Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, D

Percentage Accurate: 99.5% → 99.8%

Time: 8.7s

Alternatives: 12

Speedup: 1.3×

• 20.2% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ x (* (* (- y x) 6.0) (- (/ 2.0 3.0) z))))

C

double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - 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) * 6.0d0) * ((2.0d0 / 3.0d0) - z))
end function

Java

public static double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
}

Python

def code(x, y, z):
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z))

Julia

function code(x, y, z)
	return Float64(x + Float64(Float64(Float64(y - x) * 6.0) * Float64(Float64(2.0 / 3.0) - z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
end

Wolfram

code[x_, y_, z_] := N[(x + N[(N[(N[(y - x), $MachinePrecision] * 6.0), $MachinePrecision] * N[(N[(2.0 / 3.0), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ x (* (* (- y x) 6.0) (- (/ 2.0 3.0) z))))

C

double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - 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) * 6.0d0) * ((2.0d0 / 3.0d0) - z))
end function

Java

public static double code(double x, double y, double z) {
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
}

Python

def code(x, y, z):
	return x + (((y - x) * 6.0) * ((2.0 / 3.0) - z))

Julia

function code(x, y, z)
	return Float64(x + Float64(Float64(Float64(y - x) * 6.0) * Float64(Float64(2.0 / 3.0) - z)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = x + (((y - x) * 6.0) * ((2.0 / 3.0) - z));
end

Wolfram

code[x_, y_, z_] := N[(x + N[(N[(N[(y - x), $MachinePrecision] * 6.0), $MachinePrecision] * N[(N[(2.0 / 3.0), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(z, 6, -4\right), x - y, x\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fma (fma z 6.0 -4.0) (- x y) x))

C

double code(double x, double y, double z) {
	return fma(fma(z, 6.0, -4.0), (x - y), x);
}

Julia

function code(x, y, z)
	return fma(fma(z, 6.0, -4.0), Float64(x - y), x)
end

Wolfram

code[x_, y_, z_] := N[(N[(z * 6.0 + -4.0), $MachinePrecision] * N[(x - y), $MachinePrecision] + x), $MachinePrecision]

Derivation

1. Initial program 99.5%

   \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

3. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, 6, -4\right), x - y, x\right)} \]
4. Add Preprocessing

Alternative 2: 97.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -0.58:\\ \;\;\;\;\left(\left(y - x\right) \cdot z\right) \cdot -6\\ \mathbf{elif}\;z \leq 0.6:\\ \;\;\;\;\mathsf{fma}\left(x, -3, 4 \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-6 \cdot \left(y - x\right)\right) \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -0.58)
   (* (* (- y x) z) -6.0)
   (if (<= z 0.6) (fma x -3.0 (* 4.0 y)) (* (* -6.0 (- y x)) z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -0.58) {
		tmp = ((y - x) * z) * -6.0;
	} else if (z <= 0.6) {
		tmp = fma(x, -3.0, (4.0 * y));
	} else {
		tmp = (-6.0 * (y - x)) * z;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -0.58)
		tmp = Float64(Float64(Float64(y - x) * z) * -6.0);
	elseif (z <= 0.6)
		tmp = fma(x, -3.0, Float64(4.0 * y));
	else
		tmp = Float64(Float64(-6.0 * Float64(y - x)) * z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -0.58], N[(N[(N[(y - x), $MachinePrecision] * z), $MachinePrecision] * -6.0), $MachinePrecision], If[LessEqual[z, 0.6], N[(x * -3.0 + N[(4.0 * y), $MachinePrecision]), $MachinePrecision], N[(N[(-6.0 * N[(y - x), $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -0.57999999999999996

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 97.5

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 97.5%

      \[\leadsto \color{blue}{\left(\left(y - x\right) \cdot z\right) \cdot -6} \]

   if -0.57999999999999996 < z < 0.599999999999999978

   1. Initial program 99.3%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 97.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]
   5. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - y, -4, x\right) \]

      2. lift-fma.f64 N/A

         \[\leadsto \left(x - y\right) \cdot -4 + \color{blue}{x} \]

      3. *-commutative N/A

         \[\leadsto -4 \cdot \left(x - y\right) + x \]

      4. +-commutative N/A

         \[\leadsto x + \color{blue}{-4 \cdot \left(x - y\right)} \]

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

         \[\leadsto x + \left(-4 \cdot x - \color{blue}{-4 \cdot y}\right) \]

      6. metadata-eval N/A

         \[\leadsto x + \left(-4 \cdot x - \left(\mathsf{neg}\left(4\right)\right) \cdot y\right) \]

      7. associate--l+ N/A

         \[\leadsto \left(x + -4 \cdot x\right) - \color{blue}{\left(\mathsf{neg}\left(4\right)\right) \cdot y} \]

      8. distribute-rgt1-in N/A

         \[\leadsto \left(-4 + 1\right) \cdot x - \color{blue}{\left(\mathsf{neg}\left(4\right)\right)} \cdot y \]

      9. metadata-eval N/A

         \[\leadsto -3 \cdot x - \left(\mathsf{neg}\left(\color{blue}{4}\right)\right) \cdot y \]

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

          \[\leadsto -3 \cdot x + \color{blue}{4 \cdot y} \]

      11. *-commutative N/A

          \[\leadsto x \cdot -3 + \color{blue}{4} \cdot y \]

      12. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(x, \color{blue}{-3}, 4 \cdot y\right) \]

      13. lower-*.f64 97.9

          \[\leadsto \mathsf{fma}\left(x, -3, 4 \cdot y\right) \]

   6. Applied rewrites 97.9%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{-3}, 4 \cdot y\right) \]

   if 0.599999999999999978 < z

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 97.6

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 97.6%

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

      1. lift-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      2. lift--.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot \color{blue}{-6} \]

      4. *-commutative N/A

         \[\leadsto -6 \cdot \color{blue}{\left(\left(y - x\right) \cdot z\right)} \]

      5. associate-*l* N/A

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \color{blue}{z} \]

      6. *-rgt-identity N/A

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(z \cdot \color{blue}{1}\right) \]

      7. metadata-eval N/A

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \]

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

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

      9. *-commutative N/A

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

      10. mul-1-neg N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

      11. lower-*.f64 N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right)} \]

      12. lower-*.f64 N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(z\right)\right)}\right)\right) \]

      13. lift--.f64 N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

      14. mul-1-neg N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

      15. *-commutative N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

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

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(z \cdot \color{blue}{\left(\mathsf{neg}\left(-1\right)\right)}\right) \]

      17. metadata-eval N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(z \cdot 1\right) \]

      18. *-rgt-identity 97.6

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot z \]

   6. Applied rewrites 97.6%

      \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \color{blue}{z} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 97.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{2}{3} - z\\ t_1 := \left(-6 \cdot \left(y - x\right)\right) \cdot z\\ \mathbf{if}\;t\_0 \leq -500000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;\mathsf{fma}\left(x, -3, 4 \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- (/ 2.0 3.0) z)) (t_1 (* (* -6.0 (- y x)) z)))
   (if (<= t_0 -500000.0) t_1 (if (<= t_0 1.0) (fma x -3.0 (* 4.0 y)) t_1))))

C

double code(double x, double y, double z) {
	double t_0 = (2.0 / 3.0) - z;
	double t_1 = (-6.0 * (y - x)) * z;
	double tmp;
	if (t_0 <= -500000.0) {
		tmp = t_1;
	} else if (t_0 <= 1.0) {
		tmp = fma(x, -3.0, (4.0 * y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(2.0 / 3.0) - z)
	t_1 = Float64(Float64(-6.0 * Float64(y - x)) * z)
	tmp = 0.0
	if (t_0 <= -500000.0)
		tmp = t_1;
	elseif (t_0 <= 1.0)
		tmp = fma(x, -3.0, Float64(4.0 * y));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(2.0 / 3.0), $MachinePrecision] - z), $MachinePrecision]}, Block[{t$95$1 = N[(N[(-6.0 * N[(y - x), $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision]}, If[LessEqual[t$95$0, -500000.0], t$95$1, If[LessEqual[t$95$0, 1.0], N[(x * -3.0 + N[(4.0 * y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z) < -5e5 or 1 < (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z)

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 98.3

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 98.3%

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

      1. lift-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      2. lift--.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot \color{blue}{-6} \]

      4. *-commutative N/A

         \[\leadsto -6 \cdot \color{blue}{\left(\left(y - x\right) \cdot z\right)} \]

      5. associate-*l* N/A

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \color{blue}{z} \]

      6. *-rgt-identity N/A

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(z \cdot \color{blue}{1}\right) \]

      7. metadata-eval N/A

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \]

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

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

      9. *-commutative N/A

         \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

      10. mul-1-neg N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

      11. lower-*.f64 N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right)} \]

      12. lower-*.f64 N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(z\right)\right)}\right)\right) \]

      13. lift--.f64 N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

      14. mul-1-neg N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

      15. *-commutative N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

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

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(z \cdot \color{blue}{\left(\mathsf{neg}\left(-1\right)\right)}\right) \]

      17. metadata-eval N/A

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \left(z \cdot 1\right) \]

      18. *-rgt-identity 98.3

          \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot z \]

   6. Applied rewrites 98.3%

      \[\leadsto \left(-6 \cdot \left(y - x\right)\right) \cdot \color{blue}{z} \]

   if -5e5 < (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z) < 1

   1. Initial program 99.3%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 96.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]
   5. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \mathsf{fma}\left(x - y, -4, x\right) \]

      2. lift-fma.f64 N/A

         \[\leadsto \left(x - y\right) \cdot -4 + \color{blue}{x} \]

      3. *-commutative N/A

         \[\leadsto -4 \cdot \left(x - y\right) + x \]

      4. +-commutative N/A

         \[\leadsto x + \color{blue}{-4 \cdot \left(x - y\right)} \]

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

         \[\leadsto x + \left(-4 \cdot x - \color{blue}{-4 \cdot y}\right) \]

      6. metadata-eval N/A

         \[\leadsto x + \left(-4 \cdot x - \left(\mathsf{neg}\left(4\right)\right) \cdot y\right) \]

      7. associate--l+ N/A

         \[\leadsto \left(x + -4 \cdot x\right) - \color{blue}{\left(\mathsf{neg}\left(4\right)\right) \cdot y} \]

      8. distribute-rgt1-in N/A

         \[\leadsto \left(-4 + 1\right) \cdot x - \color{blue}{\left(\mathsf{neg}\left(4\right)\right)} \cdot y \]

      9. metadata-eval N/A

         \[\leadsto -3 \cdot x - \left(\mathsf{neg}\left(\color{blue}{4}\right)\right) \cdot y \]

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

          \[\leadsto -3 \cdot x + \color{blue}{4 \cdot y} \]

      11. *-commutative N/A

          \[\leadsto x \cdot -3 + \color{blue}{4} \cdot y \]

      12. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(x, \color{blue}{-3}, 4 \cdot y\right) \]

      13. lower-*.f64 97.0

          \[\leadsto \mathsf{fma}\left(x, -3, 4 \cdot y\right) \]

   6. Applied rewrites 97.0%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{-3}, 4 \cdot y\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 75.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(-6, z, 4\right) \cdot y\\ \mathbf{if}\;y \leq -9 \cdot 10^{+57}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 7 \cdot 10^{-11}:\\ \;\;\;\;\mathsf{fma}\left(z, 6, -3\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (fma -6.0 z 4.0) y)))
   (if (<= y -9e+57) t_0 (if (<= y 7e-11) (* (fma z 6.0 -3.0) x) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fma(-6.0, z, 4.0) * y;
	double tmp;
	if (y <= -9e+57) {
		tmp = t_0;
	} else if (y <= 7e-11) {
		tmp = fma(z, 6.0, -3.0) * x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(fma(-6.0, z, 4.0) * y)
	tmp = 0.0
	if (y <= -9e+57)
		tmp = t_0;
	elseif (y <= 7e-11)
		tmp = Float64(fma(z, 6.0, -3.0) * x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(-6.0 * z + 4.0), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[y, -9e+57], t$95$0, If[LessEqual[y, 7e-11], N[(N[(z * 6.0 + -3.0), $MachinePrecision] * x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -8.99999999999999991e57 or 7.00000000000000038e-11 < y

   1. Initial program 99.6%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{6 \cdot \left(y \cdot \left(\frac{2}{3} - z\right)\right)} \]

   4. Applied rewrites 78.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-6, z, 4\right) \cdot y} \]

   if -8.99999999999999991e57 < y < 7.00000000000000038e-11

   1. Initial program 99.5%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(1 + -6 \cdot \left(\frac{2}{3} - z\right)\right)} \]

   4. Applied rewrites 72.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 6, -3\right) \cdot x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 75.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{2}{3} - z\\ t_1 := \mathsf{fma}\left(-6, z, 4\right) \cdot y\\ \mathbf{if}\;t\_0 \leq 0.666666666664:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;\mathsf{fma}\left(x - y, -4, x\right)\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{+252}:\\ \;\;\;\;\left(z \cdot 6\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- (/ 2.0 3.0) z)) (t_1 (* (fma -6.0 z 4.0) y)))
   (if (<= t_0 0.666666666664)
     t_1
     (if (<= t_0 1.0)
       (fma (- x y) -4.0 x)
       (if (<= t_0 5e+252) (* (* z 6.0) x) t_1)))))

C

double code(double x, double y, double z) {
	double t_0 = (2.0 / 3.0) - z;
	double t_1 = fma(-6.0, z, 4.0) * y;
	double tmp;
	if (t_0 <= 0.666666666664) {
		tmp = t_1;
	} else if (t_0 <= 1.0) {
		tmp = fma((x - y), -4.0, x);
	} else if (t_0 <= 5e+252) {
		tmp = (z * 6.0) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(2.0 / 3.0) - z)
	t_1 = Float64(fma(-6.0, z, 4.0) * y)
	tmp = 0.0
	if (t_0 <= 0.666666666664)
		tmp = t_1;
	elseif (t_0 <= 1.0)
		tmp = fma(Float64(x - y), -4.0, x);
	elseif (t_0 <= 5e+252)
		tmp = Float64(Float64(z * 6.0) * x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(2.0 / 3.0), $MachinePrecision] - z), $MachinePrecision]}, Block[{t$95$1 = N[(N[(-6.0 * z + 4.0), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[t$95$0, 0.666666666664], t$95$1, If[LessEqual[t$95$0, 1.0], N[(N[(x - y), $MachinePrecision] * -4.0 + x), $MachinePrecision], If[LessEqual[t$95$0, 5e+252], N[(N[(z * 6.0), $MachinePrecision] * x), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z) < 0.66666666666399998 or 4.9999999999999997e252 < (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z)

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{6 \cdot \left(y \cdot \left(\frac{2}{3} - z\right)\right)} \]

   4. Applied rewrites 53.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-6, z, 4\right) \cdot y} \]

   if 0.66666666666399998 < (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z) < 1

   1. Initial program 99.3%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 98.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   if 1 < (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z) < 4.9999999999999997e252

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   3. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, 6, -4\right), x - y, x\right)} \]

   4. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(6 \cdot z - 3\right)} \]
   5. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

      2. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      3. associate-+l- N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot x \]

      4. distribute-rgt1-in N/A

         \[\leadsto x + \color{blue}{\left(6 \cdot z - 4\right) \cdot x} \]

      5. *-commutative N/A

         \[\leadsto x + x \cdot \color{blue}{\left(6 \cdot z - 4\right)} \]

      6. *-commutative N/A

         \[\leadsto x + \left(6 \cdot z - 4\right) \cdot \color{blue}{x} \]

      7. distribute-rgt1-in N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot \color{blue}{x} \]

      8. associate-+l- N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      9. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot x \]

      10. lower-*.f64 N/A

          \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

   6. Applied rewrites 50.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 6, -3\right) \cdot x} \]

   7. Taylor expanded in z around inf

      \[\leadsto \left(6 \cdot z\right) \cdot x \]
   8. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(z \cdot 6\right) \cdot x \]

      2. *-rgt-identity N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot 6\right) \cdot x \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot 6\right) \cdot x \]

      4. *-rgt-identity 49.4

         \[\leadsto \left(z \cdot 6\right) \cdot x \]

   9. Applied rewrites 49.4%

      \[\leadsto \left(z \cdot 6\right) \cdot x \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 74.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{2}{3} - z\\ \mathbf{if}\;t\_0 \leq -500000:\\ \;\;\;\;\left(6 \cdot x\right) \cdot z\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;\mathsf{fma}\left(x - y, -4, x\right)\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{+252}:\\ \;\;\;\;\left(z \cdot 6\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\left(y \cdot -6\right) \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (- (/ 2.0 3.0) z)))
   (if (<= t_0 -500000.0)
     (* (* 6.0 x) z)
     (if (<= t_0 1.0)
       (fma (- x y) -4.0 x)
       (if (<= t_0 5e+252) (* (* z 6.0) x) (* (* y -6.0) z))))))

C

double code(double x, double y, double z) {
	double t_0 = (2.0 / 3.0) - z;
	double tmp;
	if (t_0 <= -500000.0) {
		tmp = (6.0 * x) * z;
	} else if (t_0 <= 1.0) {
		tmp = fma((x - y), -4.0, x);
	} else if (t_0 <= 5e+252) {
		tmp = (z * 6.0) * x;
	} else {
		tmp = (y * -6.0) * z;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(2.0 / 3.0) - z)
	tmp = 0.0
	if (t_0 <= -500000.0)
		tmp = Float64(Float64(6.0 * x) * z);
	elseif (t_0 <= 1.0)
		tmp = fma(Float64(x - y), -4.0, x);
	elseif (t_0 <= 5e+252)
		tmp = Float64(Float64(z * 6.0) * x);
	else
		tmp = Float64(Float64(y * -6.0) * z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(2.0 / 3.0), $MachinePrecision] - z), $MachinePrecision]}, If[LessEqual[t$95$0, -500000.0], N[(N[(6.0 * x), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[t$95$0, 1.0], N[(N[(x - y), $MachinePrecision] * -4.0 + x), $MachinePrecision], If[LessEqual[t$95$0, 5e+252], N[(N[(z * 6.0), $MachinePrecision] * x), $MachinePrecision], N[(N[(y * -6.0), $MachinePrecision] * z), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z) < -5e5

   1. Initial program 99.8%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   3. Applied rewrites 99.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, 6, -4\right), x - y, x\right)} \]

   4. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(6 \cdot z - 3\right)} \]
   5. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

      2. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      3. associate-+l- N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot x \]

      4. distribute-rgt1-in N/A

         \[\leadsto x + \color{blue}{\left(6 \cdot z - 4\right) \cdot x} \]

      5. *-commutative N/A

         \[\leadsto x + x \cdot \color{blue}{\left(6 \cdot z - 4\right)} \]

      6. *-commutative N/A

         \[\leadsto x + \left(6 \cdot z - 4\right) \cdot \color{blue}{x} \]

      7. distribute-rgt1-in N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot \color{blue}{x} \]

      8. associate-+l- N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      9. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot x \]

      10. lower-*.f64 N/A

          \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

   6. Applied rewrites 52.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 6, -3\right) \cdot x} \]

   7. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      3. *-commutative N/A

         \[\leadsto \left(z \cdot x\right) \cdot 6 \]

      4. *-rgt-identity N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot x\right) \cdot 6 \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot x\right) \cdot 6 \]

      6. *-rgt-identity 52.4

         \[\leadsto \left(z \cdot x\right) \cdot 6 \]

   9. Applied rewrites 52.4%

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

       1. lift-*.f64 N/A

          \[\leadsto \left(z \cdot x\right) \cdot 6 \]

       2. *-commutative N/A

          \[\leadsto 6 \cdot \left(z \cdot \color{blue}{x}\right) \]

       3. lift-*.f64 N/A

          \[\leadsto 6 \cdot \left(z \cdot x\right) \]

       4. *-commutative N/A

          \[\leadsto 6 \cdot \left(x \cdot z\right) \]

       5. associate-*r* N/A

          \[\leadsto \left(6 \cdot x\right) \cdot z \]

       6. *-rgt-identity N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot 1\right) \]

       7. metadata-eval N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \]

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

          \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

       9. *-commutative N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       10. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       11. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\left(\mathsf{neg}\left(-1\right)\right) \cdot z\right) \]

       13. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(1 \cdot z\right) \]

       14. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\left(\mathsf{neg}\left(-1\right)\right) \cdot z\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       16. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       17. lower-*.f64 N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       18. lower-*.f64 N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       19. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       20. *-commutative N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \]

       22. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot 1\right) \]

       23. *-rgt-identity 52.4

           \[\leadsto \left(6 \cdot x\right) \cdot z \]

   11. Applied rewrites 52.4%

       \[\leadsto \left(6 \cdot x\right) \cdot z \]

   if -5e5 < (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z) < 1

   1. Initial program 99.3%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 96.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   if 1 < (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z) < 4.9999999999999997e252

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   3. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, 6, -4\right), x - y, x\right)} \]

   4. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(6 \cdot z - 3\right)} \]
   5. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

      2. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      3. associate-+l- N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot x \]

      4. distribute-rgt1-in N/A

         \[\leadsto x + \color{blue}{\left(6 \cdot z - 4\right) \cdot x} \]

      5. *-commutative N/A

         \[\leadsto x + x \cdot \color{blue}{\left(6 \cdot z - 4\right)} \]

      6. *-commutative N/A

         \[\leadsto x + \left(6 \cdot z - 4\right) \cdot \color{blue}{x} \]

      7. distribute-rgt1-in N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot \color{blue}{x} \]

      8. associate-+l- N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      9. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot x \]

      10. lower-*.f64 N/A

          \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

   6. Applied rewrites 50.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 6, -3\right) \cdot x} \]

   7. Taylor expanded in z around inf

      \[\leadsto \left(6 \cdot z\right) \cdot x \]
   8. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(z \cdot 6\right) \cdot x \]

      2. *-rgt-identity N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot 6\right) \cdot x \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot 6\right) \cdot x \]

      4. *-rgt-identity 49.4

         \[\leadsto \left(z \cdot 6\right) \cdot x \]

   9. Applied rewrites 49.4%

      \[\leadsto \left(z \cdot 6\right) \cdot x \]

   if 4.9999999999999997e252 < (-.f64 (/.f64 #s(literal 2 binary64) #s(literal 3 binary64)) z)

   1. Initial program 99.9%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 99.9

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\left(y - x\right) \cdot z\right) \cdot -6} \]

   5. Taylor expanded in x around 0

      \[\leadsto \left(y \cdot z\right) \cdot -6 \]
   6. Step-by-step derivation

   7. Applied rewrites 58.6%

      \[\leadsto \left(y \cdot z\right) \cdot -6 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(y \cdot z\right) \cdot \color{blue}{-6} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(y \cdot z\right) \cdot -6 \]

      3. associate-*l* N/A

         \[\leadsto y \cdot \color{blue}{\left(z \cdot -6\right)} \]

      4. *-commutative N/A

         \[\leadsto y \cdot \left(-6 \cdot \color{blue}{z}\right) \]

      5. associate-*r* N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{z} \]

      6. *-rgt-identity N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \left(z \cdot \color{blue}{1}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{\left(z \cdot 1\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \left(\color{blue}{z} \cdot 1\right) \]

      9. *-rgt-identity 58.6

         \[\leadsto \left(y \cdot -6\right) \cdot z \]

   9. Applied rewrites 58.6%

      \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{z} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 7: 49.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7 \cdot 10^{+252}:\\ \;\;\;\;\left(y \cdot -6\right) \cdot z\\ \mathbf{elif}\;z \leq -6.6 \cdot 10^{-7}:\\ \;\;\;\;\left(z \cdot 6\right) \cdot x\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{-130}:\\ \;\;\;\;4 \cdot y\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{+16}:\\ \;\;\;\;-3 \cdot x\\ \mathbf{else}:\\ \;\;\;\;\left(6 \cdot x\right) \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -7e+252)
   (* (* y -6.0) z)
   (if (<= z -6.6e-7)
     (* (* z 6.0) x)
     (if (<= z 2.6e-130)
       (* 4.0 y)
       (if (<= z 5.2e+16) (* -3.0 x) (* (* 6.0 x) z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -7e+252) {
		tmp = (y * -6.0) * z;
	} else if (z <= -6.6e-7) {
		tmp = (z * 6.0) * x;
	} else if (z <= 2.6e-130) {
		tmp = 4.0 * y;
	} else if (z <= 5.2e+16) {
		tmp = -3.0 * x;
	} else {
		tmp = (6.0 * x) * 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) :: tmp
    if (z <= (-7d+252)) then
        tmp = (y * (-6.0d0)) * z
    else if (z <= (-6.6d-7)) then
        tmp = (z * 6.0d0) * x
    else if (z <= 2.6d-130) then
        tmp = 4.0d0 * y
    else if (z <= 5.2d+16) then
        tmp = (-3.0d0) * x
    else
        tmp = (6.0d0 * x) * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -7e+252) {
		tmp = (y * -6.0) * z;
	} else if (z <= -6.6e-7) {
		tmp = (z * 6.0) * x;
	} else if (z <= 2.6e-130) {
		tmp = 4.0 * y;
	} else if (z <= 5.2e+16) {
		tmp = -3.0 * x;
	} else {
		tmp = (6.0 * x) * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -7e+252:
		tmp = (y * -6.0) * z
	elif z <= -6.6e-7:
		tmp = (z * 6.0) * x
	elif z <= 2.6e-130:
		tmp = 4.0 * y
	elif z <= 5.2e+16:
		tmp = -3.0 * x
	else:
		tmp = (6.0 * x) * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -7e+252)
		tmp = Float64(Float64(y * -6.0) * z);
	elseif (z <= -6.6e-7)
		tmp = Float64(Float64(z * 6.0) * x);
	elseif (z <= 2.6e-130)
		tmp = Float64(4.0 * y);
	elseif (z <= 5.2e+16)
		tmp = Float64(-3.0 * x);
	else
		tmp = Float64(Float64(6.0 * x) * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -7e+252)
		tmp = (y * -6.0) * z;
	elseif (z <= -6.6e-7)
		tmp = (z * 6.0) * x;
	elseif (z <= 2.6e-130)
		tmp = 4.0 * y;
	elseif (z <= 5.2e+16)
		tmp = -3.0 * x;
	else
		tmp = (6.0 * x) * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -7e+252], N[(N[(y * -6.0), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[z, -6.6e-7], N[(N[(z * 6.0), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[z, 2.6e-130], N[(4.0 * y), $MachinePrecision], If[LessEqual[z, 5.2e+16], N[(-3.0 * x), $MachinePrecision], N[(N[(6.0 * x), $MachinePrecision] * z), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if z < -6.9999999999999999e252

   1. Initial program 99.9%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 99.9

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\left(y - x\right) \cdot z\right) \cdot -6} \]

   5. Taylor expanded in x around 0

      \[\leadsto \left(y \cdot z\right) \cdot -6 \]
   6. Step-by-step derivation

   7. Applied rewrites 58.6%

      \[\leadsto \left(y \cdot z\right) \cdot -6 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(y \cdot z\right) \cdot \color{blue}{-6} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(y \cdot z\right) \cdot -6 \]

      3. associate-*l* N/A

         \[\leadsto y \cdot \color{blue}{\left(z \cdot -6\right)} \]

      4. *-commutative N/A

         \[\leadsto y \cdot \left(-6 \cdot \color{blue}{z}\right) \]

      5. associate-*r* N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{z} \]

      6. *-rgt-identity N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \left(z \cdot \color{blue}{1}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{\left(z \cdot 1\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \left(\color{blue}{z} \cdot 1\right) \]

      9. *-rgt-identity 58.6

         \[\leadsto \left(y \cdot -6\right) \cdot z \]

   9. Applied rewrites 58.6%

      \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{z} \]

   if -6.9999999999999999e252 < z < -6.6000000000000003e-7

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   3. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, 6, -4\right), x - y, x\right)} \]

   4. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(6 \cdot z - 3\right)} \]
   5. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

      2. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      3. associate-+l- N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot x \]

      4. distribute-rgt1-in N/A

         \[\leadsto x + \color{blue}{\left(6 \cdot z - 4\right) \cdot x} \]

      5. *-commutative N/A

         \[\leadsto x + x \cdot \color{blue}{\left(6 \cdot z - 4\right)} \]

      6. *-commutative N/A

         \[\leadsto x + \left(6 \cdot z - 4\right) \cdot \color{blue}{x} \]

      7. distribute-rgt1-in N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot \color{blue}{x} \]

      8. associate-+l- N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      9. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot x \]

      10. lower-*.f64 N/A

          \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

   6. Applied rewrites 50.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 6, -3\right) \cdot x} \]

   7. Taylor expanded in z around inf

      \[\leadsto \left(6 \cdot z\right) \cdot x \]
   8. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(z \cdot 6\right) \cdot x \]

      2. *-rgt-identity N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot 6\right) \cdot x \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot 6\right) \cdot x \]

      4. *-rgt-identity 48.5

         \[\leadsto \left(z \cdot 6\right) \cdot x \]

   9. Applied rewrites 48.5%

      \[\leadsto \left(z \cdot 6\right) \cdot x \]

   if -6.6000000000000003e-7 < z < 2.6000000000000001e-130

   1. Initial program 99.3%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 99.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto 4 \cdot \color{blue}{y} \]
   6. Step-by-step derivation

      1. lower-*.f64 49.7

         \[\leadsto 4 \cdot y \]

   7. Applied rewrites 49.7%

      \[\leadsto 4 \cdot \color{blue}{y} \]

   if 2.6000000000000001e-130 < z < 5.2e16

   1. Initial program 99.2%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 84.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto -3 \cdot \color{blue}{x} \]
   6. Step-by-step derivation

      1. lower-*.f64 40.6

         \[\leadsto -3 \cdot x \]

   7. Applied rewrites 40.6%

      \[\leadsto -3 \cdot \color{blue}{x} \]

   if 5.2e16 < z

   1. Initial program 99.8%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   3. Applied rewrites 99.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, 6, -4\right), x - y, x\right)} \]

   4. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(6 \cdot z - 3\right)} \]
   5. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

      2. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      3. associate-+l- N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot x \]

      4. distribute-rgt1-in N/A

         \[\leadsto x + \color{blue}{\left(6 \cdot z - 4\right) \cdot x} \]

      5. *-commutative N/A

         \[\leadsto x + x \cdot \color{blue}{\left(6 \cdot z - 4\right)} \]

      6. *-commutative N/A

         \[\leadsto x + \left(6 \cdot z - 4\right) \cdot \color{blue}{x} \]

      7. distribute-rgt1-in N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot \color{blue}{x} \]

      8. associate-+l- N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      9. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot x \]

      10. lower-*.f64 N/A

          \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

   6. Applied rewrites 52.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 6, -3\right) \cdot x} \]

   7. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      3. *-commutative N/A

         \[\leadsto \left(z \cdot x\right) \cdot 6 \]

      4. *-rgt-identity N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot x\right) \cdot 6 \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot x\right) \cdot 6 \]

      6. *-rgt-identity 52.6

         \[\leadsto \left(z \cdot x\right) \cdot 6 \]

   9. Applied rewrites 52.6%

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

       1. lift-*.f64 N/A

          \[\leadsto \left(z \cdot x\right) \cdot 6 \]

       2. *-commutative N/A

          \[\leadsto 6 \cdot \left(z \cdot \color{blue}{x}\right) \]

       3. lift-*.f64 N/A

          \[\leadsto 6 \cdot \left(z \cdot x\right) \]

       4. *-commutative N/A

          \[\leadsto 6 \cdot \left(x \cdot z\right) \]

       5. associate-*r* N/A

          \[\leadsto \left(6 \cdot x\right) \cdot z \]

       6. *-rgt-identity N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot 1\right) \]

       7. metadata-eval N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \]

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

          \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

       9. *-commutative N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       10. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       11. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\left(\mathsf{neg}\left(-1\right)\right) \cdot z\right) \]

       13. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(1 \cdot z\right) \]

       14. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\left(\mathsf{neg}\left(-1\right)\right) \cdot z\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       16. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       17. lower-*.f64 N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       18. lower-*.f64 N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       19. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       20. *-commutative N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \]

       22. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot 1\right) \]

       23. *-rgt-identity 52.7

           \[\leadsto \left(6 \cdot x\right) \cdot z \]

   11. Applied rewrites 52.7%

       \[\leadsto \left(6 \cdot x\right) \cdot z \]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 8: 49.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(6 \cdot x\right) \cdot z\\ \mathbf{if}\;z \leq -7 \cdot 10^{+252}:\\ \;\;\;\;\left(y \cdot -6\right) \cdot z\\ \mathbf{elif}\;z \leq -6.6 \cdot 10^{-7}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{-130}:\\ \;\;\;\;4 \cdot y\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{+16}:\\ \;\;\;\;-3 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (* 6.0 x) z)))
   (if (<= z -7e+252)
     (* (* y -6.0) z)
     (if (<= z -6.6e-7)
       t_0
       (if (<= z 2.6e-130) (* 4.0 y) (if (<= z 5.2e+16) (* -3.0 x) t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = (6.0 * x) * z;
	double tmp;
	if (z <= -7e+252) {
		tmp = (y * -6.0) * z;
	} else if (z <= -6.6e-7) {
		tmp = t_0;
	} else if (z <= 2.6e-130) {
		tmp = 4.0 * y;
	} else if (z <= 5.2e+16) {
		tmp = -3.0 * x;
	} 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 = (6.0d0 * x) * z
    if (z <= (-7d+252)) then
        tmp = (y * (-6.0d0)) * z
    else if (z <= (-6.6d-7)) then
        tmp = t_0
    else if (z <= 2.6d-130) then
        tmp = 4.0d0 * y
    else if (z <= 5.2d+16) then
        tmp = (-3.0d0) * x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (6.0 * x) * z;
	double tmp;
	if (z <= -7e+252) {
		tmp = (y * -6.0) * z;
	} else if (z <= -6.6e-7) {
		tmp = t_0;
	} else if (z <= 2.6e-130) {
		tmp = 4.0 * y;
	} else if (z <= 5.2e+16) {
		tmp = -3.0 * x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (6.0 * x) * z
	tmp = 0
	if z <= -7e+252:
		tmp = (y * -6.0) * z
	elif z <= -6.6e-7:
		tmp = t_0
	elif z <= 2.6e-130:
		tmp = 4.0 * y
	elif z <= 5.2e+16:
		tmp = -3.0 * x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(6.0 * x) * z)
	tmp = 0.0
	if (z <= -7e+252)
		tmp = Float64(Float64(y * -6.0) * z);
	elseif (z <= -6.6e-7)
		tmp = t_0;
	elseif (z <= 2.6e-130)
		tmp = Float64(4.0 * y);
	elseif (z <= 5.2e+16)
		tmp = Float64(-3.0 * x);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (6.0 * x) * z;
	tmp = 0.0;
	if (z <= -7e+252)
		tmp = (y * -6.0) * z;
	elseif (z <= -6.6e-7)
		tmp = t_0;
	elseif (z <= 2.6e-130)
		tmp = 4.0 * y;
	elseif (z <= 5.2e+16)
		tmp = -3.0 * x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(6.0 * x), $MachinePrecision] * z), $MachinePrecision]}, If[LessEqual[z, -7e+252], N[(N[(y * -6.0), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[z, -6.6e-7], t$95$0, If[LessEqual[z, 2.6e-130], N[(4.0 * y), $MachinePrecision], If[LessEqual[z, 5.2e+16], N[(-3.0 * x), $MachinePrecision], t$95$0]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -6.9999999999999999e252

   1. Initial program 99.9%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 99.9

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\left(y - x\right) \cdot z\right) \cdot -6} \]

   5. Taylor expanded in x around 0

      \[\leadsto \left(y \cdot z\right) \cdot -6 \]
   6. Step-by-step derivation

   7. Applied rewrites 58.6%

      \[\leadsto \left(y \cdot z\right) \cdot -6 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(y \cdot z\right) \cdot \color{blue}{-6} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(y \cdot z\right) \cdot -6 \]

      3. associate-*l* N/A

         \[\leadsto y \cdot \color{blue}{\left(z \cdot -6\right)} \]

      4. *-commutative N/A

         \[\leadsto y \cdot \left(-6 \cdot \color{blue}{z}\right) \]

      5. associate-*r* N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{z} \]

      6. *-rgt-identity N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \left(z \cdot \color{blue}{1}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{\left(z \cdot 1\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \left(\color{blue}{z} \cdot 1\right) \]

      9. *-rgt-identity 58.6

         \[\leadsto \left(y \cdot -6\right) \cdot z \]

   9. Applied rewrites 58.6%

      \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{z} \]

   if -6.9999999999999999e252 < z < -6.6000000000000003e-7 or 5.2e16 < z

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   3. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(z, 6, -4\right), x - y, x\right)} \]

   4. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(6 \cdot z - 3\right)} \]
   5. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

      2. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      3. associate-+l- N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot x \]

      4. distribute-rgt1-in N/A

         \[\leadsto x + \color{blue}{\left(6 \cdot z - 4\right) \cdot x} \]

      5. *-commutative N/A

         \[\leadsto x + x \cdot \color{blue}{\left(6 \cdot z - 4\right)} \]

      6. *-commutative N/A

         \[\leadsto x + \left(6 \cdot z - 4\right) \cdot \color{blue}{x} \]

      7. distribute-rgt1-in N/A

         \[\leadsto \left(\left(6 \cdot z - 4\right) + 1\right) \cdot \color{blue}{x} \]

      8. associate-+l- N/A

         \[\leadsto \left(6 \cdot z - \left(4 - 1\right)\right) \cdot x \]

      9. metadata-eval N/A

         \[\leadsto \left(6 \cdot z - 3\right) \cdot x \]

      10. lower-*.f64 N/A

          \[\leadsto \left(6 \cdot z - 3\right) \cdot \color{blue}{x} \]

   6. Applied rewrites 51.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, 6, -3\right) \cdot x} \]

   7. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      3. *-commutative N/A

         \[\leadsto \left(z \cdot x\right) \cdot 6 \]

      4. *-rgt-identity N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot x\right) \cdot 6 \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\left(z \cdot 1\right) \cdot x\right) \cdot 6 \]

      6. *-rgt-identity 50.8

         \[\leadsto \left(z \cdot x\right) \cdot 6 \]

   9. Applied rewrites 50.8%

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

       1. lift-*.f64 N/A

          \[\leadsto \left(z \cdot x\right) \cdot 6 \]

       2. *-commutative N/A

          \[\leadsto 6 \cdot \left(z \cdot \color{blue}{x}\right) \]

       3. lift-*.f64 N/A

          \[\leadsto 6 \cdot \left(z \cdot x\right) \]

       4. *-commutative N/A

          \[\leadsto 6 \cdot \left(x \cdot z\right) \]

       5. associate-*r* N/A

          \[\leadsto \left(6 \cdot x\right) \cdot z \]

       6. *-rgt-identity N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot 1\right) \]

       7. metadata-eval N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \]

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

          \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

       9. *-commutative N/A

          \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       10. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       11. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\left(\mathsf{neg}\left(-1\right)\right) \cdot z\right) \]

       13. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(1 \cdot z\right) \]

       14. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\left(\mathsf{neg}\left(-1\right)\right) \cdot z\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       16. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       17. lower-*.f64 N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       18. lower-*.f64 N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \]

       19. mul-1-neg N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(-1 \cdot z\right)\right) \]

       20. *-commutative N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(\mathsf{neg}\left(z \cdot -1\right)\right) \]

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

           \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \]

       22. metadata-eval N/A

           \[\leadsto \left(6 \cdot x\right) \cdot \left(z \cdot 1\right) \]

       23. *-rgt-identity 50.8

           \[\leadsto \left(6 \cdot x\right) \cdot z \]

   11. Applied rewrites 50.8%

       \[\leadsto \left(6 \cdot x\right) \cdot z \]

   if -6.6000000000000003e-7 < z < 2.6000000000000001e-130

   1. Initial program 99.3%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 99.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto 4 \cdot \color{blue}{y} \]
   6. Step-by-step derivation

      1. lower-*.f64 49.7

         \[\leadsto 4 \cdot y \]

   7. Applied rewrites 49.7%

      \[\leadsto 4 \cdot \color{blue}{y} \]

   if 2.6000000000000001e-130 < z < 5.2e16

   1. Initial program 99.2%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 84.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto -3 \cdot \color{blue}{x} \]
   6. Step-by-step derivation

      1. lower-*.f64 40.6

         \[\leadsto -3 \cdot x \]

   7. Applied rewrites 40.6%

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

Alternative 9: 49.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(z \cdot x\right) \cdot 6\\ \mathbf{if}\;z \leq -7 \cdot 10^{+252}:\\ \;\;\;\;\left(y \cdot -6\right) \cdot z\\ \mathbf{elif}\;z \leq -6.6 \cdot 10^{-7}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{-130}:\\ \;\;\;\;4 \cdot y\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{+16}:\\ \;\;\;\;-3 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (* z x) 6.0)))
   (if (<= z -7e+252)
     (* (* y -6.0) z)
     (if (<= z -6.6e-7)
       t_0
       (if (<= z 2.6e-130) (* 4.0 y) (if (<= z 5.2e+16) (* -3.0 x) t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = (z * x) * 6.0;
	double tmp;
	if (z <= -7e+252) {
		tmp = (y * -6.0) * z;
	} else if (z <= -6.6e-7) {
		tmp = t_0;
	} else if (z <= 2.6e-130) {
		tmp = 4.0 * y;
	} else if (z <= 5.2e+16) {
		tmp = -3.0 * x;
	} 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 = (z * x) * 6.0d0
    if (z <= (-7d+252)) then
        tmp = (y * (-6.0d0)) * z
    else if (z <= (-6.6d-7)) then
        tmp = t_0
    else if (z <= 2.6d-130) then
        tmp = 4.0d0 * y
    else if (z <= 5.2d+16) then
        tmp = (-3.0d0) * x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (z * x) * 6.0;
	double tmp;
	if (z <= -7e+252) {
		tmp = (y * -6.0) * z;
	} else if (z <= -6.6e-7) {
		tmp = t_0;
	} else if (z <= 2.6e-130) {
		tmp = 4.0 * y;
	} else if (z <= 5.2e+16) {
		tmp = -3.0 * x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (z * x) * 6.0
	tmp = 0
	if z <= -7e+252:
		tmp = (y * -6.0) * z
	elif z <= -6.6e-7:
		tmp = t_0
	elif z <= 2.6e-130:
		tmp = 4.0 * y
	elif z <= 5.2e+16:
		tmp = -3.0 * x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z * x) * 6.0)
	tmp = 0.0
	if (z <= -7e+252)
		tmp = Float64(Float64(y * -6.0) * z);
	elseif (z <= -6.6e-7)
		tmp = t_0;
	elseif (z <= 2.6e-130)
		tmp = Float64(4.0 * y);
	elseif (z <= 5.2e+16)
		tmp = Float64(-3.0 * x);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (z * x) * 6.0;
	tmp = 0.0;
	if (z <= -7e+252)
		tmp = (y * -6.0) * z;
	elseif (z <= -6.6e-7)
		tmp = t_0;
	elseif (z <= 2.6e-130)
		tmp = 4.0 * y;
	elseif (z <= 5.2e+16)
		tmp = -3.0 * x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z * x), $MachinePrecision] * 6.0), $MachinePrecision]}, If[LessEqual[z, -7e+252], N[(N[(y * -6.0), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[z, -6.6e-7], t$95$0, If[LessEqual[z, 2.6e-130], N[(4.0 * y), $MachinePrecision], If[LessEqual[z, 5.2e+16], N[(-3.0 * x), $MachinePrecision], t$95$0]]]]]

Derivation

1. Split input into 4 regimes

2. if z < -6.9999999999999999e252

   1. Initial program 99.9%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 99.9

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 99.9%

      \[\leadsto \color{blue}{\left(\left(y - x\right) \cdot z\right) \cdot -6} \]

   5. Taylor expanded in x around 0

      \[\leadsto \left(y \cdot z\right) \cdot -6 \]
   6. Step-by-step derivation

   7. Applied rewrites 58.6%

      \[\leadsto \left(y \cdot z\right) \cdot -6 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(y \cdot z\right) \cdot \color{blue}{-6} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(y \cdot z\right) \cdot -6 \]

      3. associate-*l* N/A

         \[\leadsto y \cdot \color{blue}{\left(z \cdot -6\right)} \]

      4. *-commutative N/A

         \[\leadsto y \cdot \left(-6 \cdot \color{blue}{z}\right) \]

      5. associate-*r* N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{z} \]

      6. *-rgt-identity N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \left(z \cdot \color{blue}{1}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{\left(z \cdot 1\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \left(y \cdot -6\right) \cdot \left(\color{blue}{z} \cdot 1\right) \]

      9. *-rgt-identity 58.6

         \[\leadsto \left(y \cdot -6\right) \cdot z \]

   9. Applied rewrites 58.6%

      \[\leadsto \left(y \cdot -6\right) \cdot \color{blue}{z} \]

   if -6.9999999999999999e252 < z < -6.6000000000000003e-7 or 5.2e16 < z

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 97.7

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 97.7%

      \[\leadsto \color{blue}{\left(\left(y - x\right) \cdot z\right) \cdot -6} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      3. remove-double-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(x \cdot z\right)\right)\right)\right) \cdot 6 \]

      4. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(-1 \cdot \left(x \cdot z\right)\right)\right) \cdot 6 \]

      5. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(x \cdot z\right)\right)\right)\right) \cdot 6 \]

      6. *-commutative N/A

         \[\leadsto \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z \cdot x\right)\right)\right)\right) \cdot 6 \]

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

         \[\leadsto \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right) \cdot x\right)\right) \cdot 6 \]

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

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \cdot x\right) \cdot 6 \]

      9. lower-*.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \cdot x\right) \cdot 6 \]

      10. mul-1-neg N/A

          \[\leadsto \left(\left(\mathsf{neg}\left(-1 \cdot z\right)\right) \cdot x\right) \cdot 6 \]

      11. *-commutative N/A

          \[\leadsto \left(\left(\mathsf{neg}\left(z \cdot -1\right)\right) \cdot x\right) \cdot 6 \]

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

          \[\leadsto \left(\left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \cdot x\right) \cdot 6 \]

      13. metadata-eval N/A

          \[\leadsto \left(\left(z \cdot 1\right) \cdot x\right) \cdot 6 \]

      14. *-rgt-identity 50.8

          \[\leadsto \left(z \cdot x\right) \cdot 6 \]

   7. Applied rewrites 50.8%

      \[\leadsto \left(z \cdot x\right) \cdot \color{blue}{6} \]

   if -6.6000000000000003e-7 < z < 2.6000000000000001e-130

   1. Initial program 99.3%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 99.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto 4 \cdot \color{blue}{y} \]
   6. Step-by-step derivation

      1. lower-*.f64 49.7

         \[\leadsto 4 \cdot y \]

   7. Applied rewrites 49.7%

      \[\leadsto 4 \cdot \color{blue}{y} \]

   if 2.6000000000000001e-130 < z < 5.2e16

   1. Initial program 99.2%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 84.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto -3 \cdot \color{blue}{x} \]
   6. Step-by-step derivation

      1. lower-*.f64 40.6

         \[\leadsto -3 \cdot x \]

   7. Applied rewrites 40.6%

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

Alternative 10: 49.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(z \cdot x\right) \cdot 6\\ \mathbf{if}\;z \leq -6.6 \cdot 10^{-7}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{-130}:\\ \;\;\;\;4 \cdot y\\ \mathbf{elif}\;z \leq 5.2 \cdot 10^{+16}:\\ \;\;\;\;-3 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (* z x) 6.0)))
   (if (<= z -6.6e-7)
     t_0
     (if (<= z 2.6e-130) (* 4.0 y) (if (<= z 5.2e+16) (* -3.0 x) t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = (z * x) * 6.0;
	double tmp;
	if (z <= -6.6e-7) {
		tmp = t_0;
	} else if (z <= 2.6e-130) {
		tmp = 4.0 * y;
	} else if (z <= 5.2e+16) {
		tmp = -3.0 * x;
	} 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 = (z * x) * 6.0d0
    if (z <= (-6.6d-7)) then
        tmp = t_0
    else if (z <= 2.6d-130) then
        tmp = 4.0d0 * y
    else if (z <= 5.2d+16) then
        tmp = (-3.0d0) * x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (z * x) * 6.0;
	double tmp;
	if (z <= -6.6e-7) {
		tmp = t_0;
	} else if (z <= 2.6e-130) {
		tmp = 4.0 * y;
	} else if (z <= 5.2e+16) {
		tmp = -3.0 * x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (z * x) * 6.0
	tmp = 0
	if z <= -6.6e-7:
		tmp = t_0
	elif z <= 2.6e-130:
		tmp = 4.0 * y
	elif z <= 5.2e+16:
		tmp = -3.0 * x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(z * x) * 6.0)
	tmp = 0.0
	if (z <= -6.6e-7)
		tmp = t_0;
	elseif (z <= 2.6e-130)
		tmp = Float64(4.0 * y);
	elseif (z <= 5.2e+16)
		tmp = Float64(-3.0 * x);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (z * x) * 6.0;
	tmp = 0.0;
	if (z <= -6.6e-7)
		tmp = t_0;
	elseif (z <= 2.6e-130)
		tmp = 4.0 * y;
	elseif (z <= 5.2e+16)
		tmp = -3.0 * x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(z * x), $MachinePrecision] * 6.0), $MachinePrecision]}, If[LessEqual[z, -6.6e-7], t$95$0, If[LessEqual[z, 2.6e-130], N[(4.0 * y), $MachinePrecision], If[LessEqual[z, 5.2e+16], N[(-3.0 * x), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if z < -6.6000000000000003e-7 or 5.2e16 < z

   1. Initial program 99.7%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around inf

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

      1. *-commutative N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(z \cdot \left(y - x\right)\right) \cdot \color{blue}{-6} \]

      3. *-commutative N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

      5. lift--.f64 97.9

         \[\leadsto \left(\left(y - x\right) \cdot z\right) \cdot -6 \]

   4. Applied rewrites 97.9%

      \[\leadsto \color{blue}{\left(\left(y - x\right) \cdot z\right) \cdot -6} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      2. lower-*.f64 N/A

         \[\leadsto \left(x \cdot z\right) \cdot 6 \]

      3. remove-double-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(x \cdot z\right)\right)\right)\right) \cdot 6 \]

      4. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(-1 \cdot \left(x \cdot z\right)\right)\right) \cdot 6 \]

      5. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(x \cdot z\right)\right)\right)\right) \cdot 6 \]

      6. *-commutative N/A

         \[\leadsto \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z \cdot x\right)\right)\right)\right) \cdot 6 \]

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

         \[\leadsto \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right) \cdot x\right)\right) \cdot 6 \]

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

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \cdot x\right) \cdot 6 \]

      9. lower-*.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(z\right)\right)\right)\right) \cdot x\right) \cdot 6 \]

      10. mul-1-neg N/A

          \[\leadsto \left(\left(\mathsf{neg}\left(-1 \cdot z\right)\right) \cdot x\right) \cdot 6 \]

      11. *-commutative N/A

          \[\leadsto \left(\left(\mathsf{neg}\left(z \cdot -1\right)\right) \cdot x\right) \cdot 6 \]

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

          \[\leadsto \left(\left(z \cdot \left(\mathsf{neg}\left(-1\right)\right)\right) \cdot x\right) \cdot 6 \]

      13. metadata-eval N/A

          \[\leadsto \left(\left(z \cdot 1\right) \cdot x\right) \cdot 6 \]

      14. *-rgt-identity 51.0

          \[\leadsto \left(z \cdot x\right) \cdot 6 \]

   7. Applied rewrites 51.0%

      \[\leadsto \left(z \cdot x\right) \cdot \color{blue}{6} \]

   if -6.6000000000000003e-7 < z < 2.6000000000000001e-130

   1. Initial program 99.3%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 99.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto 4 \cdot \color{blue}{y} \]
   6. Step-by-step derivation

      1. lower-*.f64 49.7

         \[\leadsto 4 \cdot y \]

   7. Applied rewrites 49.7%

      \[\leadsto 4 \cdot \color{blue}{y} \]

   if 2.6000000000000001e-130 < z < 5.2e16

   1. Initial program 99.2%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 84.4%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto -3 \cdot \color{blue}{x} \]
   6. Step-by-step derivation

      1. lower-*.f64 40.6

         \[\leadsto -3 \cdot x \]

   7. Applied rewrites 40.6%

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

Alternative 11: 37.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.75 \cdot 10^{+75}:\\ \;\;\;\;4 \cdot y\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{-169}:\\ \;\;\;\;-3 \cdot x\\ \mathbf{else}:\\ \;\;\;\;4 \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.75e+75) (* 4.0 y) (if (<= y 1.35e-169) (* -3.0 x) (* 4.0 y))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.75e+75) {
		tmp = 4.0 * y;
	} else if (y <= 1.35e-169) {
		tmp = -3.0 * x;
	} else {
		tmp = 4.0 * 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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= (-1.75d+75)) then
        tmp = 4.0d0 * y
    else if (y <= 1.35d-169) then
        tmp = (-3.0d0) * x
    else
        tmp = 4.0d0 * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.75e+75) {
		tmp = 4.0 * y;
	} else if (y <= 1.35e-169) {
		tmp = -3.0 * x;
	} else {
		tmp = 4.0 * y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.75e+75:
		tmp = 4.0 * y
	elif y <= 1.35e-169:
		tmp = -3.0 * x
	else:
		tmp = 4.0 * y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.75e+75)
		tmp = Float64(4.0 * y);
	elseif (y <= 1.35e-169)
		tmp = Float64(-3.0 * x);
	else
		tmp = Float64(4.0 * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.75e+75)
		tmp = 4.0 * y;
	elseif (y <= 1.35e-169)
		tmp = -3.0 * x;
	else
		tmp = 4.0 * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.75e+75], N[(4.0 * y), $MachinePrecision], If[LessEqual[y, 1.35e-169], N[(-3.0 * x), $MachinePrecision], N[(4.0 * y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.7499999999999999e75 or 1.3500000000000001e-169 < y

   1. Initial program 99.5%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 51.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto 4 \cdot \color{blue}{y} \]
   6. Step-by-step derivation

      1. lower-*.f64 35.8

         \[\leadsto 4 \cdot y \]

   7. Applied rewrites 35.8%

      \[\leadsto 4 \cdot \color{blue}{y} \]

   if -1.7499999999999999e75 < y < 1.3500000000000001e-169

   1. Initial program 99.5%

      \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

   4. Applied rewrites 51.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto -3 \cdot \color{blue}{x} \]
   6. Step-by-step derivation

      1. lower-*.f64 39.3

         \[\leadsto -3 \cdot x \]

   7. Applied rewrites 39.3%

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

Alternative 12: 26.6% accurate, 4.6× speedup

Math

\[\begin{array}{l} \\ -3 \cdot x \end{array} \]

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := N[(-3.0 * x), $MachinePrecision]

Derivation

1. Initial program 99.5%

   \[x + \left(\left(y - x\right) \cdot 6\right) \cdot \left(\frac{2}{3} - z\right) \]

2. Taylor expanded in z around 0

   \[\leadsto \color{blue}{x + 4 \cdot \left(y - x\right)} \]

4. Applied rewrites 51.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x - y, -4, x\right)} \]

5. Taylor expanded in x around inf

   \[\leadsto -3 \cdot \color{blue}{x} \]
6. Step-by-step derivation

   1. lower-*.f64 26.6

      \[\leadsto -3 \cdot x \]

7. Applied rewrites 26.6%

   \[\leadsto -3 \cdot \color{blue}{x} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025130 
(FPCore (x y z)
  :name "Data.Colour.RGBSpace.HSL:hsl from colour-2.3.3, D"
  :precision binary64
  (+ x (* (* (- y x) 6.0) (- (/ 2.0 3.0) z))))