Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, H

Percentage Accurate: 95.7% → 97.9%

Time: 6.4s

Alternatives: 12

Speedup: 1.0×

• 29.0% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- x (/ y (* z 3.0))) (/ t (* (* z 3.0) y))))

C

double code(double x, double y, double z, double t) {
	return (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x - (y / (z * 3.0d0))) + (t / ((z * 3.0d0) * y))
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y));
}

Python

def code(x, y, z, t):
	return (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y))

Julia

function code(x, y, z, t)
	return Float64(Float64(x - Float64(y / Float64(z * 3.0))) + Float64(t / Float64(Float64(z * 3.0) * y)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x - N[(y / N[(z * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t / N[(N[(z * 3.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 95.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- x (/ y (* z 3.0))) (/ t (* (* z 3.0) y))))

C

double code(double x, double y, double z, double t) {
	return (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x - (y / (z * 3.0d0))) + (t / ((z * 3.0d0) * y))
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y));
}

Python

def code(x, y, z, t):
	return (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y))

Julia

function code(x, y, z, t)
	return Float64(Float64(x - Float64(y / Float64(z * 3.0))) + Float64(t / Float64(Float64(z * 3.0) * y)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x - N[(y / N[(z * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t / N[(N[(z * 3.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 97.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.6 \cdot 10^{-63}:\\ \;\;\;\;x + \frac{\mathsf{fma}\left(\frac{0.3333333333333333}{y \cdot y}, t, -0.3333333333333333\right)}{z} \cdot y\\ \mathbf{elif}\;y \leq 1.32 \cdot 10^{-120}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z}, \frac{0.3333333333333333}{y}, x\right)\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\frac{y - \frac{t}{y}}{3}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.6e-63)
   (+ x (* (/ (fma (/ 0.3333333333333333 (* y y)) t -0.3333333333333333) z) y))
   (if (<= y 1.32e-120)
     (fma (/ t z) (/ 0.3333333333333333 y) x)
     (- x (/ (/ (- y (/ t y)) 3.0) z)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.6e-63) {
		tmp = x + ((fma((0.3333333333333333 / (y * y)), t, -0.3333333333333333) / z) * y);
	} else if (y <= 1.32e-120) {
		tmp = fma((t / z), (0.3333333333333333 / y), x);
	} else {
		tmp = x - (((y - (t / y)) / 3.0) / z);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.6e-63)
		tmp = Float64(x + Float64(Float64(fma(Float64(0.3333333333333333 / Float64(y * y)), t, -0.3333333333333333) / z) * y));
	elseif (y <= 1.32e-120)
		tmp = fma(Float64(t / z), Float64(0.3333333333333333 / y), x);
	else
		tmp = Float64(x - Float64(Float64(Float64(y - Float64(t / y)) / 3.0) / z));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.6e-63], N[(x + N[(N[(N[(N[(0.3333333333333333 / N[(y * y), $MachinePrecision]), $MachinePrecision] * t + -0.3333333333333333), $MachinePrecision] / z), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.32e-120], N[(N[(t / z), $MachinePrecision] * N[(0.3333333333333333 / y), $MachinePrecision] + x), $MachinePrecision], N[(x - N[(N[(N[(y - N[(t / y), $MachinePrecision]), $MachinePrecision] / 3.0), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.59999999999999994e-63

   1. Initial program 98.4%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. fp-cancel-sub-sign-inv N/A

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

      2. +-commutative N/A

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

      3. associate-+l+ N/A

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

      4. distribute-rgt-in N/A

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

      5. associate-*l/ N/A

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

      6. associate-/l* N/A

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

      7. *-inverses N/A

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

      8. *-rgt-identity N/A

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

      9. lower-+.f64 N/A

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

      10. lower-*.f64 N/A

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

   5. Applied rewrites 99.8%

      \[\leadsto \color{blue}{x + \frac{\mathsf{fma}\left(\frac{0.3333333333333333}{y \cdot y}, t, -0.3333333333333333\right)}{z} \cdot y} \]

   if -1.59999999999999994e-63 < y < 1.32000000000000004e-120

   1. Initial program 93.3%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. associate-/r* N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lower-*.f64 99.7

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

   4. Applied rewrites 99.7%

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

   5. Taylor expanded in y around 0

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

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{\color{blue}{\frac{1}{3} \cdot \frac{t}{z} - \left(\mathsf{neg}\left(x\right)\right) \cdot y}}{y} \]

      2. div-sub N/A

         \[\leadsto \color{blue}{\frac{\frac{1}{3} \cdot \frac{t}{z}}{y} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y}} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{\frac{t}{z}}{y}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      4. associate-/r* N/A

         \[\leadsto \frac{1}{3} \cdot \color{blue}{\frac{t}{z \cdot y}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{\color{blue}{y \cdot z}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      6. associate-*l/ N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\frac{\mathsf{neg}\left(x\right)}{y} \cdot y} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right)} \cdot y \]

      8. mul-1-neg N/A

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

      9. *-commutative N/A

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

      10. fp-cancel-sub-sign-inv N/A

          \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{t}{y \cdot z} + \left(\mathsf{neg}\left(y\right)\right) \cdot \left(-1 \cdot \frac{x}{y}\right)} \]

      11. *-commutative N/A

          \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} + \left(\mathsf{neg}\left(y\right)\right) \cdot \left(-1 \cdot \frac{x}{y}\right) \]

      12. distribute-lft-neg-in N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{\left(\mathsf{neg}\left(y \cdot \left(-1 \cdot \frac{x}{y}\right)\right)\right)} \]

      13. mul-1-neg N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(y \cdot \color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right)}\right)\right) \]

      14. distribute-rgt-neg-out N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(y \cdot \frac{x}{y}\right)\right)}\right)\right) \]

      15. associate-/l* N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\color{blue}{\frac{y \cdot x}{y}}\right)\right)\right)\right) \]

      16. *-commutative N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{\color{blue}{x \cdot y}}{y}\right)\right)\right)\right) \]

      17. remove-double-neg N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{\frac{x \cdot y}{y}} \]

      18. associate-/l* N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{x \cdot \frac{y}{y}} \]

      19. *-inverses N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + x \cdot \color{blue}{1} \]

      20. *-rgt-identity N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{x} \]

      21. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{y \cdot z}, \frac{1}{3}, x\right)} \]

   7. Applied rewrites 93.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{z \cdot y}, 0.3333333333333333, x\right)} \]
   8. Step-by-step derivation

   9. Applied rewrites 99.6%

      \[\leadsto \mathsf{fma}\left(\frac{t}{z}, \color{blue}{\frac{0.3333333333333333}{y}}, x\right) \]

   if 1.32000000000000004e-120 < y

   1. Initial program 97.2%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. associate-+l- N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      4. lower--.f64 N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto x - \left(\color{blue}{\frac{y}{z \cdot 3}} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]

      6. lift-/.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{t}{\left(z \cdot 3\right) \cdot y}}\right) \]

      7. lift-*.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{\left(z \cdot 3\right) \cdot y}}\right) \]

      8. *-commutative N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{y \cdot \left(z \cdot 3\right)}}\right) \]

      9. associate-/r* N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{\frac{t}{y}}{z \cdot 3}}\right) \]

      10. sub-div N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      11. lower-/.f64 N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      12. lower--.f64 N/A

          \[\leadsto x - \frac{\color{blue}{y - \frac{t}{y}}}{z \cdot 3} \]

      13. lower-/.f64 98.5

          \[\leadsto x - \frac{y - \color{blue}{\frac{t}{y}}}{z \cdot 3} \]

      14. lift-*.f64 N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{z \cdot 3}} \]

      15. *-commutative N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      16. lower-*.f64 98.5

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

   4. Applied rewrites 98.5%

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

      1. lift-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{3 \cdot z}} \]

      2. lift-*.f64 N/A

         \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      3. associate-/r* N/A

         \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

      4. lower-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

      5. lower-/.f64 98.6

         \[\leadsto x - \frac{\color{blue}{\frac{y - \frac{t}{y}}{3}}}{z} \]

   6. Applied rewrites 98.6%

      \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.6 \cdot 10^{-63}:\\ \;\;\;\;x + \frac{\mathsf{fma}\left(\frac{0.3333333333333333}{y \cdot y}, t, -0.3333333333333333\right)}{z} \cdot y\\ \mathbf{elif}\;y \leq 1.32 \cdot 10^{-120}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z}, \frac{0.3333333333333333}{y}, x\right)\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\frac{y - \frac{t}{y}}{3}}{z}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 96.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x - \frac{y}{z \cdot 3}\\ \mathbf{if}\;t\_1 + \frac{t}{\left(z \cdot 3\right) \cdot y} \leq 1.5 \cdot 10^{+212}:\\ \;\;\;\;t\_1 + \frac{\frac{t}{z}}{3 \cdot y}\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\frac{y - \frac{t}{y}}{3}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- x (/ y (* z 3.0)))))
   (if (<= (+ t_1 (/ t (* (* z 3.0) y))) 1.5e+212)
     (+ t_1 (/ (/ t z) (* 3.0 y)))
     (- x (/ (/ (- y (/ t y)) 3.0) z)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x - (y / (z * 3.0));
	double tmp;
	if ((t_1 + (t / ((z * 3.0) * y))) <= 1.5e+212) {
		tmp = t_1 + ((t / z) / (3.0 * y));
	} else {
		tmp = x - (((y - (t / y)) / 3.0) / z);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x - (y / (z * 3.0d0))
    if ((t_1 + (t / ((z * 3.0d0) * y))) <= 1.5d+212) then
        tmp = t_1 + ((t / z) / (3.0d0 * y))
    else
        tmp = x - (((y - (t / y)) / 3.0d0) / z)
    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 * 3.0));
	double tmp;
	if ((t_1 + (t / ((z * 3.0) * y))) <= 1.5e+212) {
		tmp = t_1 + ((t / z) / (3.0 * y));
	} else {
		tmp = x - (((y - (t / y)) / 3.0) / z);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x - (y / (z * 3.0))
	tmp = 0
	if (t_1 + (t / ((z * 3.0) * y))) <= 1.5e+212:
		tmp = t_1 + ((t / z) / (3.0 * y))
	else:
		tmp = x - (((y - (t / y)) / 3.0) / z)
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x - Float64(y / Float64(z * 3.0)))
	tmp = 0.0
	if (Float64(t_1 + Float64(t / Float64(Float64(z * 3.0) * y))) <= 1.5e+212)
		tmp = Float64(t_1 + Float64(Float64(t / z) / Float64(3.0 * y)));
	else
		tmp = Float64(x - Float64(Float64(Float64(y - Float64(t / y)) / 3.0) / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x - (y / (z * 3.0));
	tmp = 0.0;
	if ((t_1 + (t / ((z * 3.0) * y))) <= 1.5e+212)
		tmp = t_1 + ((t / z) / (3.0 * y));
	else
		tmp = x - (((y - (t / y)) / 3.0) / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x - N[(y / N[(z * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(t$95$1 + N[(t / N[(N[(z * 3.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.5e+212], N[(t$95$1 + N[(N[(t / z), $MachinePrecision] / N[(3.0 * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(N[(y - N[(t / y), $MachinePrecision]), $MachinePrecision] / 3.0), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 x (/.f64 y (*.f64 z #s(literal 3 binary64)))) (/.f64 t (*.f64 (*.f64 z #s(literal 3 binary64)) y))) < 1.5e212

   1. Initial program 98.3%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. associate-/r* N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lower-*.f64 99.1

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

   4. Applied rewrites 99.1%

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

   if 1.5e212 < (+.f64 (-.f64 x (/.f64 y (*.f64 z #s(literal 3 binary64)))) (/.f64 t (*.f64 (*.f64 z #s(literal 3 binary64)) y)))

   1. Initial program 89.7%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. associate-+l- N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      4. lower--.f64 N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto x - \left(\color{blue}{\frac{y}{z \cdot 3}} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]

      6. lift-/.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{t}{\left(z \cdot 3\right) \cdot y}}\right) \]

      7. lift-*.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{\left(z \cdot 3\right) \cdot y}}\right) \]

      8. *-commutative N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{y \cdot \left(z \cdot 3\right)}}\right) \]

      9. associate-/r* N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{\frac{t}{y}}{z \cdot 3}}\right) \]

      10. sub-div N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      11. lower-/.f64 N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      12. lower--.f64 N/A

          \[\leadsto x - \frac{\color{blue}{y - \frac{t}{y}}}{z \cdot 3} \]

      13. lower-/.f64 99.8

          \[\leadsto x - \frac{y - \color{blue}{\frac{t}{y}}}{z \cdot 3} \]

      14. lift-*.f64 N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{z \cdot 3}} \]

      15. *-commutative N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      16. lower-*.f64 99.8

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

   4. Applied rewrites 99.8%

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

      1. lift-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{3 \cdot z}} \]

      2. lift-*.f64 N/A

         \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      3. associate-/r* N/A

         \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

      4. lower-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

      5. lower-/.f64 99.9

         \[\leadsto x - \frac{\color{blue}{\frac{y - \frac{t}{y}}{3}}}{z} \]

   6. Applied rewrites 99.9%

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

Alternative 3: 96.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{\left(z \cdot 3\right) \cdot y}\\ \mathbf{if}\;\left(x - \frac{y}{z \cdot 3}\right) + t\_1 \leq 1.5 \cdot 10^{+212}:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, \frac{y}{z}, x\right) + t\_1\\ \mathbf{else}:\\ \;\;\;\;x - \frac{\frac{y - \frac{t}{y}}{3}}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ t (* (* z 3.0) y))))
   (if (<= (+ (- x (/ y (* z 3.0))) t_1) 1.5e+212)
     (+ (fma -0.3333333333333333 (/ y z) x) t_1)
     (- x (/ (/ (- y (/ t y)) 3.0) z)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = t / ((z * 3.0) * y);
	double tmp;
	if (((x - (y / (z * 3.0))) + t_1) <= 1.5e+212) {
		tmp = fma(-0.3333333333333333, (y / z), x) + t_1;
	} else {
		tmp = x - (((y - (t / y)) / 3.0) / z);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(t / Float64(Float64(z * 3.0) * y))
	tmp = 0.0
	if (Float64(Float64(x - Float64(y / Float64(z * 3.0))) + t_1) <= 1.5e+212)
		tmp = Float64(fma(-0.3333333333333333, Float64(y / z), x) + t_1);
	else
		tmp = Float64(x - Float64(Float64(Float64(y - Float64(t / y)) / 3.0) / z));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(t / N[(N[(z * 3.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(x - N[(y / N[(z * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision], 1.5e+212], N[(N[(-0.3333333333333333 * N[(y / z), $MachinePrecision] + x), $MachinePrecision] + t$95$1), $MachinePrecision], N[(x - N[(N[(N[(y - N[(t / y), $MachinePrecision]), $MachinePrecision] / 3.0), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 x (/.f64 y (*.f64 z #s(literal 3 binary64)))) (/.f64 t (*.f64 (*.f64 z #s(literal 3 binary64)) y))) < 1.5e212

   1. Initial program 98.3%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. metadata-eval N/A

         \[\leadsto \left(x - \color{blue}{\left(\mathsf{neg}\left(\frac{-1}{3}\right)\right)} \cdot \frac{y}{z}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]

      2. fp-cancel-sign-sub-inv N/A

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

      3. +-commutative N/A

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

      4. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{3}, \frac{y}{z}, x\right)} + \frac{t}{\left(z \cdot 3\right) \cdot y} \]

      5. lower-/.f64 98.3

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

   5. Applied rewrites 98.3%

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

   if 1.5e212 < (+.f64 (-.f64 x (/.f64 y (*.f64 z #s(literal 3 binary64)))) (/.f64 t (*.f64 (*.f64 z #s(literal 3 binary64)) y)))

   1. Initial program 89.7%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. associate-+l- N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      4. lower--.f64 N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto x - \left(\color{blue}{\frac{y}{z \cdot 3}} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]

      6. lift-/.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{t}{\left(z \cdot 3\right) \cdot y}}\right) \]

      7. lift-*.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{\left(z \cdot 3\right) \cdot y}}\right) \]

      8. *-commutative N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{y \cdot \left(z \cdot 3\right)}}\right) \]

      9. associate-/r* N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{\frac{t}{y}}{z \cdot 3}}\right) \]

      10. sub-div N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      11. lower-/.f64 N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      12. lower--.f64 N/A

          \[\leadsto x - \frac{\color{blue}{y - \frac{t}{y}}}{z \cdot 3} \]

      13. lower-/.f64 99.8

          \[\leadsto x - \frac{y - \color{blue}{\frac{t}{y}}}{z \cdot 3} \]

      14. lift-*.f64 N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{z \cdot 3}} \]

      15. *-commutative N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      16. lower-*.f64 99.8

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

   4. Applied rewrites 99.8%

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

      1. lift-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{3 \cdot z}} \]

      2. lift-*.f64 N/A

         \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      3. associate-/r* N/A

         \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

      4. lower-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

      5. lower-/.f64 99.9

         \[\leadsto x - \frac{\color{blue}{\frac{y - \frac{t}{y}}{3}}}{z} \]

   6. Applied rewrites 99.9%

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

Alternative 4: 98.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-64} \lor \neg \left(y \leq 1.32 \cdot 10^{-120}\right):\\ \;\;\;\;x - \frac{\frac{y - \frac{t}{y}}{3}}{z}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z}, \frac{0.3333333333333333}{y}, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -9e-64) (not (<= y 1.32e-120)))
   (- x (/ (/ (- y (/ t y)) 3.0) z))
   (fma (/ t z) (/ 0.3333333333333333 y) x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9e-64) || !(y <= 1.32e-120)) {
		tmp = x - (((y - (t / y)) / 3.0) / z);
	} else {
		tmp = fma((t / z), (0.3333333333333333 / y), x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -9e-64) || !(y <= 1.32e-120))
		tmp = Float64(x - Float64(Float64(Float64(y - Float64(t / y)) / 3.0) / z));
	else
		tmp = fma(Float64(t / z), Float64(0.3333333333333333 / y), x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -9e-64], N[Not[LessEqual[y, 1.32e-120]], $MachinePrecision]], N[(x - N[(N[(N[(y - N[(t / y), $MachinePrecision]), $MachinePrecision] / 3.0), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], N[(N[(t / z), $MachinePrecision] * N[(0.3333333333333333 / y), $MachinePrecision] + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -9.00000000000000019e-64 or 1.32000000000000004e-120 < y

   1. Initial program 97.8%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. associate-+l- N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      4. lower--.f64 N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto x - \left(\color{blue}{\frac{y}{z \cdot 3}} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]

      6. lift-/.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{t}{\left(z \cdot 3\right) \cdot y}}\right) \]

      7. lift-*.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{\left(z \cdot 3\right) \cdot y}}\right) \]

      8. *-commutative N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{y \cdot \left(z \cdot 3\right)}}\right) \]

      9. associate-/r* N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{\frac{t}{y}}{z \cdot 3}}\right) \]

      10. sub-div N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      11. lower-/.f64 N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      12. lower--.f64 N/A

          \[\leadsto x - \frac{\color{blue}{y - \frac{t}{y}}}{z \cdot 3} \]

      13. lower-/.f64 99.1

          \[\leadsto x - \frac{y - \color{blue}{\frac{t}{y}}}{z \cdot 3} \]

      14. lift-*.f64 N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{z \cdot 3}} \]

      15. *-commutative N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      16. lower-*.f64 99.1

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

   4. Applied rewrites 99.1%

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

      1. lift-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{3 \cdot z}} \]

      2. lift-*.f64 N/A

         \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      3. associate-/r* N/A

         \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

      4. lower-/.f64 N/A

         \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

      5. lower-/.f64 99.2

         \[\leadsto x - \frac{\color{blue}{\frac{y - \frac{t}{y}}{3}}}{z} \]

   6. Applied rewrites 99.2%

      \[\leadsto x - \color{blue}{\frac{\frac{y - \frac{t}{y}}{3}}{z}} \]

   if -9.00000000000000019e-64 < y < 1.32000000000000004e-120

   1. Initial program 93.3%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. associate-/r* N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lower-*.f64 99.7

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

   4. Applied rewrites 99.7%

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

   5. Taylor expanded in y around 0

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

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{\color{blue}{\frac{1}{3} \cdot \frac{t}{z} - \left(\mathsf{neg}\left(x\right)\right) \cdot y}}{y} \]

      2. div-sub N/A

         \[\leadsto \color{blue}{\frac{\frac{1}{3} \cdot \frac{t}{z}}{y} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y}} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{\frac{t}{z}}{y}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      4. associate-/r* N/A

         \[\leadsto \frac{1}{3} \cdot \color{blue}{\frac{t}{z \cdot y}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{\color{blue}{y \cdot z}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      6. associate-*l/ N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\frac{\mathsf{neg}\left(x\right)}{y} \cdot y} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right)} \cdot y \]

      8. mul-1-neg N/A

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

      9. *-commutative N/A

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

      10. fp-cancel-sub-sign-inv N/A

          \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{t}{y \cdot z} + \left(\mathsf{neg}\left(y\right)\right) \cdot \left(-1 \cdot \frac{x}{y}\right)} \]

      11. *-commutative N/A

          \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} + \left(\mathsf{neg}\left(y\right)\right) \cdot \left(-1 \cdot \frac{x}{y}\right) \]

      12. distribute-lft-neg-in N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{\left(\mathsf{neg}\left(y \cdot \left(-1 \cdot \frac{x}{y}\right)\right)\right)} \]

      13. mul-1-neg N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(y \cdot \color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right)}\right)\right) \]

      14. distribute-rgt-neg-out N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(y \cdot \frac{x}{y}\right)\right)}\right)\right) \]

      15. associate-/l* N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\color{blue}{\frac{y \cdot x}{y}}\right)\right)\right)\right) \]

      16. *-commutative N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{\color{blue}{x \cdot y}}{y}\right)\right)\right)\right) \]

      17. remove-double-neg N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{\frac{x \cdot y}{y}} \]

      18. associate-/l* N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{x \cdot \frac{y}{y}} \]

      19. *-inverses N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + x \cdot \color{blue}{1} \]

      20. *-rgt-identity N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{x} \]

      21. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{y \cdot z}, \frac{1}{3}, x\right)} \]

   7. Applied rewrites 93.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{z \cdot y}, 0.3333333333333333, x\right)} \]
   8. Step-by-step derivation

   9. Applied rewrites 99.6%

      \[\leadsto \mathsf{fma}\left(\frac{t}{z}, \color{blue}{\frac{0.3333333333333333}{y}}, x\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-64} \lor \neg \left(y \leq 1.32 \cdot 10^{-120}\right):\\ \;\;\;\;x - \frac{\frac{y - \frac{t}{y}}{3}}{z}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z}, \frac{0.3333333333333333}{y}, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-64} \lor \neg \left(y \leq 5 \cdot 10^{-121}\right):\\ \;\;\;\;x - \frac{y - \frac{t}{y}}{3 \cdot z}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z}, \frac{0.3333333333333333}{y}, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -9e-64) (not (<= y 5e-121)))
   (- x (/ (- y (/ t y)) (* 3.0 z)))
   (fma (/ t z) (/ 0.3333333333333333 y) x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9e-64) || !(y <= 5e-121)) {
		tmp = x - ((y - (t / y)) / (3.0 * z));
	} else {
		tmp = fma((t / z), (0.3333333333333333 / y), x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -9e-64) || !(y <= 5e-121))
		tmp = Float64(x - Float64(Float64(y - Float64(t / y)) / Float64(3.0 * z)));
	else
		tmp = fma(Float64(t / z), Float64(0.3333333333333333 / y), x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -9e-64], N[Not[LessEqual[y, 5e-121]], $MachinePrecision]], N[(x - N[(N[(y - N[(t / y), $MachinePrecision]), $MachinePrecision] / N[(3.0 * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(t / z), $MachinePrecision] * N[(0.3333333333333333 / y), $MachinePrecision] + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -9.00000000000000019e-64 or 4.99999999999999989e-121 < y

   1. Initial program 97.8%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. associate-+l- N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      4. lower--.f64 N/A

         \[\leadsto \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto x - \left(\color{blue}{\frac{y}{z \cdot 3}} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]

      6. lift-/.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{t}{\left(z \cdot 3\right) \cdot y}}\right) \]

      7. lift-*.f64 N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{\left(z \cdot 3\right) \cdot y}}\right) \]

      8. *-commutative N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \frac{t}{\color{blue}{y \cdot \left(z \cdot 3\right)}}\right) \]

      9. associate-/r* N/A

         \[\leadsto x - \left(\frac{y}{z \cdot 3} - \color{blue}{\frac{\frac{t}{y}}{z \cdot 3}}\right) \]

      10. sub-div N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      11. lower-/.f64 N/A

          \[\leadsto x - \color{blue}{\frac{y - \frac{t}{y}}{z \cdot 3}} \]

      12. lower--.f64 N/A

          \[\leadsto x - \frac{\color{blue}{y - \frac{t}{y}}}{z \cdot 3} \]

      13. lower-/.f64 99.1

          \[\leadsto x - \frac{y - \color{blue}{\frac{t}{y}}}{z \cdot 3} \]

      14. lift-*.f64 N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{z \cdot 3}} \]

      15. *-commutative N/A

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

      16. lower-*.f64 99.1

          \[\leadsto x - \frac{y - \frac{t}{y}}{\color{blue}{3 \cdot z}} \]

   4. Applied rewrites 99.1%

      \[\leadsto \color{blue}{x - \frac{y - \frac{t}{y}}{3 \cdot z}} \]

   if -9.00000000000000019e-64 < y < 4.99999999999999989e-121

   1. Initial program 93.3%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. associate-/r* N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lower-*.f64 99.7

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

   4. Applied rewrites 99.7%

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

   5. Taylor expanded in y around 0

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

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{\color{blue}{\frac{1}{3} \cdot \frac{t}{z} - \left(\mathsf{neg}\left(x\right)\right) \cdot y}}{y} \]

      2. div-sub N/A

         \[\leadsto \color{blue}{\frac{\frac{1}{3} \cdot \frac{t}{z}}{y} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y}} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{\frac{t}{z}}{y}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      4. associate-/r* N/A

         \[\leadsto \frac{1}{3} \cdot \color{blue}{\frac{t}{z \cdot y}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{\color{blue}{y \cdot z}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      6. associate-*l/ N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\frac{\mathsf{neg}\left(x\right)}{y} \cdot y} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right)} \cdot y \]

      8. mul-1-neg N/A

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

      9. *-commutative N/A

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

      10. fp-cancel-sub-sign-inv N/A

          \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{t}{y \cdot z} + \left(\mathsf{neg}\left(y\right)\right) \cdot \left(-1 \cdot \frac{x}{y}\right)} \]

      11. *-commutative N/A

          \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} + \left(\mathsf{neg}\left(y\right)\right) \cdot \left(-1 \cdot \frac{x}{y}\right) \]

      12. distribute-lft-neg-in N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{\left(\mathsf{neg}\left(y \cdot \left(-1 \cdot \frac{x}{y}\right)\right)\right)} \]

      13. mul-1-neg N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(y \cdot \color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right)}\right)\right) \]

      14. distribute-rgt-neg-out N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(y \cdot \frac{x}{y}\right)\right)}\right)\right) \]

      15. associate-/l* N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\color{blue}{\frac{y \cdot x}{y}}\right)\right)\right)\right) \]

      16. *-commutative N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{\color{blue}{x \cdot y}}{y}\right)\right)\right)\right) \]

      17. remove-double-neg N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{\frac{x \cdot y}{y}} \]

      18. associate-/l* N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{x \cdot \frac{y}{y}} \]

      19. *-inverses N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + x \cdot \color{blue}{1} \]

      20. *-rgt-identity N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{x} \]

      21. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{y \cdot z}, \frac{1}{3}, x\right)} \]

   7. Applied rewrites 93.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{z \cdot y}, 0.3333333333333333, x\right)} \]
   8. Step-by-step derivation

   9. Applied rewrites 99.6%

      \[\leadsto \mathsf{fma}\left(\frac{t}{z}, \color{blue}{\frac{0.3333333333333333}{y}}, x\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9 \cdot 10^{-64} \lor \neg \left(y \leq 5 \cdot 10^{-121}\right):\\ \;\;\;\;x - \frac{y - \frac{t}{y}}{3 \cdot z}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z}, \frac{0.3333333333333333}{y}, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 91.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+58} \lor \neg \left(y \leq 1.6 \cdot 10^{+34}\right):\\ \;\;\;\;x + \frac{-0.3333333333333333}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z}, \frac{0.3333333333333333}{y}, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -6e+58) (not (<= y 1.6e+34)))
   (+ x (* (/ -0.3333333333333333 z) y))
   (fma (/ t z) (/ 0.3333333333333333 y) x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -6e+58) || !(y <= 1.6e+34)) {
		tmp = x + ((-0.3333333333333333 / z) * y);
	} else {
		tmp = fma((t / z), (0.3333333333333333 / y), x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -6e+58) || !(y <= 1.6e+34))
		tmp = Float64(x + Float64(Float64(-0.3333333333333333 / z) * y));
	else
		tmp = fma(Float64(t / z), Float64(0.3333333333333333 / y), x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -6e+58], N[Not[LessEqual[y, 1.6e+34]], $MachinePrecision]], N[(x + N[(N[(-0.3333333333333333 / z), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(N[(t / z), $MachinePrecision] * N[(0.3333333333333333 / y), $MachinePrecision] + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -6.0000000000000005e58 or 1.5999999999999999e34 < y

   1. Initial program 97.7%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. fp-cancel-sub-sign-inv N/A

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

      2. +-commutative N/A

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

      3. associate-+l+ N/A

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

      4. distribute-rgt-in N/A

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

      5. associate-*l/ N/A

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

      6. associate-/l* N/A

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

      7. *-inverses N/A

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

      8. *-rgt-identity N/A

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

      9. lower-+.f64 N/A

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

      10. lower-*.f64 N/A

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

   5. Applied rewrites 99.8%

      \[\leadsto \color{blue}{x + \frac{\mathsf{fma}\left(\frac{0.3333333333333333}{y \cdot y}, t, -0.3333333333333333\right)}{z} \cdot y} \]

   6. Taylor expanded in y around inf

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

   8. Applied rewrites 98.8%

      \[\leadsto x + \frac{-0.3333333333333333}{z} \cdot y \]

   if -6.0000000000000005e58 < y < 1.5999999999999999e34

   1. Initial program 95.0%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. associate-/r* N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lower-*.f64 99.7

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

   4. Applied rewrites 99.7%

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

   5. Taylor expanded in y around 0

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

      1. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{\color{blue}{\frac{1}{3} \cdot \frac{t}{z} - \left(\mathsf{neg}\left(x\right)\right) \cdot y}}{y} \]

      2. div-sub N/A

         \[\leadsto \color{blue}{\frac{\frac{1}{3} \cdot \frac{t}{z}}{y} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y}} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{\frac{t}{z}}{y}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      4. associate-/r* N/A

         \[\leadsto \frac{1}{3} \cdot \color{blue}{\frac{t}{z \cdot y}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{\color{blue}{y \cdot z}} - \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{y} \]

      6. associate-*l/ N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\frac{\mathsf{neg}\left(x\right)}{y} \cdot y} \]

      7. distribute-neg-frac N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right)} \cdot y \]

      8. mul-1-neg N/A

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

      9. *-commutative N/A

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

      10. fp-cancel-sub-sign-inv N/A

          \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{t}{y \cdot z} + \left(\mathsf{neg}\left(y\right)\right) \cdot \left(-1 \cdot \frac{x}{y}\right)} \]

      11. *-commutative N/A

          \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} + \left(\mathsf{neg}\left(y\right)\right) \cdot \left(-1 \cdot \frac{x}{y}\right) \]

      12. distribute-lft-neg-in N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{\left(\mathsf{neg}\left(y \cdot \left(-1 \cdot \frac{x}{y}\right)\right)\right)} \]

      13. mul-1-neg N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(y \cdot \color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right)}\right)\right) \]

      14. distribute-rgt-neg-out N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(y \cdot \frac{x}{y}\right)\right)}\right)\right) \]

      15. associate-/l* N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\color{blue}{\frac{y \cdot x}{y}}\right)\right)\right)\right) \]

      16. *-commutative N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(\frac{\color{blue}{x \cdot y}}{y}\right)\right)\right)\right) \]

      17. remove-double-neg N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{\frac{x \cdot y}{y}} \]

      18. associate-/l* N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{x \cdot \frac{y}{y}} \]

      19. *-inverses N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + x \cdot \color{blue}{1} \]

      20. *-rgt-identity N/A

          \[\leadsto \frac{t}{y \cdot z} \cdot \frac{1}{3} + \color{blue}{x} \]

      21. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{y \cdot z}, \frac{1}{3}, x\right)} \]

   7. Applied rewrites 88.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{z \cdot y}, 0.3333333333333333, x\right)} \]
   8. Step-by-step derivation

   9. Applied rewrites 92.7%

      \[\leadsto \mathsf{fma}\left(\frac{t}{z}, \color{blue}{\frac{0.3333333333333333}{y}}, x\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 95.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+58} \lor \neg \left(y \leq 1.6 \cdot 10^{+34}\right):\\ \;\;\;\;x + \frac{-0.3333333333333333}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z}, \frac{0.3333333333333333}{y}, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 89.5% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+58} \lor \neg \left(y \leq 1.18 \cdot 10^{+36}\right):\\ \;\;\;\;x + \frac{-0.3333333333333333}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z \cdot y}, 0.3333333333333333, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -6e+58) (not (<= y 1.18e+36)))
   (+ x (* (/ -0.3333333333333333 z) y))
   (fma (/ t (* z y)) 0.3333333333333333 x)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -6e+58) || !(y <= 1.18e+36)) {
		tmp = x + ((-0.3333333333333333 / z) * y);
	} else {
		tmp = fma((t / (z * y)), 0.3333333333333333, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -6e+58) || !(y <= 1.18e+36))
		tmp = Float64(x + Float64(Float64(-0.3333333333333333 / z) * y));
	else
		tmp = fma(Float64(t / Float64(z * y)), 0.3333333333333333, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -6e+58], N[Not[LessEqual[y, 1.18e+36]], $MachinePrecision]], N[(x + N[(N[(-0.3333333333333333 / z), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(N[(t / N[(z * y), $MachinePrecision]), $MachinePrecision] * 0.3333333333333333 + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -6.0000000000000005e58 or 1.17999999999999997e36 < y

   1. Initial program 97.7%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. fp-cancel-sub-sign-inv N/A

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

      2. +-commutative N/A

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

      3. associate-+l+ N/A

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

      4. distribute-rgt-in N/A

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

      5. associate-*l/ N/A

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

      6. associate-/l* N/A

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

      7. *-inverses N/A

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

      8. *-rgt-identity N/A

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

      9. lower-+.f64 N/A

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

      10. lower-*.f64 N/A

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

   5. Applied rewrites 99.8%

      \[\leadsto \color{blue}{x + \frac{\mathsf{fma}\left(\frac{0.3333333333333333}{y \cdot y}, t, -0.3333333333333333\right)}{z} \cdot y} \]

   6. Taylor expanded in y around inf

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

   8. Applied rewrites 98.8%

      \[\leadsto x + \frac{-0.3333333333333333}{z} \cdot y \]

   if -6.0000000000000005e58 < y < 1.17999999999999997e36

   1. Initial program 95.0%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. div-add N/A

         \[\leadsto \color{blue}{\frac{\frac{1}{3} \cdot \frac{t}{z}}{y} + \frac{x \cdot y}{y}} \]

      2. associate-/l* N/A

         \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{\frac{t}{z}}{y}} + \frac{x \cdot y}{y} \]

      3. associate-/r* N/A

         \[\leadsto \frac{1}{3} \cdot \color{blue}{\frac{t}{z \cdot y}} + \frac{x \cdot y}{y} \]

      4. *-commutative N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{\color{blue}{y \cdot z}} + \frac{x \cdot y}{y} \]

      5. associate-/l* N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} + \color{blue}{x \cdot \frac{y}{y}} \]

      6. fp-cancel-sign-sub-inv N/A

         \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{t}{y \cdot z} - \left(\mathsf{neg}\left(x\right)\right) \cdot \frac{y}{y}} \]

      7. distribute-lft-neg-in N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\left(\mathsf{neg}\left(x \cdot \frac{y}{y}\right)\right)} \]

      8. distribute-rgt-neg-in N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{x \cdot \left(\mathsf{neg}\left(\frac{y}{y}\right)\right)} \]

      9. *-inverses N/A

         \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - x \cdot \left(\mathsf{neg}\left(\color{blue}{1}\right)\right) \]

      10. metadata-eval N/A

          \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - x \cdot \color{blue}{-1} \]

      11. *-commutative N/A

          \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{-1 \cdot x} \]

      12. metadata-eval N/A

          \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right)} \cdot x \]

      13. fp-cancel-sign-sub-inv N/A

          \[\leadsto \color{blue}{\frac{1}{3} \cdot \frac{t}{y \cdot z} + 1 \cdot x} \]

      14. *-lft-identity N/A

          \[\leadsto \frac{1}{3} \cdot \frac{t}{y \cdot z} + \color{blue}{x} \]

      15. *-commutative N/A

          \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} + x \]

      16. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{y \cdot z}, \frac{1}{3}, x\right)} \]

      17. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{t}{y \cdot z}}, \frac{1}{3}, x\right) \]

      18. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\frac{t}{\color{blue}{z \cdot y}}, \frac{1}{3}, x\right) \]

      19. lower-*.f64 88.0

          \[\leadsto \mathsf{fma}\left(\frac{t}{\color{blue}{z \cdot y}}, 0.3333333333333333, x\right) \]

   5. Applied rewrites 88.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{z \cdot y}, 0.3333333333333333, x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 92.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+58} \lor \neg \left(y \leq 1.18 \cdot 10^{+36}\right):\\ \;\;\;\;x + \frac{-0.3333333333333333}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{z \cdot y}, 0.3333333333333333, x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 76.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.36 \cdot 10^{-70} \lor \neg \left(y \leq 2.9 \cdot 10^{-42}\right):\\ \;\;\;\;x + \frac{-0.3333333333333333}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\frac{0.3333333333333333 \cdot t}{z \cdot y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -1.36e-70) (not (<= y 2.9e-42)))
   (+ x (* (/ -0.3333333333333333 z) y))
   (/ (* 0.3333333333333333 t) (* z y))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.36e-70) || !(y <= 2.9e-42)) {
		tmp = x + ((-0.3333333333333333 / z) * y);
	} else {
		tmp = (0.3333333333333333 * t) / (z * y);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-1.36d-70)) .or. (.not. (y <= 2.9d-42))) then
        tmp = x + (((-0.3333333333333333d0) / z) * y)
    else
        tmp = (0.3333333333333333d0 * t) / (z * y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.36e-70) || !(y <= 2.9e-42)) {
		tmp = x + ((-0.3333333333333333 / z) * y);
	} else {
		tmp = (0.3333333333333333 * t) / (z * y);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -1.36e-70) or not (y <= 2.9e-42):
		tmp = x + ((-0.3333333333333333 / z) * y)
	else:
		tmp = (0.3333333333333333 * t) / (z * y)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -1.36e-70) || !(y <= 2.9e-42))
		tmp = Float64(x + Float64(Float64(-0.3333333333333333 / z) * y));
	else
		tmp = Float64(Float64(0.3333333333333333 * t) / Float64(z * y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -1.36e-70) || ~((y <= 2.9e-42)))
		tmp = x + ((-0.3333333333333333 / z) * y);
	else
		tmp = (0.3333333333333333 * t) / (z * y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -1.36e-70], N[Not[LessEqual[y, 2.9e-42]], $MachinePrecision]], N[(x + N[(N[(-0.3333333333333333 / z), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(N[(0.3333333333333333 * t), $MachinePrecision] / N[(z * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.36000000000000001e-70 or 2.9000000000000003e-42 < y

   1. Initial program 98.3%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. fp-cancel-sub-sign-inv N/A

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

      2. +-commutative N/A

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

      3. associate-+l+ N/A

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

      4. distribute-rgt-in N/A

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

      5. associate-*l/ N/A

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

      6. associate-/l* N/A

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

      7. *-inverses N/A

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

      8. *-rgt-identity N/A

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

      9. lower-+.f64 N/A

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

      10. lower-*.f64 N/A

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

   5. Applied rewrites 99.8%

      \[\leadsto \color{blue}{x + \frac{\mathsf{fma}\left(\frac{0.3333333333333333}{y \cdot y}, t, -0.3333333333333333\right)}{z} \cdot y} \]

   6. Taylor expanded in y around inf

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

   8. Applied rewrites 89.4%

      \[\leadsto x + \frac{-0.3333333333333333}{z} \cdot y \]

   if -1.36000000000000001e-70 < y < 2.9000000000000003e-42

   1. Initial program 93.4%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. associate-/r* N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lower-*.f64 99.7

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

   4. Applied rewrites 99.7%

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

   5. Taylor expanded in y around 0

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

      1. *-commutative N/A

         \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} \]

      3. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{t}{y \cdot z}} \cdot \frac{1}{3} \]

      4. *-commutative N/A

         \[\leadsto \frac{t}{\color{blue}{z \cdot y}} \cdot \frac{1}{3} \]

      5. lower-*.f64 65.3

         \[\leadsto \frac{t}{\color{blue}{z \cdot y}} \cdot 0.3333333333333333 \]

   7. Applied rewrites 65.3%

      \[\leadsto \color{blue}{\frac{t}{z \cdot y} \cdot 0.3333333333333333} \]
   8. Step-by-step derivation

   9. Applied rewrites 65.3%

      \[\leadsto \frac{0.3333333333333333 \cdot t}{\color{blue}{z \cdot y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 78.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.36 \cdot 10^{-70} \lor \neg \left(y \leq 2.9 \cdot 10^{-42}\right):\\ \;\;\;\;x + \frac{-0.3333333333333333}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\frac{0.3333333333333333 \cdot t}{z \cdot y}\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 76.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.36 \cdot 10^{-70} \lor \neg \left(y \leq 2.9 \cdot 10^{-42}\right):\\ \;\;\;\;x + \frac{-0.3333333333333333}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{z \cdot y} \cdot 0.3333333333333333\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -1.36e-70) (not (<= y 2.9e-42)))
   (+ x (* (/ -0.3333333333333333 z) y))
   (* (/ t (* z y)) 0.3333333333333333)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.36e-70) || !(y <= 2.9e-42)) {
		tmp = x + ((-0.3333333333333333 / z) * y);
	} else {
		tmp = (t / (z * y)) * 0.3333333333333333;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-1.36d-70)) .or. (.not. (y <= 2.9d-42))) then
        tmp = x + (((-0.3333333333333333d0) / z) * y)
    else
        tmp = (t / (z * y)) * 0.3333333333333333d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -1.36e-70) || !(y <= 2.9e-42)) {
		tmp = x + ((-0.3333333333333333 / z) * y);
	} else {
		tmp = (t / (z * y)) * 0.3333333333333333;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -1.36e-70) or not (y <= 2.9e-42):
		tmp = x + ((-0.3333333333333333 / z) * y)
	else:
		tmp = (t / (z * y)) * 0.3333333333333333
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -1.36e-70) || !(y <= 2.9e-42))
		tmp = Float64(x + Float64(Float64(-0.3333333333333333 / z) * y));
	else
		tmp = Float64(Float64(t / Float64(z * y)) * 0.3333333333333333);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -1.36e-70) || ~((y <= 2.9e-42)))
		tmp = x + ((-0.3333333333333333 / z) * y);
	else
		tmp = (t / (z * y)) * 0.3333333333333333;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -1.36e-70], N[Not[LessEqual[y, 2.9e-42]], $MachinePrecision]], N[(x + N[(N[(-0.3333333333333333 / z), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(N[(t / N[(z * y), $MachinePrecision]), $MachinePrecision] * 0.3333333333333333), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.36000000000000001e-70 or 2.9000000000000003e-42 < y

   1. Initial program 98.3%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. fp-cancel-sub-sign-inv N/A

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

      2. +-commutative N/A

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

      3. associate-+l+ N/A

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

      4. distribute-rgt-in N/A

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

      5. associate-*l/ N/A

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

      6. associate-/l* N/A

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

      7. *-inverses N/A

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

      8. *-rgt-identity N/A

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

      9. lower-+.f64 N/A

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

      10. lower-*.f64 N/A

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

   5. Applied rewrites 99.8%

      \[\leadsto \color{blue}{x + \frac{\mathsf{fma}\left(\frac{0.3333333333333333}{y \cdot y}, t, -0.3333333333333333\right)}{z} \cdot y} \]

   6. Taylor expanded in y around inf

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

   8. Applied rewrites 89.4%

      \[\leadsto x + \frac{-0.3333333333333333}{z} \cdot y \]

   if -1.36000000000000001e-70 < y < 2.9000000000000003e-42

   1. Initial program 93.4%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. *-commutative N/A

         \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{t}{y \cdot z} \cdot \frac{1}{3}} \]

      3. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{t}{y \cdot z}} \cdot \frac{1}{3} \]

      4. *-commutative N/A

         \[\leadsto \frac{t}{\color{blue}{z \cdot y}} \cdot \frac{1}{3} \]

      5. lower-*.f64 65.3

         \[\leadsto \frac{t}{\color{blue}{z \cdot y}} \cdot 0.3333333333333333 \]

   5. Applied rewrites 65.3%

      \[\leadsto \color{blue}{\frac{t}{z \cdot y} \cdot 0.3333333333333333} \]
3. Recombined 2 regimes into one program.

4. Final simplification 78.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.36 \cdot 10^{-70} \lor \neg \left(y \leq 2.9 \cdot 10^{-42}\right):\\ \;\;\;\;x + \frac{-0.3333333333333333}{z} \cdot y\\ \mathbf{else}:\\ \;\;\;\;\frac{t}{z \cdot y} \cdot 0.3333333333333333\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 64.0% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ x + \frac{-0.3333333333333333}{z} \cdot y \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (+ x (* (/ -0.3333333333333333 z) y)))

C

double code(double x, double y, double z, double t) {
	return x + ((-0.3333333333333333 / z) * y);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + (((-0.3333333333333333d0) / z) * y)
end function

Java

public static double code(double x, double y, double z, double t) {
	return x + ((-0.3333333333333333 / z) * y);
}

Python

def code(x, y, z, t):
	return x + ((-0.3333333333333333 / z) * y)

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(-0.3333333333333333 / z) * y))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((-0.3333333333333333 / z) * y);
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(-0.3333333333333333 / z), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 96.0%

   \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
2. Add Preprocessing

3. Taylor expanded in y around inf

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

   1. fp-cancel-sub-sign-inv N/A

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

   2. +-commutative N/A

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

   3. associate-+l+ N/A

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

   4. distribute-rgt-in N/A

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

   5. associate-*l/ N/A

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

   6. associate-/l* N/A

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

   7. *-inverses N/A

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

   8. *-rgt-identity N/A

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

   9. lower-+.f64 N/A

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

   10. lower-*.f64 N/A

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

5. Applied rewrites 78.3%

   \[\leadsto \color{blue}{x + \frac{\mathsf{fma}\left(\frac{0.3333333333333333}{y \cdot y}, t, -0.3333333333333333\right)}{z} \cdot y} \]

6. Taylor expanded in y around inf

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

8. Applied rewrites 62.9%

   \[\leadsto x + \frac{-0.3333333333333333}{z} \cdot y \]
9. Add Preprocessing

Alternative 11: 63.9% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(-0.3333333333333333, \frac{y}{z}, x\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (fma -0.3333333333333333 (/ y z) x))

C

double code(double x, double y, double z, double t) {
	return fma(-0.3333333333333333, (y / z), x);
}

Julia

function code(x, y, z, t)
	return fma(-0.3333333333333333, Float64(y / z), x)
end

Wolfram

code[x_, y_, z_, t_] := N[(-0.3333333333333333 * N[(y / z), $MachinePrecision] + x), $MachinePrecision]

Derivation

1. Initial program 96.0%

   \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
2. Add Preprocessing

3. Taylor expanded in y around inf

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

   1. fp-cancel-sub-sign-inv N/A

      \[\leadsto y \cdot \color{blue}{\left(\frac{x}{y} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \frac{1}{z}\right)} \]

   2. *-lft-identity N/A

      \[\leadsto y \cdot \left(\color{blue}{1 \cdot \frac{x}{y}} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \frac{1}{z}\right) \]

   3. metadata-eval N/A

      \[\leadsto y \cdot \left(\color{blue}{\left(\mathsf{neg}\left(-1\right)\right)} \cdot \frac{x}{y} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \frac{1}{z}\right) \]

   4. distribute-lft-neg-out N/A

      \[\leadsto y \cdot \left(\color{blue}{\left(\mathsf{neg}\left(-1 \cdot \frac{x}{y}\right)\right)} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \frac{1}{z}\right) \]

   5. *-commutative N/A

      \[\leadsto y \cdot \left(\left(\mathsf{neg}\left(\color{blue}{\frac{x}{y} \cdot -1}\right)\right) + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \frac{1}{z}\right) \]

   6. distribute-lft-neg-out N/A

      \[\leadsto y \cdot \left(\color{blue}{\left(\mathsf{neg}\left(\frac{x}{y}\right)\right) \cdot -1} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \frac{1}{z}\right) \]

   7. mul-1-neg N/A

      \[\leadsto y \cdot \left(\color{blue}{\left(-1 \cdot \frac{x}{y}\right)} \cdot -1 + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right) \cdot \frac{1}{z}\right) \]

   8. distribute-lft-neg-out N/A

      \[\leadsto y \cdot \left(\left(-1 \cdot \frac{x}{y}\right) \cdot -1 + \color{blue}{\left(\mathsf{neg}\left(\frac{1}{3} \cdot \frac{1}{z}\right)\right)}\right) \]

   9. *-rgt-identity N/A

      \[\leadsto y \cdot \left(\left(-1 \cdot \frac{x}{y}\right) \cdot -1 + \left(\mathsf{neg}\left(\color{blue}{\left(\frac{1}{3} \cdot \frac{1}{z}\right) \cdot 1}\right)\right)\right) \]

   10. distribute-rgt-neg-in N/A

       \[\leadsto y \cdot \left(\left(-1 \cdot \frac{x}{y}\right) \cdot -1 + \color{blue}{\left(\frac{1}{3} \cdot \frac{1}{z}\right) \cdot \left(\mathsf{neg}\left(1\right)\right)}\right) \]

   11. metadata-eval N/A

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

   12. distribute-rgt-in N/A

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

   13. associate-*l* N/A

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

   14. *-commutative N/A

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

   15. mul-1-neg N/A

       \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y\right)\right)} \cdot \left(-1 \cdot \frac{x}{y} + \frac{1}{3} \cdot \frac{1}{z}\right) \]

   16. distribute-lft-neg-in N/A

       \[\leadsto \color{blue}{\mathsf{neg}\left(y \cdot \left(-1 \cdot \frac{x}{y} + \frac{1}{3} \cdot \frac{1}{z}\right)\right)} \]

   17. mul-1-neg N/A

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

   18. associate-*r* N/A

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

   19. +-commutative N/A

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

   20. distribute-lft-in N/A

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

5. Applied rewrites 62.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left(-0.3333333333333333, \frac{y}{z}, x\right)} \]
6. Add Preprocessing

Alternative 12: 36.2% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \frac{-0.3333333333333333 \cdot y}{z} \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (/ (* -0.3333333333333333 y) z))

C

double code(double x, double y, double z, double t) {
	return (-0.3333333333333333 * y) / z;
}

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 = ((-0.3333333333333333d0) * y) / z
end function

Java

public static double code(double x, double y, double z, double t) {
	return (-0.3333333333333333 * y) / z;
}

Python

def code(x, y, z, t):
	return (-0.3333333333333333 * y) / z

Julia

function code(x, y, z, t)
	return Float64(Float64(-0.3333333333333333 * y) / z)
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (-0.3333333333333333 * y) / z;
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(-0.3333333333333333 * y), $MachinePrecision] / z), $MachinePrecision]

Derivation

1. Initial program 96.0%

   \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. distribute-lft-out-- N/A

      \[\leadsto \color{blue}{\frac{1}{3} \cdot \left(\frac{t}{y \cdot z} - \frac{y}{z}\right)} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{t}{y \cdot z} - \frac{y}{z}\right) \cdot \frac{1}{3}} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{t}{y \cdot z} - \frac{y}{z}\right) \cdot \frac{1}{3}} \]

   4. associate-/r* N/A

      \[\leadsto \left(\color{blue}{\frac{\frac{t}{y}}{z}} - \frac{y}{z}\right) \cdot \frac{1}{3} \]

   5. div-sub N/A

      \[\leadsto \color{blue}{\frac{\frac{t}{y} - y}{z}} \cdot \frac{1}{3} \]

   6. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\frac{t}{y} - y}{z}} \cdot \frac{1}{3} \]

   7. lower--.f64 N/A

      \[\leadsto \frac{\color{blue}{\frac{t}{y} - y}}{z} \cdot \frac{1}{3} \]

   8. lower-/.f64 67.6

      \[\leadsto \frac{\color{blue}{\frac{t}{y}} - y}{z} \cdot 0.3333333333333333 \]

5. Applied rewrites 67.6%

   \[\leadsto \color{blue}{\frac{\frac{t}{y} - y}{z} \cdot 0.3333333333333333} \]

6. Taylor expanded in y around inf

   \[\leadsto \frac{-1 \cdot y}{z} \cdot \frac{1}{3} \]
7. Step-by-step derivation

8. Applied rewrites 34.0%

   \[\leadsto \frac{-y}{z} \cdot 0.3333333333333333 \]
9. Step-by-step derivation

10. Applied rewrites 34.0%

    \[\leadsto \frac{0.3333333333333333 \cdot \left(-y\right)}{\color{blue}{z}} \]

11. Taylor expanded in y around inf

    \[\leadsto \frac{\frac{-1}{3} \cdot y}{z} \]
12. Step-by-step derivation

13. Applied rewrites 34.0%

    \[\leadsto \frac{-0.3333333333333333 \cdot y}{z} \]
14. Add Preprocessing

Developer Target 1: 95.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \left(x - \frac{y}{z \cdot 3}\right) + \frac{\frac{t}{z \cdot 3}}{y} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- x (/ y (* z 3.0))) (/ (/ t (* z 3.0)) y)))

C

double code(double x, double y, double z, double t) {
	return (x - (y / (z * 3.0))) + ((t / (z * 3.0)) / y);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x - (y / (z * 3.0d0))) + ((t / (z * 3.0d0)) / y)
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x - (y / (z * 3.0))) + ((t / (z * 3.0)) / y);
}

Python

def code(x, y, z, t):
	return (x - (y / (z * 3.0))) + ((t / (z * 3.0)) / y)

Julia

function code(x, y, z, t)
	return Float64(Float64(x - Float64(y / Float64(z * 3.0))) + Float64(Float64(t / Float64(z * 3.0)) / y))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x - (y / (z * 3.0))) + ((t / (z * 3.0)) / y);
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x - N[(y / N[(z * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t / N[(z * 3.0), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2025016 
(FPCore (x y z t)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, H"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (- x (/ y (* z 3))) (/ (/ t (* z 3)) y)))

  (+ (- x (/ y (* z 3.0))) (/ t (* (* z 3.0) y))))