Data.Colour.RGBSpace.HSV:hsv from colour-2.3.3, I

Percentage Accurate: 96.0% → 97.8%

Time: 5.1s

Alternatives: 6

Speedup: 0.5×

Specification

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return x * (1.0 - (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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x * (1.0d0 - (y * z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 96.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return x * (1.0 - (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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x * (1.0d0 - (y * z))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 97.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;1 - y \cdot z \leq -5 \cdot 10^{+234}:\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;x - \left(z \cdot y\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= (- 1.0 (* y z)) -5e+234) (* (* (- x) y) z) (- x (* (* z y) x))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((1.0 - (y * z)) <= -5e+234) {
		tmp = (-x * y) * z;
	} else {
		tmp = x - ((z * y) * x);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((1.0d0 - (y * z)) <= (-5d+234)) then
        tmp = (-x * y) * z
    else
        tmp = x - ((z * y) * x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((1.0 - (y * z)) <= -5e+234) {
		tmp = (-x * y) * z;
	} else {
		tmp = x - ((z * y) * x);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (1.0 - (y * z)) <= -5e+234:
		tmp = (-x * y) * z
	else:
		tmp = x - ((z * y) * x)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (Float64(1.0 - Float64(y * z)) <= -5e+234)
		tmp = Float64(Float64(Float64(-x) * y) * z);
	else
		tmp = Float64(x - Float64(Float64(z * y) * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((1.0 - (y * z)) <= -5e+234)
		tmp = (-x * y) * z;
	else
		tmp = x - ((z * y) * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[N[(1.0 - N[(y * z), $MachinePrecision]), $MachinePrecision], -5e+234], N[(N[((-x) * y), $MachinePrecision] * z), $MachinePrecision], N[(x - N[(N[(z * y), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 #s(literal 1 binary64) (*.f64 y z)) < -5.0000000000000003e234

   1. Initial program 64.1%

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

   3. Taylor expanded in y around 0

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

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

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

      2. metadata-eval N/A

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

      3. *-lft-identity N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 64.1

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

   5. Applied rewrites 64.1%

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

   6. Taylor expanded in y around inf

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

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

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

      2. *-lft-identity N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      3. *-commutative N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      4. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      6. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      8. *-commutative N/A

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

      9. associate-*r* N/A

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

      10. associate-*l* N/A

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

      11. lower-*.f64 N/A

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

      12. mul-1-neg N/A

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

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

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

      14. lift-*.f64 N/A

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

      15. lift-neg.f64 99.8

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

   8. Applied rewrites 99.8%

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

      1. lift-*.f64 N/A

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

      2. lift-neg.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. mul-1-neg N/A

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

      6. *-commutative N/A

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

      7. associate-*r* N/A

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

      8. associate-*r* N/A

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

      9. associate-*r* N/A

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

      10. lower-*.f64 N/A

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

      11. associate-*r* N/A

          \[\leadsto \left(\left(-1 \cdot x\right) \cdot y\right) \cdot z \]

      12. mul-1-neg N/A

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

      13. lower-*.f64 N/A

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

      14. lift-neg.f64 99.9

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

   10. Applied rewrites 99.9%

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

   if -5.0000000000000003e234 < (-.f64 #s(literal 1 binary64) (*.f64 y z))

   1. Initial program 98.3%

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

   3. Taylor expanded in y around 0

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

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

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

      2. metadata-eval N/A

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

      3. *-lft-identity N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 98.3

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

   5. Applied rewrites 98.3%

      \[\leadsto \color{blue}{x - \left(z \cdot y\right) \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;1 - y \cdot z \leq -5 \cdot 10^{+234}:\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;x - \left(z \cdot y\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 96.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\left(-x\right) \cdot y\right) \cdot z\\ \mathbf{if}\;y \cdot z \leq -2000:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \cdot z \leq 0.05:\\ \;\;\;\;x\\ \mathbf{elif}\;y \cdot z \leq 2 \cdot 10^{+234}:\\ \;\;\;\;x \cdot \left(\left(-y\right) \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (* (- x) y) z)))
   (if (<= (* y z) -2000.0)
     t_0
     (if (<= (* y z) 0.05) x (if (<= (* y z) 2e+234) (* x (* (- y) z)) t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = (-x * y) * z;
	double tmp;
	if ((y * z) <= -2000.0) {
		tmp = t_0;
	} else if ((y * z) <= 0.05) {
		tmp = x;
	} else if ((y * z) <= 2e+234) {
		tmp = x * (-y * z);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (-x * y) * z
    if ((y * z) <= (-2000.0d0)) then
        tmp = t_0
    else if ((y * z) <= 0.05d0) then
        tmp = x
    else if ((y * z) <= 2d+234) then
        tmp = x * (-y * z)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (-x * y) * z;
	double tmp;
	if ((y * z) <= -2000.0) {
		tmp = t_0;
	} else if ((y * z) <= 0.05) {
		tmp = x;
	} else if ((y * z) <= 2e+234) {
		tmp = x * (-y * z);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (-x * y) * z
	tmp = 0
	if (y * z) <= -2000.0:
		tmp = t_0
	elif (y * z) <= 0.05:
		tmp = x
	elif (y * z) <= 2e+234:
		tmp = x * (-y * z)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(-x) * y) * z)
	tmp = 0.0
	if (Float64(y * z) <= -2000.0)
		tmp = t_0;
	elseif (Float64(y * z) <= 0.05)
		tmp = x;
	elseif (Float64(y * z) <= 2e+234)
		tmp = Float64(x * Float64(Float64(-y) * z));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (-x * y) * z;
	tmp = 0.0;
	if ((y * z) <= -2000.0)
		tmp = t_0;
	elseif ((y * z) <= 0.05)
		tmp = x;
	elseif ((y * z) <= 2e+234)
		tmp = x * (-y * z);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[((-x) * y), $MachinePrecision] * z), $MachinePrecision]}, If[LessEqual[N[(y * z), $MachinePrecision], -2000.0], t$95$0, If[LessEqual[N[(y * z), $MachinePrecision], 0.05], x, If[LessEqual[N[(y * z), $MachinePrecision], 2e+234], N[(x * N[((-y) * z), $MachinePrecision]), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 y z) < -2e3 or 2.00000000000000004e234 < (*.f64 y z)

   1. Initial program 86.1%

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

   3. Taylor expanded in y around 0

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

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

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

      2. metadata-eval N/A

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

      3. *-lft-identity N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 86.1

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

   5. Applied rewrites 86.1%

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

   6. Taylor expanded in y around inf

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

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

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

      2. *-lft-identity N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      3. *-commutative N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      4. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      6. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      8. *-commutative N/A

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

      9. associate-*r* N/A

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

      10. associate-*l* N/A

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

      11. lower-*.f64 N/A

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

      12. mul-1-neg N/A

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

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

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

      14. lift-*.f64 N/A

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

      15. lift-neg.f64 93.0

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

   8. Applied rewrites 93.0%

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

      1. lift-*.f64 N/A

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

      2. lift-neg.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. mul-1-neg N/A

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

      6. *-commutative N/A

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

      7. associate-*r* N/A

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

      8. associate-*r* N/A

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

      9. associate-*r* N/A

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

      10. lower-*.f64 N/A

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

      11. associate-*r* N/A

          \[\leadsto \left(\left(-1 \cdot x\right) \cdot y\right) \cdot z \]

      12. mul-1-neg N/A

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

      13. lower-*.f64 N/A

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

      14. lift-neg.f64 94.7

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

   10. Applied rewrites 94.7%

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

   if -2e3 < (*.f64 y z) < 0.050000000000000003

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x} \]
   4. Step-by-step derivation

   5. Applied rewrites 96.0%

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

   if 0.050000000000000003 < (*.f64 y z) < 2.00000000000000004e234

   1. Initial program 99.5%

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

   3. Taylor expanded in y around inf

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

      1. associate-*r* N/A

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

      2. lower-*.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 95.3

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

   5. Applied rewrites 95.3%

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

4. Final simplification 95.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot z \leq -2000:\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot z\\ \mathbf{elif}\;y \cdot z \leq 0.05:\\ \;\;\;\;x\\ \mathbf{elif}\;y \cdot z \leq 2 \cdot 10^{+234}:\\ \;\;\;\;x \cdot \left(\left(-y\right) \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 94.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - y \cdot z\\ \mathbf{if}\;t\_0 \leq -100000:\\ \;\;\;\;\left(\left(-x\right) \cdot z\right) \cdot y\\ \mathbf{elif}\;t\_0 \leq 2:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- 1.0 (* y z))))
   (if (<= t_0 -100000.0)
     (* (* (- x) z) y)
     (if (<= t_0 2.0) x (* (* (- x) y) z)))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 - (y * z);
	double tmp;
	if (t_0 <= -100000.0) {
		tmp = (-x * z) * y;
	} else if (t_0 <= 2.0) {
		tmp = x;
	} else {
		tmp = (-x * y) * z;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - (y * z)
    if (t_0 <= (-100000.0d0)) then
        tmp = (-x * z) * y
    else if (t_0 <= 2.0d0) then
        tmp = x
    else
        tmp = (-x * y) * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 - (y * z);
	double tmp;
	if (t_0 <= -100000.0) {
		tmp = (-x * z) * y;
	} else if (t_0 <= 2.0) {
		tmp = x;
	} else {
		tmp = (-x * y) * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 - (y * z)
	tmp = 0
	if t_0 <= -100000.0:
		tmp = (-x * z) * y
	elif t_0 <= 2.0:
		tmp = x
	else:
		tmp = (-x * y) * z
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 - Float64(y * z))
	tmp = 0.0
	if (t_0 <= -100000.0)
		tmp = Float64(Float64(Float64(-x) * z) * y);
	elseif (t_0 <= 2.0)
		tmp = x;
	else
		tmp = Float64(Float64(Float64(-x) * y) * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 - (y * z);
	tmp = 0.0;
	if (t_0 <= -100000.0)
		tmp = (-x * z) * y;
	elseif (t_0 <= 2.0)
		tmp = x;
	else
		tmp = (-x * y) * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 - N[(y * z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -100000.0], N[(N[((-x) * z), $MachinePrecision] * y), $MachinePrecision], If[LessEqual[t$95$0, 2.0], x, N[(N[((-x) * y), $MachinePrecision] * z), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 #s(literal 1 binary64) (*.f64 y z)) < -1e5

   1. Initial program 86.2%

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

   3. Taylor expanded in y around 0

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

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

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

      2. metadata-eval N/A

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

      3. *-lft-identity N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 86.2

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

   5. Applied rewrites 86.2%

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

   6. Taylor expanded in y around inf

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

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

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

      2. *-lft-identity N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      3. *-commutative N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      4. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      6. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      8. *-commutative N/A

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

      9. associate-*r* N/A

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

      10. associate-*l* N/A

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

      11. lower-*.f64 N/A

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

      12. mul-1-neg N/A

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

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

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

      14. lift-*.f64 N/A

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

      15. lift-neg.f64 80.3

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

   8. Applied rewrites 80.3%

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

   if -1e5 < (-.f64 #s(literal 1 binary64) (*.f64 y z)) < 2

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x} \]
   4. Step-by-step derivation

   5. Applied rewrites 96.0%

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

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

   1. Initial program 93.7%

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

   3. Taylor expanded in y around 0

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

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

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

      2. metadata-eval N/A

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

      3. *-lft-identity N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 93.7

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

   5. Applied rewrites 93.7%

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

   6. Taylor expanded in y around inf

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

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

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

      2. *-lft-identity N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      3. *-commutative N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      4. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      6. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      8. *-commutative N/A

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

      9. associate-*r* N/A

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

      10. associate-*l* N/A

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

      11. lower-*.f64 N/A

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

      12. mul-1-neg N/A

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

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

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

      14. lift-*.f64 N/A

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

      15. lift-neg.f64 90.7

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

   8. Applied rewrites 90.7%

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

      1. lift-*.f64 N/A

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

      2. lift-neg.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. mul-1-neg N/A

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

      6. *-commutative N/A

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

      7. associate-*r* N/A

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

      8. associate-*r* N/A

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

      9. associate-*r* N/A

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

      10. lower-*.f64 N/A

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

      11. associate-*r* N/A

          \[\leadsto \left(\left(-1 \cdot x\right) \cdot y\right) \cdot z \]

      12. mul-1-neg N/A

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

      13. lower-*.f64 N/A

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

      14. lift-neg.f64 93.0

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

   10. Applied rewrites 93.0%

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

4. Final simplification 91.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;1 - y \cdot z \leq -100000:\\ \;\;\;\;\left(\left(-x\right) \cdot z\right) \cdot y\\ \mathbf{elif}\;1 - y \cdot z \leq 2:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 94.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot z \leq -2000 \lor \neg \left(y \cdot z \leq 0.05\right):\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= (* y z) -2000.0) (not (<= (* y z) 0.05))) (* (* (- x) y) z) x))

C

double code(double x, double y, double z) {
	double tmp;
	if (((y * z) <= -2000.0) || !((y * z) <= 0.05)) {
		tmp = (-x * y) * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (((y * z) <= (-2000.0d0)) .or. (.not. ((y * z) <= 0.05d0))) then
        tmp = (-x * y) * z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (((y * z) <= -2000.0) || !((y * z) <= 0.05)) {
		tmp = (-x * y) * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if ((y * z) <= -2000.0) or not ((y * z) <= 0.05):
		tmp = (-x * y) * z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((Float64(y * z) <= -2000.0) || !(Float64(y * z) <= 0.05))
		tmp = Float64(Float64(Float64(-x) * y) * z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (((y * z) <= -2000.0) || ~(((y * z) <= 0.05)))
		tmp = (-x * y) * z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[N[(y * z), $MachinePrecision], -2000.0], N[Not[LessEqual[N[(y * z), $MachinePrecision], 0.05]], $MachinePrecision]], N[(N[((-x) * y), $MachinePrecision] * z), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y z) < -2e3 or 0.050000000000000003 < (*.f64 y z)

   1. Initial program 90.1%

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

   3. Taylor expanded in y around 0

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

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

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

      2. metadata-eval N/A

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

      3. *-lft-identity N/A

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

      4. lower--.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 90.1

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

   5. Applied rewrites 90.1%

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

   6. Taylor expanded in y around inf

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

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

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

      2. *-lft-identity N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      3. *-commutative N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      4. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      6. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \left(x \cdot \left(y \cdot z\right)\right) \]

      8. *-commutative N/A

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

      9. associate-*r* N/A

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

      10. associate-*l* N/A

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

      11. lower-*.f64 N/A

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

      12. mul-1-neg N/A

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

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

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

      14. lift-*.f64 N/A

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

      15. lift-neg.f64 85.7

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

   8. Applied rewrites 85.7%

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

      1. lift-*.f64 N/A

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

      2. lift-neg.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. mul-1-neg N/A

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

      6. *-commutative N/A

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

      7. associate-*r* N/A

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

      8. associate-*r* N/A

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

      9. associate-*r* N/A

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

      10. lower-*.f64 N/A

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

      11. associate-*r* N/A

          \[\leadsto \left(\left(-1 \cdot x\right) \cdot y\right) \cdot z \]

      12. mul-1-neg N/A

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

      13. lower-*.f64 N/A

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

      14. lift-neg.f64 92.6

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

   10. Applied rewrites 92.6%

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

   if -2e3 < (*.f64 y z) < 0.050000000000000003

   1. Initial program 100.0%

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

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x} \]
   4. Step-by-step derivation

   5. Applied rewrites 96.0%

      \[\leadsto \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 94.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot z \leq -2000 \lor \neg \left(y \cdot z \leq 0.05\right):\\ \;\;\;\;\left(\left(-x\right) \cdot y\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 94.1% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (- x (* (* y x) z)))

C

double code(double x, double y, double z) {
	return x - ((y * x) * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = x - ((y * x) * z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 95.5%

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

3. Taylor expanded in y around 0

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

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

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

   2. metadata-eval N/A

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

   3. *-lft-identity N/A

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

   4. lower--.f64 N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 95.5

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

5. Applied rewrites 95.5%

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

   1. lift-*.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. *-commutative N/A

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

   4. *-commutative N/A

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

   5. associate-*r* N/A

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

   6. lower-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 95.0

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

7. Applied rewrites 95.0%

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

8. Final simplification 95.0%

   \[\leadsto x - \left(y \cdot x\right) \cdot z \]
9. Add Preprocessing

Alternative 6: 50.9% accurate, 14.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 95.5%

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

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{x} \]
4. Step-by-step derivation

5. Applied rewrites 53.8%

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

6. Final simplification 53.8%

   \[\leadsto x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2025051 
(FPCore (x y z)
  :name "Data.Colour.RGBSpace.HSV:hsv from colour-2.3.3, I"
  :precision binary64
  (* x (- 1.0 (* y z))))