (x + (t_1 / (t * z))) / 1.0;
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (y * z) - x;
	double t_2 = (t * z) - x;
	double t_3 = (z / t_2) * (y / (x - -1.0));
	double t_4 = (x + (t_1 / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -1000000000.0) {
		tmp = t_3;
	} else if (t_4 <= 2e-23) {
		tmp = (x + (t_1 / (t * z))) / 1.0;
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (y * z) - x
	t_2 = (t * z) - x
	t_3 = (z / t_2) * (y / (x - -1.0))
	t_4 = (x + (t_1 / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -1000000000.0:
		tmp = t_3
	elif t_4 <= 2e-23:
		tmp = (x + (t_1 / (t * z))) / 1.0
	elif t_4 <= 4.0:
		tmp = (x - (x / t_2)) / (x + 1.0)
	elif t_4 <= math.inf:
		tmp = t_3
	else:
		tmp = (x + (y / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y * z) - x)
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(z / t_2) * Float64(y / Float64(x - -1.0)))
	t_4 = Float64(Float64(x + Float64(t_1 / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -1000000000.0)
		tmp = t_3;
	elseif (t_4 <= 2e-23)
		tmp = Float64(Float64(x + Float64(t_1 / Float64(t * z))) / 1.0);
	elseif (t_4 <= 4.0)
		tmp = Float64(Float64(x - Float64(x / t_2)) / Float64(x + 1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (y * z) - x;
	t_2 = (t * z) - x;
	t_3 = (z / t_2) * (y / (x - -1.0));
	t_4 = (x + (t_1 / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -1000000000.0)
		tmp = t_3;
	elseif (t_4 <= 2e-23)
		tmp = (x + (t_1 / (t * z))) / 1.0;
	elseif (t_4 <= 4.0)
		tmp = (x - (x / t_2)) / (x + 1.0);
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = (x + (y / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z / t$95$2), $MachinePrecision] * N[(y / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x + N[(t$95$1 / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, -1000000000.0], t$95$3, If[LessEqual[t$95$4, 2e-23], N[(N[(x + N[(t$95$1 / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 1.0), $MachinePrecision], If[LessEqual[t$95$4, 4.0], N[(N[(x - N[(x / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$4, Infinity], t$95$3, N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -1e9 or 4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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. lift-*.f64 N/A

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

      4. times-frac N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. lower-/.f64 31.8%

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

      12. lift-+.f64 N/A

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

      13. add-flip N/A

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

      14. metadata-eval N/A

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

      15. lower--.f64 31.8%

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

   6. Applied rewrites 31.8%

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

   if -1e9 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.9999999999999999e-23

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-*.f64 51.0%

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

   4. Applied rewrites 51.0%

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

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 31.7%

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

   if 1.9999999999999999e-23 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   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 67.6%

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

   4. Applied rewrites 67.6%

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

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

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

Alternative 4: 95.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} t_1 := y \cdot z - x\\ t_2 := t \cdot z - x\\ t_3 := \frac{x + \frac{t\_1}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_3 \leq 5 \cdot 10^{+142}:\\ \;\;\;\;\frac{x + \frac{t\_1}{\mathsf{fma}\left(z, t, -x\right)}}{x + 1}\\ \mathbf{elif}\;t\_3 \leq \infty:\\ \;\;\;\;\frac{z}{t\_2} \cdot \frac{y}{x - -1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (- (* y z) x))
       (t_2 (- (* t z) x))
       (t_3 (/ (+ x (/ t_1 t_2)) (+ x 1.0))))
  (if (<= t_3 5e+142)
    (/ (+ x (/ t_1 (fma z t (- x)))) (+ x 1.0))
    (if (<= t_3 INFINITY)
      (* (/ z t_2) (/ y (- x -1.0)))
      (/ (+ x (/ y t)) (+ x 1.0))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (y * z) - x;
	double t_2 = (t * z) - x;
	double t_3 = (x + (t_1 / t_2)) / (x + 1.0);
	double tmp;
	if (t_3 <= 5e+142) {
		tmp = (x + (t_1 / fma(z, t, -x))) / (x + 1.0);
	} else if (t_3 <= ((double) INFINITY)) {
		tmp = (z / t_2) * (y / (x - -1.0));
	} else {
		tmp = (x + (y / t)) / (x + 1.0);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(y * z) - x)
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(x + Float64(t_1 / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_3 <= 5e+142)
		tmp = Float64(Float64(x + Float64(t_1 / fma(z, t, Float64(-x)))) / Float64(x + 1.0));
	elseif (t_3 <= Inf)
		tmp = Float64(Float64(z / t_2) * Float64(y / Float64(x - -1.0)));
	else
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x + N[(t$95$1 / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$3, 5e+142], N[(N[(x + N[(t$95$1 / N[(z * t + (-x)), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$3, Infinity], N[(N[(z / t$95$2), $MachinePrecision] * N[(y / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 5.0000000000000001e142

   1. Initial program 89.1%

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

      1. lift--.f64 N/A

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

      2. sub-flip N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 N/A

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

      6. lower-neg.f64 89.1%

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

   3. Applied rewrites 89.1%

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

   if 5.0000000000000001e142 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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. lift-*.f64 N/A

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

      4. times-frac N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. lower-/.f64 31.8%

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

      12. lift-+.f64 N/A

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

      13. add-flip N/A

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

      14. metadata-eval N/A

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

      15. lower--.f64 31.8%

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

   6. Applied rewrites 31.8%

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

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

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

Alternative 5: 95.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} t_1 := t \cdot z - x\\ t_2 := \frac{x + \frac{y \cdot z - x}{t\_1}}{x + 1}\\ \mathbf{if}\;t\_2 \leq 5 \cdot 10^{+142}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_2 \leq \infty:\\ \;\;\;\;\frac{z}{t\_1} \cdot \frac{y}{x - -1}\\ \mathbf{else}:\\ \;\;\;\;\frac{x + \frac{y}{t}}{x + 1}\\ \end{array} \]

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 5e+142)
    t_2
    (if (<= t_2 INFINITY)
      (* (/ z t_1) (/ y (- x -1.0)))
      (/ (+ x (/ y t)) (+ x 1.0))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (t * z) - x;
	double t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0);
	double tmp;
	if (t_2 <= 5e+142) {
		tmp = t_2;
	} else if (t_2 <= ((double) INFINITY)) {
		tmp = (z / t_1) * (y / (x - -1.0));
	} else {
		tmp = (x + (y / t)) / (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)) / (x + 1.0);
	double tmp;
	if (t_2 <= 5e+142) {
		tmp = t_2;
	} else if (t_2 <= Double.POSITIVE_INFINITY) {
		tmp = (z / t_1) * (y / (x - -1.0));
	} else {
		tmp = (x + (y / t)) / (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 <= 5e+142:
		tmp = t_2
	elif t_2 <= math.inf:
		tmp = (z / t_1) * (y / (x - -1.0))
	else:
		tmp = (x + (y / t)) / (x + 1.0)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(t * z) - x)
	t_2 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_1)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_2 <= 5e+142)
		tmp = t_2;
	elseif (t_2 <= Inf)
		tmp = Float64(Float64(z / t_1) * Float64(y / Float64(x - -1.0)));
	else
		tmp = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (t * z) - x;
	t_2 = (x + (((y * z) - x) / t_1)) / (x + 1.0);
	tmp = 0.0;
	if (t_2 <= 5e+142)
		tmp = t_2;
	elseif (t_2 <= Inf)
		tmp = (z / t_1) * (y / (x - -1.0));
	else
		tmp = (x + (y / t)) / (x + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(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, 5e+142], t$95$2, If[LessEqual[t$95$2, Infinity], N[(N[(z / t$95$1), $MachinePrecision] * N[(y / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 5.0000000000000001e142

   1. Initial program 89.1%

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

   if 5.0000000000000001e142 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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. lift-*.f64 N/A

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

      4. times-frac N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. lower-/.f64 31.8%

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

      12. lift-+.f64 N/A

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

      13. add-flip N/A

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

      14. metadata-eval N/A

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

      15. lower--.f64 31.8%

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

   6. Applied rewrites 31.8%

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

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

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

Alternative 6: 94.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := t \cdot z - x\\ t_3 := \frac{z}{t\_2} \cdot \frac{y}{x - -1}\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -0.1:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 2 \cdot 10^{-23}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 4:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x + 1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
       (t_2 (- (* t z) x))
       (t_3 (* (/ z t_2) (/ y (- x -1.0))))
       (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
  (if (<= t_4 -0.1)
    t_3
    (if (<= t_4 2e-23)
      t_1
      (if (<= t_4 4.0)
        (/ (- x (/ x t_2)) (+ x 1.0))
        (if (<= t_4 INFINITY) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / t_2) * (y / (x - -1.0));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -0.1) {
		tmp = t_3;
	} else if (t_4 <= 2e-23) {
		tmp = t_1;
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / t_2) * (y / (x - -1.0));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -0.1) {
		tmp = t_3;
	} else if (t_4 <= 2e-23) {
		tmp = t_1;
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (t * z) - x
	t_3 = (z / t_2) * (y / (x - -1.0))
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -0.1:
		tmp = t_3
	elif t_4 <= 2e-23:
		tmp = t_1
	elif t_4 <= 4.0:
		tmp = (x - (x / t_2)) / (x + 1.0)
	elif t_4 <= math.inf:
		tmp = t_3
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(z / t_2) * Float64(y / Float64(x - -1.0)))
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -0.1)
		tmp = t_3;
	elseif (t_4 <= 2e-23)
		tmp = t_1;
	elseif (t_4 <= 4.0)
		tmp = Float64(Float64(x - Float64(x / t_2)) / Float64(x + 1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (t * z) - x;
	t_3 = (z / t_2) * (y / (x - -1.0));
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -0.1)
		tmp = t_3;
	elseif (t_4 <= 2e-23)
		tmp = t_1;
	elseif (t_4 <= 4.0)
		tmp = (x - (x / t_2)) / (x + 1.0);
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z / t$95$2), $MachinePrecision] * N[(y / N[(x - -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / t$95$2), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$4, -0.1], t$95$3, If[LessEqual[t$95$4, 2e-23], t$95$1, If[LessEqual[t$95$4, 4.0], 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))) < -0.10000000000000001 or 4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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. lift-*.f64 N/A

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

      4. times-frac N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. lower-/.f64 31.8%

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

      12. lift-+.f64 N/A

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

      13. add-flip N/A

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

      14. metadata-eval N/A

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

      15. lower--.f64 31.8%

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

   6. Applied rewrites 31.8%

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

   if -0.10000000000000001 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.9999999999999999e-23 or +inf.0 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

   if 1.9999999999999999e-23 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   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 67.6%

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

   4. Applied rewrites 67.6%

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

Alternative 7: 93.4% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := t \cdot z - x\\ t_3 := \frac{z}{t\_2 \cdot \left(x - -1\right)} \cdot y\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -0.1:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 2 \cdot 10^{-23}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 4:\\ \;\;\;\;\frac{x - \frac{x}{t\_2}}{x + 1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
       (t_2 (- (* t z) x))
       (t_3 (* (/ z (* t_2 (- x -1.0))) y))
       (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
  (if (<= t_4 -0.1)
    t_3
    (if (<= t_4 2e-23)
      t_1
      (if (<= t_4 4.0)
        (/ (- x (/ x t_2)) (+ x 1.0))
        (if (<= t_4 INFINITY) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / (t_2 * (x - -1.0))) * y;
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -0.1) {
		tmp = t_3;
	} else if (t_4 <= 2e-23) {
		tmp = t_1;
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / (t_2 * (x - -1.0))) * y;
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -0.1) {
		tmp = t_3;
	} else if (t_4 <= 2e-23) {
		tmp = t_1;
	} else if (t_4 <= 4.0) {
		tmp = (x - (x / t_2)) / (x + 1.0);
	} else if (t_4 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (t * z) - x
	t_3 = (z / (t_2 * (x - -1.0))) * y
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -0.1:
		tmp = t_3
	elif t_4 <= 2e-23:
		tmp = t_1
	elif t_4 <= 4.0:
		tmp = (x - (x / t_2)) / (x + 1.0)
	elif t_4 <= math.inf:
		tmp = t_3
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(z / Float64(t_2 * Float64(x - -1.0))) * y)
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -0.1)
		tmp = t_3;
	elseif (t_4 <= 2e-23)
		tmp = t_1;
	elseif (t_4 <= 4.0)
		tmp = Float64(Float64(x - Float64(x / t_2)) / Float64(x + 1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (t * z) - x;
	t_3 = (z / (t_2 * (x - -1.0))) * y;
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -0.1)
		tmp = t_3;
	elseif (t_4 <= 2e-23)
		tmp = t_1;
	elseif (t_4 <= 4.0)
		tmp = (x - (x / t_2)) / (x + 1.0);
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z / N[(t$95$2 * N[(x - -1.0), $MachinePrecision]), $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, -0.1], t$95$3, If[LessEqual[t$95$4, 2e-23], t$95$1, If[LessEqual[t$95$4, 4.0], 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))) < -0.10000000000000001 or 4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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 31.1%

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

      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

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

      9. lift-+.f64 N/A

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

      10. +-commutative N/A

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

      11. lift-+.f64 N/A

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

      12. lower-*.f64 31.1%

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

      13. lift-+.f64 N/A

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

      14. add-flip N/A

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

      15. metadata-eval N/A

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

      16. lower--.f64 31.1%

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

   6. Applied rewrites 31.1%

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

   if -0.10000000000000001 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.9999999999999999e-23 or +inf.0 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

   if 1.9999999999999999e-23 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   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 67.6%

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

   4. Applied rewrites 67.6%

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

Alternative 8: 92.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := t \cdot z - x\\ t_3 := \frac{z}{t\_2 \cdot \left(x - -1\right)} \cdot y\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -0.1:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 4 \cdot 10^{-13}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 4:\\ \;\;\;\;\frac{x + -1 \cdot -1}{x + 1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
       (t_2 (- (* t z) x))
       (t_3 (* (/ z (* t_2 (- x -1.0))) y))
       (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
  (if (<= t_4 -0.1)
    t_3
    (if (<= t_4 4e-13)
      t_1
      (if (<= t_4 4.0)
        (/ (+ x (* -1.0 -1.0)) (+ x 1.0))
        (if (<= t_4 INFINITY) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / (t_2 * (x - -1.0))) * y;
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -0.1) {
		tmp = t_3;
	} else if (t_4 <= 4e-13) {
		tmp = t_1;
	} else if (t_4 <= 4.0) {
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = (z / (t_2 * (x - -1.0))) * y;
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -0.1) {
		tmp = t_3;
	} else if (t_4 <= 4e-13) {
		tmp = t_1;
	} else if (t_4 <= 4.0) {
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	} else if (t_4 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (t * z) - x
	t_3 = (z / (t_2 * (x - -1.0))) * y
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -0.1:
		tmp = t_3
	elif t_4 <= 4e-13:
		tmp = t_1
	elif t_4 <= 4.0:
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0)
	elif t_4 <= math.inf:
		tmp = t_3
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(Float64(z / Float64(t_2 * Float64(x - -1.0))) * y)
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -0.1)
		tmp = t_3;
	elseif (t_4 <= 4e-13)
		tmp = t_1;
	elseif (t_4 <= 4.0)
		tmp = Float64(Float64(x + Float64(-1.0 * -1.0)) / Float64(x + 1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (t * z) - x;
	t_3 = (z / (t_2 * (x - -1.0))) * y;
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -0.1)
		tmp = t_3;
	elseif (t_4 <= 4e-13)
		tmp = t_1;
	elseif (t_4 <= 4.0)
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(N[(z / N[(t$95$2 * N[(x - -1.0), $MachinePrecision]), $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, -0.1], t$95$3, If[LessEqual[t$95$4, 4e-13], t$95$1, If[LessEqual[t$95$4, 4.0], N[(N[(x + N[(-1.0 * -1.0), $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))) < -0.10000000000000001 or 4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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 31.1%

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

      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

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

      9. lift-+.f64 N/A

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

      10. +-commutative N/A

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

      11. lift-+.f64 N/A

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

      12. lower-*.f64 31.1%

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

      13. lift-+.f64 N/A

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

      14. add-flip N/A

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

      15. metadata-eval N/A

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

      16. lower--.f64 31.1%

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

   6. Applied rewrites 31.1%

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

   if -0.10000000000000001 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4.0000000000000001e-13 or +inf.0 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

   if 4.0000000000000001e-13 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   2. Taylor expanded in t around 0

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 58.3%

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

   4. Applied rewrites 58.3%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 54.2%

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

Alternative 9: 89.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := t \cdot z - x\\ t_3 := z \cdot \frac{y}{t\_2 \cdot \left(x - -1\right)}\\ t_4 := \frac{x + \frac{y \cdot z - x}{t\_2}}{x + 1}\\ \mathbf{if}\;t\_4 \leq -0.1:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_4 \leq 4 \cdot 10^{-13}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_4 \leq 4:\\ \;\;\;\;\frac{x + -1 \cdot -1}{x + 1}\\ \mathbf{elif}\;t\_4 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
       (t_2 (- (* t z) x))
       (t_3 (* z (/ y (* t_2 (- x -1.0)))))
       (t_4 (/ (+ x (/ (- (* y z) x) t_2)) (+ x 1.0))))
  (if (<= t_4 -0.1)
    t_3
    (if (<= t_4 4e-13)
      t_1
      (if (<= t_4 4.0)
        (/ (+ x (* -1.0 -1.0)) (+ x 1.0))
        (if (<= t_4 INFINITY) t_3 t_1))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = z * (y / (t_2 * (x - -1.0)));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -0.1) {
		tmp = t_3;
	} else if (t_4 <= 4e-13) {
		tmp = t_1;
	} else if (t_4 <= 4.0) {
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	} else if (t_4 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (t * z) - x;
	double t_3 = z * (y / (t_2 * (x - -1.0)));
	double t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	double tmp;
	if (t_4 <= -0.1) {
		tmp = t_3;
	} else if (t_4 <= 4e-13) {
		tmp = t_1;
	} else if (t_4 <= 4.0) {
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	} else if (t_4 <= Double.POSITIVE_INFINITY) {
		tmp = t_3;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (t * z) - x
	t_3 = z * (y / (t_2 * (x - -1.0)))
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0)
	tmp = 0
	if t_4 <= -0.1:
		tmp = t_3
	elif t_4 <= 4e-13:
		tmp = t_1
	elif t_4 <= 4.0:
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0)
	elif t_4 <= math.inf:
		tmp = t_3
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(t * z) - x)
	t_3 = Float64(z * Float64(y / Float64(t_2 * Float64(x - -1.0))))
	t_4 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / t_2)) / Float64(x + 1.0))
	tmp = 0.0
	if (t_4 <= -0.1)
		tmp = t_3;
	elseif (t_4 <= 4e-13)
		tmp = t_1;
	elseif (t_4 <= 4.0)
		tmp = Float64(Float64(x + Float64(-1.0 * -1.0)) / Float64(x + 1.0));
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (t * z) - x;
	t_3 = z * (y / (t_2 * (x - -1.0)));
	t_4 = (x + (((y * z) - x) / t_2)) / (x + 1.0);
	tmp = 0.0;
	if (t_4 <= -0.1)
		tmp = t_3;
	elseif (t_4 <= 4e-13)
		tmp = t_1;
	elseif (t_4 <= 4.0)
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	elseif (t_4 <= Inf)
		tmp = t_3;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]}, Block[{t$95$3 = N[(z * N[(y / N[(t$95$2 * N[(x - -1.0), $MachinePrecision]), $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, -0.1], t$95$3, If[LessEqual[t$95$4, 4e-13], t$95$1, If[LessEqual[t$95$4, 4.0], N[(N[(x + N[(-1.0 * -1.0), $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))) < -0.10000000000000001 or 4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < +inf.0

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

      \[\leadsto \color{blue}{\frac{y \cdot z}{\left(1 + x\right) \cdot \left(t \cdot z - x\right)}} \]
   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 27.9%

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

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lift-+.f64 N/A

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

      10. +-commutative N/A

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

      11. lift-+.f64 N/A

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

      12. lower-*.f64 27.9%

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

      13. lift-+.f64 N/A

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

      14. add-flip N/A

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

      15. metadata-eval N/A

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

      16. lower--.f64 27.9%

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

   6. Applied rewrites 27.9%

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

   if -0.10000000000000001 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4.0000000000000001e-13 or +inf.0 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

   if 4.0000000000000001e-13 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   2. Taylor expanded in t around 0

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 58.3%

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

   4. Applied rewrites 58.3%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 54.2%

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

Alternative 10: 85.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} t_1 := \frac{x + \frac{y}{t}}{x + 1}\\ t_2 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_2 \leq 4 \cdot 10^{-13}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 1.0000000000002:\\ \;\;\;\;\frac{x + -1 \cdot -1}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (/ (+ x (/ y t)) (+ x 1.0)))
       (t_2 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
  (if (<= t_2 4e-13)
    t_1
    (if (<= t_2 1.0000000000002)
      (/ (+ x (* -1.0 -1.0)) (+ x 1.0))
      t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= 4e-13) {
		tmp = t_1;
	} else if (t_2 <= 1.0000000000002) {
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x + (y / t)) / (x + 1.0d0)
    t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_2 <= 4d-13) then
        tmp = t_1
    else if (t_2 <= 1.0000000000002d0) then
        tmp = (x + ((-1.0d0) * (-1.0d0))) / (x + 1.0d0)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + (y / t)) / (x + 1.0);
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= 4e-13) {
		tmp = t_1;
	} else if (t_2 <= 1.0000000000002) {
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (y / t)) / (x + 1.0)
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_2 <= 4e-13:
		tmp = t_1
	elif t_2 <= 1.0000000000002:
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(y / t)) / Float64(x + 1.0))
	t_2 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_2 <= 4e-13)
		tmp = t_1;
	elseif (t_2 <= 1.0000000000002)
		tmp = Float64(Float64(x + Float64(-1.0 * -1.0)) / Float64(x + 1.0));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (y / t)) / (x + 1.0);
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_2 <= 4e-13)
		tmp = t_1;
	elseif (t_2 <= 1.0000000000002)
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(y / t), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, 4e-13], t$95$1, If[LessEqual[t$95$2, 1.0000000000002], N[(N[(x + N[(-1.0 * -1.0), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4.0000000000000001e-13 or 1.0000000000002001 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

   if 4.0000000000000001e-13 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 1.0000000000002001

   1. Initial program 89.1%

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

   2. Taylor expanded in t around 0

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 58.3%

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

   4. Applied rewrites 58.3%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 54.2%

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

Alternative 11: 81.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} t_1 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_1 \leq 4 \cdot 10^{-13}:\\ \;\;\;\;1 \cdot \left(\frac{y}{t} + x\right)\\ \mathbf{elif}\;t\_1 \leq 4:\\ \;\;\;\;\frac{x + -1 \cdot -1}{x + 1}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{t \cdot \left(1 + x\right)}\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
  (if (<= t_1 4e-13)
    (* 1.0 (+ (/ y t) x))
    (if (<= t_1 4.0)
      (/ (+ x (* -1.0 -1.0)) (+ x 1.0))
      (/ y (* t (+ 1.0 x)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= 4e-13) {
		tmp = 1.0 * ((y / t) + x);
	} else if (t_1 <= 4.0) {
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	} else {
		tmp = y / (t * (1.0 + x));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_1 <= 4d-13) then
        tmp = 1.0d0 * ((y / t) + x)
    else if (t_1 <= 4.0d0) then
        tmp = (x + ((-1.0d0) * (-1.0d0))) / (x + 1.0d0)
    else
        tmp = y / (t * (1.0d0 + x))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_1 <= 4e-13) {
		tmp = 1.0 * ((y / t) + x);
	} else if (t_1 <= 4.0) {
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	} else {
		tmp = y / (t * (1.0 + x));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_1 <= 4e-13:
		tmp = 1.0 * ((y / t) + x)
	elif t_1 <= 4.0:
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0)
	else:
		tmp = y / (t * (1.0 + x))
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_1 <= 4e-13)
		tmp = Float64(1.0 * Float64(Float64(y / t) + x));
	elseif (t_1 <= 4.0)
		tmp = Float64(Float64(x + Float64(-1.0 * -1.0)) / Float64(x + 1.0));
	else
		tmp = Float64(y / Float64(t * Float64(1.0 + x)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_1 <= 4e-13)
		tmp = 1.0 * ((y / t) + x);
	elseif (t_1 <= 4.0)
		tmp = (x + (-1.0 * -1.0)) / (x + 1.0);
	else
		tmp = y / (t * (1.0 + x));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 4e-13], N[(1.0 * N[(N[(y / t), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 4.0], N[(N[(x + N[(-1.0 * -1.0), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], N[(y / N[(t * N[(1.0 + x), $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))) < 4.0000000000000001e-13

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

      1. lift-/.f64 N/A

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

      2. mult-flip N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. frac-2neg N/A

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

      6. metadata-eval N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. add-flip N/A

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

      10. metadata-eval N/A

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

      11. sub-negate N/A

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

      12. lower--.f64 70.4%

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 70.4%

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

   6. Applied rewrites 70.4%

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

   7. Taylor expanded in x around 0

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

   9. Applied rewrites 33.8%

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

   if 4.0000000000000001e-13 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   2. Taylor expanded in t around 0

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

      1. lower-*.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-*.f64 58.3%

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

   4. Applied rewrites 58.3%

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

   5. Taylor expanded in x around inf

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

   7. Applied rewrites 54.2%

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

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

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

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

   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 26.0%

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

   7. Applied rewrites 26.0%

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

Alternative 12: 77.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-12}:\\ \;\;\;\;\frac{x}{1 + x}\\ \mathbf{elif}\;x \leq 0.75:\\ \;\;\;\;1 \cdot \left(\frac{y}{t} + x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{x - 1}{x}\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (if (<= x -7.2e-12)
  (/ x (+ 1.0 x))
  (if (<= x 0.75) (* 1.0 (+ (/ y t) x)) (/ (- x 1.0) x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -7.2e-12) {
		tmp = x / (1.0 + x);
	} else if (x <= 0.75) {
		tmp = 1.0 * ((y / t) + x);
	} else {
		tmp = (x - 1.0) / x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if (x <= (-7.2d-12)) then
        tmp = x / (1.0d0 + x)
    else if (x <= 0.75d0) then
        tmp = 1.0d0 * ((y / t) + x)
    else
        tmp = (x - 1.0d0) / x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (x <= -7.2e-12) {
		tmp = x / (1.0 + x);
	} else if (x <= 0.75) {
		tmp = 1.0 * ((y / t) + x);
	} else {
		tmp = (x - 1.0) / x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if x <= -7.2e-12:
		tmp = x / (1.0 + x)
	elif x <= 0.75:
		tmp = 1.0 * ((y / t) + x)
	else:
		tmp = (x - 1.0) / x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (x <= -7.2e-12)
		tmp = Float64(x / Float64(1.0 + x));
	elseif (x <= 0.75)
		tmp = Float64(1.0 * Float64(Float64(y / t) + x));
	else
		tmp = Float64(Float64(x - 1.0) / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (x <= -7.2e-12)
		tmp = x / (1.0 + x);
	elseif (x <= 0.75)
		tmp = 1.0 * ((y / t) + x);
	else
		tmp = (x - 1.0) / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[x, -7.2e-12], N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 0.75], N[(1.0 * N[(N[(y / t), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], N[(N[(x - 1.0), $MachinePrecision] / x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.2e-12

   1. Initial program 89.1%

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

   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 56.6%

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

   4. Applied rewrites 56.6%

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

   if -7.2e-12 < x < 0.75

   1. Initial program 89.1%

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

   2. Taylor expanded in x around 0

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

      1. lower-/.f64 70.5%

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

   4. Applied rewrites 70.5%

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

      1. lift-/.f64 N/A

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

      2. mult-flip N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. frac-2neg N/A

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

      6. metadata-eval N/A

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

      7. lower-/.f64 N/A

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

      8. lift-+.f64 N/A

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

      9. add-flip N/A

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

      10. metadata-eval N/A

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

      11. sub-negate N/A

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

      12. lower--.f64 70.4%

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 70.4%

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

   6. Applied rewrites 70.4%

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

   7. Taylor expanded in x around 0

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

   9. Applied rewrites 33.8%

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

   if 0.75 < x

   1. Initial program 89.1%

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

   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 56.6%

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

   4. Applied rewrites 56.6%

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

   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. Step-by-step derivation

      1. lift--.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. sub-to-fraction N/A

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

      4. lower-/.f64 N/A

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

      5. *-lft-identity N/A

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

      6. lower--.f64 46.2%

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

   9. Applied rewrites 46.2%

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

Alternative 13: 69.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} t_1 := \frac{y}{t \cdot \left(1 + x\right)}\\ t_2 := \frac{x + \frac{y \cdot z - x}{t \cdot z - x}}{x + 1}\\ \mathbf{if}\;t\_2 \leq -0.1:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 4:\\ \;\;\;\;\frac{x}{1 + x}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t)
  :precision binary64
  (let* ((t_1 (/ y (* t (+ 1.0 x))))
       (t_2 (/ (+ x (/ (- (* y z) x) (- (* t z) x))) (+ x 1.0))))
  (if (<= t_2 -0.1) t_1 (if (<= t_2 4.0) (/ x (+ 1.0 x)) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = y / (t * (1.0 + x));
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= -0.1) {
		tmp = t_1;
	} else if (t_2 <= 4.0) {
		tmp = 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) :: tmp
    t_1 = y / (t * (1.0d0 + x))
    t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0d0)
    if (t_2 <= (-0.1d0)) then
        tmp = t_1
    else if (t_2 <= 4.0d0) then
        tmp = 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 * (1.0 + x));
	double t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	double tmp;
	if (t_2 <= -0.1) {
		tmp = t_1;
	} else if (t_2 <= 4.0) {
		tmp = x / (1.0 + x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = y / (t * (1.0 + x))
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0)
	tmp = 0
	if t_2 <= -0.1:
		tmp = t_1
	elif t_2 <= 4.0:
		tmp = x / (1.0 + x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(y / Float64(t * Float64(1.0 + x)))
	t_2 = Float64(Float64(x + Float64(Float64(Float64(y * z) - x) / Float64(Float64(t * z) - x))) / Float64(x + 1.0))
	tmp = 0.0
	if (t_2 <= -0.1)
		tmp = t_1;
	elseif (t_2 <= 4.0)
		tmp = Float64(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 * (1.0 + x));
	t_2 = (x + (((y * z) - x) / ((t * z) - x))) / (x + 1.0);
	tmp = 0.0;
	if (t_2 <= -0.1)
		tmp = t_1;
	elseif (t_2 <= 4.0)
		tmp = x / (1.0 + x);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(y / N[(t * N[(1.0 + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + N[(N[(N[(y * z), $MachinePrecision] - x), $MachinePrecision] / N[(N[(t * z), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(x + 1.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -0.1], t$95$1, If[LessEqual[t$95$2, 4.0], N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < -0.10000000000000001 or 4 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64)))

   1. Initial program 89.1%

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

   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 27.8%

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

   4. Applied rewrites 27.8%

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

   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 26.0%

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

   7. Applied rewrites 26.0%

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

   if -0.10000000000000001 < (/.f64 (+.f64 x (/.f64 (-.f64 (*.f64 y z) x) (-.f64 (*.f64 t z) x))) (+.f64 x #s(literal 1 binary64))) < 4

   1. Initial program 89.1%

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

   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 56.6%

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

   4. Applied rewrites 56.6%

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

Alternative 14: 56.6% accurate, 3.4× speedup

Math

\[\frac{x}{1 + x} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 89.1%

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

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 56.6%

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

4. Applied rewrites 56.6%

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

Alternative 15: 46.2% accurate, 3.4× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 89.1%

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

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 56.6%

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

4. Applied rewrites 56.6%

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

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. Step-by-step derivation

   1. lift--.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. sub-to-fraction N/A

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

   4. lower-/.f64 N/A

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

   5. *-lft-identity N/A

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

   6. lower--.f64 46.2%

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

9. Applied rewrites 46.2%

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

Alternative 16: 3.2% accurate, 5.2× speedup

Math

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

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 89.1%

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

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 56.6%

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

4. Applied rewrites 56.6%

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

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 3.2%

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

10. Applied rewrites 3.2%

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

Reproduce

herbie shell --seed 2025212 
(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)))