Statistics.Distribution.CauchyLorentz:$cdensity from math-functions-0.1.5.2

Percentage Accurate: 90.3% → 97.7%

Time: 3.7s

Alternatives: 8

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 90.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 97.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ [x, y, z_m] = \mathsf{sort}([x, y, z_m])\\ \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 9.4 \cdot 10^{+114}:\\ \;\;\;\;\frac{\frac{\frac{1}{x}}{\mathsf{fma}\left(z\_m, z\_m, 1\right)}}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{z\_m \cdot y}}{z\_m \cdot x}\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 9.4e+114)
   (/ (/ (/ 1.0 x) (fma z_m z_m 1.0)) y)
   (/ (/ 1.0 (* z_m y)) (* z_m x))))

C

z_m = fabs(z);
assert(x < y && y < z_m);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 9.4e+114) {
		tmp = ((1.0 / x) / fma(z_m, z_m, 1.0)) / y;
	} else {
		tmp = (1.0 / (z_m * y)) / (z_m * x);
	}
	return tmp;
}

Julia

z_m = abs(z)
x, y, z_m = sort([x, y, z_m])
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 9.4e+114)
		tmp = Float64(Float64(Float64(1.0 / x) / fma(z_m, z_m, 1.0)) / y);
	else
		tmp = Float64(Float64(1.0 / Float64(z_m * y)) / Float64(z_m * x));
	end
	return tmp
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 9.4e+114], N[(N[(N[(1.0 / x), $MachinePrecision] / N[(z$95$m * z$95$m + 1.0), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision], N[(N[(1.0 / N[(z$95$m * y), $MachinePrecision]), $MachinePrecision] / N[(z$95$m * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 9.4000000000000001e114

   1. Initial program 97.7%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. *-commutative N/A

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

      7. associate-/r* N/A

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

      8. lower-/.f64 N/A

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

      9. lower-/.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. pow2 N/A

          \[\leadsto \frac{\frac{\frac{1}{x}}{1 + \color{blue}{{z}^{2}}}}{y} \]

      12. +-commutative N/A

          \[\leadsto \frac{\frac{\frac{1}{x}}{\color{blue}{{z}^{2} + 1}}}{y} \]

      13. pow2 N/A

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

      14. lower-fma.f64 98.1

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

   3. Applied rewrites 98.1%

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

   if 9.4000000000000001e114 < z

   1. Initial program 74.5%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. pow2 N/A

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

      5. distribute-lft-in N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot 1 + y \cdot {z}^{2}}} \]

      6. *-rgt-identity N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y} + y \cdot {z}^{2}} \]

      7. +-commutative N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot {z}^{2} + y}} \]

      8. pow2 N/A

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

      9. associate-*r* N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-*.f64 87.4

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

   3. Applied rewrites 87.4%

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

   4. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      6. pow2 N/A

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

      7. lift-*.f64 74.4

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

   6. Applied rewrites 74.4%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. *-commutative N/A

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

      6. associate-*l* N/A

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

      7. lower-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 N/A

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

      10. lower-*.f64 96.0

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

   8. Applied rewrites 96.0%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. associate-/r* N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 N/A

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

      10. lift-*.f64 96.6

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

   10. Applied rewrites 96.6%

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

Alternative 2: 96.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ [x, y, z_m] = \mathsf{sort}([x, y, z_m])\\ \\ \begin{array}{l} \mathbf{if}\;y \cdot \left(1 + z\_m \cdot z\_m\right) \leq 2 \cdot 10^{+302}:\\ \;\;\;\;\frac{1}{\left(\mathsf{fma}\left(z\_m, z\_m, 1\right) \cdot y\right) \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(z\_m \cdot x\right) \cdot y\right) \cdot z\_m}\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
(FPCore (x y z_m)
 :precision binary64
 (if (<= (* y (+ 1.0 (* z_m z_m))) 2e+302)
   (/ 1.0 (* (* (fma z_m z_m 1.0) y) x))
   (/ 1.0 (* (* (* z_m x) y) z_m))))

C

z_m = fabs(z);
assert(x < y && y < z_m);
double code(double x, double y, double z_m) {
	double tmp;
	if ((y * (1.0 + (z_m * z_m))) <= 2e+302) {
		tmp = 1.0 / ((fma(z_m, z_m, 1.0) * y) * x);
	} else {
		tmp = 1.0 / (((z_m * x) * y) * z_m);
	}
	return tmp;
}

Julia

z_m = abs(z)
x, y, z_m = sort([x, y, z_m])
function code(x, y, z_m)
	tmp = 0.0
	if (Float64(y * Float64(1.0 + Float64(z_m * z_m))) <= 2e+302)
		tmp = Float64(1.0 / Float64(Float64(fma(z_m, z_m, 1.0) * y) * x));
	else
		tmp = Float64(1.0 / Float64(Float64(Float64(z_m * x) * y) * z_m));
	end
	return tmp
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
code[x_, y_, z$95$m_] := If[LessEqual[N[(y * N[(1.0 + N[(z$95$m * z$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2e+302], N[(1.0 / N[(N[(N[(z$95$m * z$95$m + 1.0), $MachinePrecision] * y), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(N[(z$95$m * x), $MachinePrecision] * y), $MachinePrecision] * z$95$m), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y (+.f64 #s(literal 1 binary64) (*.f64 z z))) < 2.0000000000000002e302

   1. Initial program 97.3%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. pow2 N/A

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

      7. associate-/r* N/A

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

      8. lower-/.f64 N/A

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

      9. *-commutative N/A

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

      10. pow2 N/A

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

      11. lower-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lower-*.f64 N/A

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

      14. pow2 N/A

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

      15. +-commutative N/A

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

      16. pow2 N/A

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

      17. lower-fma.f64 96.8

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

   3. Applied rewrites 96.8%

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

   if 2.0000000000000002e302 < (*.f64 y (+.f64 #s(literal 1 binary64) (*.f64 z z)))

   1. Initial program 70.4%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. pow2 N/A

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

      5. distribute-lft-in N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot 1 + y \cdot {z}^{2}}} \]

      6. *-rgt-identity N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y} + y \cdot {z}^{2}} \]

      7. +-commutative N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot {z}^{2} + y}} \]

      8. pow2 N/A

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

      9. associate-*r* N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-*.f64 80.3

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

   3. Applied rewrites 80.3%

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

   4. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      6. pow2 N/A

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

      7. lift-*.f64 68.5

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

   6. Applied rewrites 68.5%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. *-commutative N/A

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

      6. associate-*l* N/A

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

      7. lower-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 N/A

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

      10. lower-*.f64 92.6

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

   8. Applied rewrites 92.6%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. associate-*r* N/A

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

      6. lower-*.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lift-*.f64 95.6

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

   10. Applied rewrites 95.6%

       \[\leadsto \frac{1}{\left(\left(z \cdot x\right) \cdot y\right) \cdot \color{blue}{z}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 96.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ [x, y, z_m] = \mathsf{sort}([x, y, z_m])\\ \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 0.86:\\ \;\;\;\;\frac{\frac{1 - z\_m \cdot z\_m}{y}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(z\_m \cdot x\right) \cdot z\_m\right) \cdot y}\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 0.86)
   (/ (/ (- 1.0 (* z_m z_m)) y) x)
   (/ 1.0 (* (* (* z_m x) z_m) y))))

C

z_m = fabs(z);
assert(x < y && y < z_m);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = ((1.0 - (z_m * z_m)) / y) / x;
	} else {
		tmp = 1.0 / (((z_m * x) * z_m) * y);
	}
	return tmp;
}

Fortran

z_m =     private
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
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_m)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    real(8) :: tmp
    if (z_m <= 0.86d0) then
        tmp = ((1.0d0 - (z_m * z_m)) / y) / x
    else
        tmp = 1.0d0 / (((z_m * x) * z_m) * y)
    end if
    code = tmp
end function

Java

z_m = Math.abs(z);
assert x < y && y < z_m;
public static double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = ((1.0 - (z_m * z_m)) / y) / x;
	} else {
		tmp = 1.0 / (((z_m * x) * z_m) * y);
	}
	return tmp;
}

Python

z_m = math.fabs(z)
[x, y, z_m] = sort([x, y, z_m])
def code(x, y, z_m):
	tmp = 0
	if z_m <= 0.86:
		tmp = ((1.0 - (z_m * z_m)) / y) / x
	else:
		tmp = 1.0 / (((z_m * x) * z_m) * y)
	return tmp

Julia

z_m = abs(z)
x, y, z_m = sort([x, y, z_m])
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 0.86)
		tmp = Float64(Float64(Float64(1.0 - Float64(z_m * z_m)) / y) / x);
	else
		tmp = Float64(1.0 / Float64(Float64(Float64(z_m * x) * z_m) * y));
	end
	return tmp
end

MATLAB

z_m = abs(z);
x, y, z_m = num2cell(sort([x, y, z_m])){:}
function tmp_2 = code(x, y, z_m)
	tmp = 0.0;
	if (z_m <= 0.86)
		tmp = ((1.0 - (z_m * z_m)) / y) / x;
	else
		tmp = 1.0 / (((z_m * x) * z_m) * y);
	end
	tmp_2 = tmp;
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 0.86], N[(N[(N[(1.0 - N[(z$95$m * z$95$m), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision] / x), $MachinePrecision], N[(1.0 / N[(N[(N[(z$95$m * x), $MachinePrecision] * z$95$m), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 0.859999999999999987

   1. Initial program 99.7%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot y} + \color{blue}{-1 \cdot \frac{{z}^{2}}{x \cdot y}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{x \cdot y} + \frac{-1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      3. div-add-rev N/A

         \[\leadsto \frac{1 + -1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      4. mul-1-neg N/A

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

      5. pow2 N/A

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

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

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

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

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

      8. pow2 N/A

         \[\leadsto \frac{1 - {z}^{2}}{x \cdot y} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x \cdot y}} \]

      10. lower--.f64 N/A

          \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x} \cdot y} \]

      11. pow2 N/A

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 98.8

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

   4. Applied rewrites 98.8%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. associate-/r* N/A

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

      6. lower-/.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. lift--.f64 N/A

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

      9. lift-*.f64 99.3

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

   6. Applied rewrites 99.3%

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

   if 0.859999999999999987 < z

   1. Initial program 81.2%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. pow2 N/A

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

      5. distribute-lft-in N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot 1 + y \cdot {z}^{2}}} \]

      6. *-rgt-identity N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y} + y \cdot {z}^{2}} \]

      7. +-commutative N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot {z}^{2} + y}} \]

      8. pow2 N/A

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

      9. associate-*r* N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-*.f64 89.3

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      6. pow2 N/A

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

      7. lift-*.f64 80.0

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

   6. Applied rewrites 80.0%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. *-commutative N/A

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

      6. associate-*l* N/A

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

      7. lower-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 N/A

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

      10. lower-*.f64 93.9

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

   8. Applied rewrites 93.9%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 N/A

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

      9. lift-*.f64 87.8

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

   10. Applied rewrites 87.8%

       \[\leadsto \frac{1}{\left(\left(z \cdot x\right) \cdot z\right) \cdot \color{blue}{y}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 96.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ [x, y, z_m] = \mathsf{sort}([x, y, z_m])\\ \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 0.86:\\ \;\;\;\;\frac{1 - z\_m \cdot z\_m}{y \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(z\_m \cdot x\right) \cdot z\_m\right) \cdot y}\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 0.86)
   (/ (- 1.0 (* z_m z_m)) (* y x))
   (/ 1.0 (* (* (* z_m x) z_m) y))))

C

z_m = fabs(z);
assert(x < y && y < z_m);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	} else {
		tmp = 1.0 / (((z_m * x) * z_m) * y);
	}
	return tmp;
}

Fortran

z_m =     private
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
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_m)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    real(8) :: tmp
    if (z_m <= 0.86d0) then
        tmp = (1.0d0 - (z_m * z_m)) / (y * x)
    else
        tmp = 1.0d0 / (((z_m * x) * z_m) * y)
    end if
    code = tmp
end function

Java

z_m = Math.abs(z);
assert x < y && y < z_m;
public static double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	} else {
		tmp = 1.0 / (((z_m * x) * z_m) * y);
	}
	return tmp;
}

Python

z_m = math.fabs(z)
[x, y, z_m] = sort([x, y, z_m])
def code(x, y, z_m):
	tmp = 0
	if z_m <= 0.86:
		tmp = (1.0 - (z_m * z_m)) / (y * x)
	else:
		tmp = 1.0 / (((z_m * x) * z_m) * y)
	return tmp

Julia

z_m = abs(z)
x, y, z_m = sort([x, y, z_m])
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 0.86)
		tmp = Float64(Float64(1.0 - Float64(z_m * z_m)) / Float64(y * x));
	else
		tmp = Float64(1.0 / Float64(Float64(Float64(z_m * x) * z_m) * y));
	end
	return tmp
end

MATLAB

z_m = abs(z);
x, y, z_m = num2cell(sort([x, y, z_m])){:}
function tmp_2 = code(x, y, z_m)
	tmp = 0.0;
	if (z_m <= 0.86)
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	else
		tmp = 1.0 / (((z_m * x) * z_m) * y);
	end
	tmp_2 = tmp;
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 0.86], N[(N[(1.0 - N[(z$95$m * z$95$m), $MachinePrecision]), $MachinePrecision] / N[(y * x), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(N[(z$95$m * x), $MachinePrecision] * z$95$m), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 0.859999999999999987

   1. Initial program 99.7%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot y} + \color{blue}{-1 \cdot \frac{{z}^{2}}{x \cdot y}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{x \cdot y} + \frac{-1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      3. div-add-rev N/A

         \[\leadsto \frac{1 + -1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      4. mul-1-neg N/A

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

      5. pow2 N/A

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

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

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

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

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

      8. pow2 N/A

         \[\leadsto \frac{1 - {z}^{2}}{x \cdot y} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x \cdot y}} \]

      10. lower--.f64 N/A

          \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x} \cdot y} \]

      11. pow2 N/A

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 98.8

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

   4. Applied rewrites 98.8%

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

   if 0.859999999999999987 < z

   1. Initial program 81.2%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. pow2 N/A

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

      5. distribute-lft-in N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot 1 + y \cdot {z}^{2}}} \]

      6. *-rgt-identity N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y} + y \cdot {z}^{2}} \]

      7. +-commutative N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot {z}^{2} + y}} \]

      8. pow2 N/A

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

      9. associate-*r* N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-*.f64 89.3

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      6. pow2 N/A

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

      7. lift-*.f64 80.0

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

   6. Applied rewrites 80.0%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. *-commutative N/A

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

      6. associate-*l* N/A

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

      7. lower-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 N/A

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

      10. lower-*.f64 93.9

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

   8. Applied rewrites 93.9%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. *-commutative N/A

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

      8. lower-*.f64 N/A

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

      9. lift-*.f64 87.8

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

   10. Applied rewrites 87.8%

       \[\leadsto \frac{1}{\left(\left(z \cdot x\right) \cdot z\right) \cdot \color{blue}{y}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 93.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ [x, y, z_m] = \mathsf{sort}([x, y, z_m])\\ \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 0.86:\\ \;\;\;\;\frac{1 - z\_m \cdot z\_m}{y \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(z\_m \cdot x\right) \cdot y\right) \cdot z\_m}\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 0.86)
   (/ (- 1.0 (* z_m z_m)) (* y x))
   (/ 1.0 (* (* (* z_m x) y) z_m))))

C

z_m = fabs(z);
assert(x < y && y < z_m);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	} else {
		tmp = 1.0 / (((z_m * x) * y) * z_m);
	}
	return tmp;
}

Fortran

z_m =     private
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
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_m)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    real(8) :: tmp
    if (z_m <= 0.86d0) then
        tmp = (1.0d0 - (z_m * z_m)) / (y * x)
    else
        tmp = 1.0d0 / (((z_m * x) * y) * z_m)
    end if
    code = tmp
end function

Java

z_m = Math.abs(z);
assert x < y && y < z_m;
public static double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	} else {
		tmp = 1.0 / (((z_m * x) * y) * z_m);
	}
	return tmp;
}

Python

z_m = math.fabs(z)
[x, y, z_m] = sort([x, y, z_m])
def code(x, y, z_m):
	tmp = 0
	if z_m <= 0.86:
		tmp = (1.0 - (z_m * z_m)) / (y * x)
	else:
		tmp = 1.0 / (((z_m * x) * y) * z_m)
	return tmp

Julia

z_m = abs(z)
x, y, z_m = sort([x, y, z_m])
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 0.86)
		tmp = Float64(Float64(1.0 - Float64(z_m * z_m)) / Float64(y * x));
	else
		tmp = Float64(1.0 / Float64(Float64(Float64(z_m * x) * y) * z_m));
	end
	return tmp
end

MATLAB

z_m = abs(z);
x, y, z_m = num2cell(sort([x, y, z_m])){:}
function tmp_2 = code(x, y, z_m)
	tmp = 0.0;
	if (z_m <= 0.86)
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	else
		tmp = 1.0 / (((z_m * x) * y) * z_m);
	end
	tmp_2 = tmp;
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 0.86], N[(N[(1.0 - N[(z$95$m * z$95$m), $MachinePrecision]), $MachinePrecision] / N[(y * x), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(N[(z$95$m * x), $MachinePrecision] * y), $MachinePrecision] * z$95$m), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 0.859999999999999987

   1. Initial program 99.7%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot y} + \color{blue}{-1 \cdot \frac{{z}^{2}}{x \cdot y}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{x \cdot y} + \frac{-1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      3. div-add-rev N/A

         \[\leadsto \frac{1 + -1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      4. mul-1-neg N/A

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

      5. pow2 N/A

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

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

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

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

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

      8. pow2 N/A

         \[\leadsto \frac{1 - {z}^{2}}{x \cdot y} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x \cdot y}} \]

      10. lower--.f64 N/A

          \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x} \cdot y} \]

      11. pow2 N/A

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 98.8

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

   4. Applied rewrites 98.8%

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

   if 0.859999999999999987 < z

   1. Initial program 81.2%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. pow2 N/A

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

      5. distribute-lft-in N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot 1 + y \cdot {z}^{2}}} \]

      6. *-rgt-identity N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y} + y \cdot {z}^{2}} \]

      7. +-commutative N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot {z}^{2} + y}} \]

      8. pow2 N/A

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

      9. associate-*r* N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-*.f64 89.3

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      6. pow2 N/A

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

      7. lift-*.f64 80.0

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

   6. Applied rewrites 80.0%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. *-commutative N/A

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

      6. associate-*l* N/A

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

      7. lower-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 N/A

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

      10. lower-*.f64 93.9

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

   8. Applied rewrites 93.9%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. associate-*r* N/A

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

      6. lower-*.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lift-*.f64 94.3

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

   10. Applied rewrites 94.3%

       \[\leadsto \frac{1}{\left(\left(z \cdot x\right) \cdot y\right) \cdot \color{blue}{z}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 93.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ [x, y, z_m] = \mathsf{sort}([x, y, z_m])\\ \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 0.86:\\ \;\;\;\;\frac{1 - z\_m \cdot z\_m}{y \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(y \cdot z\_m\right) \cdot \left(z\_m \cdot x\right)}\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 0.86)
   (/ (- 1.0 (* z_m z_m)) (* y x))
   (/ 1.0 (* (* y z_m) (* z_m x)))))

C

z_m = fabs(z);
assert(x < y && y < z_m);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	} else {
		tmp = 1.0 / ((y * z_m) * (z_m * x));
	}
	return tmp;
}

Fortran

z_m =     private
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
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_m)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    real(8) :: tmp
    if (z_m <= 0.86d0) then
        tmp = (1.0d0 - (z_m * z_m)) / (y * x)
    else
        tmp = 1.0d0 / ((y * z_m) * (z_m * x))
    end if
    code = tmp
end function

Java

z_m = Math.abs(z);
assert x < y && y < z_m;
public static double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	} else {
		tmp = 1.0 / ((y * z_m) * (z_m * x));
	}
	return tmp;
}

Python

z_m = math.fabs(z)
[x, y, z_m] = sort([x, y, z_m])
def code(x, y, z_m):
	tmp = 0
	if z_m <= 0.86:
		tmp = (1.0 - (z_m * z_m)) / (y * x)
	else:
		tmp = 1.0 / ((y * z_m) * (z_m * x))
	return tmp

Julia

z_m = abs(z)
x, y, z_m = sort([x, y, z_m])
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 0.86)
		tmp = Float64(Float64(1.0 - Float64(z_m * z_m)) / Float64(y * x));
	else
		tmp = Float64(1.0 / Float64(Float64(y * z_m) * Float64(z_m * x)));
	end
	return tmp
end

MATLAB

z_m = abs(z);
x, y, z_m = num2cell(sort([x, y, z_m])){:}
function tmp_2 = code(x, y, z_m)
	tmp = 0.0;
	if (z_m <= 0.86)
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	else
		tmp = 1.0 / ((y * z_m) * (z_m * x));
	end
	tmp_2 = tmp;
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 0.86], N[(N[(1.0 - N[(z$95$m * z$95$m), $MachinePrecision]), $MachinePrecision] / N[(y * x), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(y * z$95$m), $MachinePrecision] * N[(z$95$m * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 0.859999999999999987

   1. Initial program 99.7%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot y} + \color{blue}{-1 \cdot \frac{{z}^{2}}{x \cdot y}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{x \cdot y} + \frac{-1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      3. div-add-rev N/A

         \[\leadsto \frac{1 + -1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      4. mul-1-neg N/A

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

      5. pow2 N/A

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

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

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

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

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

      8. pow2 N/A

         \[\leadsto \frac{1 - {z}^{2}}{x \cdot y} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x \cdot y}} \]

      10. lower--.f64 N/A

          \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x} \cdot y} \]

      11. pow2 N/A

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 98.8

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

   4. Applied rewrites 98.8%

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

   if 0.859999999999999987 < z

   1. Initial program 81.2%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. pow2 N/A

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

      5. distribute-lft-in N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot 1 + y \cdot {z}^{2}}} \]

      6. *-rgt-identity N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y} + y \cdot {z}^{2}} \]

      7. +-commutative N/A

         \[\leadsto \frac{\frac{1}{x}}{\color{blue}{y \cdot {z}^{2} + y}} \]

      8. pow2 N/A

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

      9. associate-*r* N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-*.f64 89.3

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

   3. Applied rewrites 89.3%

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

   4. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      6. pow2 N/A

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

      7. lift-*.f64 80.0

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

   6. Applied rewrites 80.0%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*l* N/A

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

      5. *-commutative N/A

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

      6. associate-*l* N/A

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

      7. lower-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 N/A

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

      10. lower-*.f64 93.9

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

   8. Applied rewrites 93.9%

      \[\leadsto \frac{1}{\left(y \cdot z\right) \cdot \color{blue}{\left(z \cdot x\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 89.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ [x, y, z_m] = \mathsf{sort}([x, y, z_m])\\ \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 0.86:\\ \;\;\;\;\frac{1 - z\_m \cdot z\_m}{y \cdot x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(z\_m \cdot z\_m\right) \cdot y\right) \cdot x}\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 0.86)
   (/ (- 1.0 (* z_m z_m)) (* y x))
   (/ 1.0 (* (* (* z_m z_m) y) x))))

C

z_m = fabs(z);
assert(x < y && y < z_m);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	} else {
		tmp = 1.0 / (((z_m * z_m) * y) * x);
	}
	return tmp;
}

Fortran

z_m =     private
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
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_m)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    real(8) :: tmp
    if (z_m <= 0.86d0) then
        tmp = (1.0d0 - (z_m * z_m)) / (y * x)
    else
        tmp = 1.0d0 / (((z_m * z_m) * y) * x)
    end if
    code = tmp
end function

Java

z_m = Math.abs(z);
assert x < y && y < z_m;
public static double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 0.86) {
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	} else {
		tmp = 1.0 / (((z_m * z_m) * y) * x);
	}
	return tmp;
}

Python

z_m = math.fabs(z)
[x, y, z_m] = sort([x, y, z_m])
def code(x, y, z_m):
	tmp = 0
	if z_m <= 0.86:
		tmp = (1.0 - (z_m * z_m)) / (y * x)
	else:
		tmp = 1.0 / (((z_m * z_m) * y) * x)
	return tmp

Julia

z_m = abs(z)
x, y, z_m = sort([x, y, z_m])
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 0.86)
		tmp = Float64(Float64(1.0 - Float64(z_m * z_m)) / Float64(y * x));
	else
		tmp = Float64(1.0 / Float64(Float64(Float64(z_m * z_m) * y) * x));
	end
	return tmp
end

MATLAB

z_m = abs(z);
x, y, z_m = num2cell(sort([x, y, z_m])){:}
function tmp_2 = code(x, y, z_m)
	tmp = 0.0;
	if (z_m <= 0.86)
		tmp = (1.0 - (z_m * z_m)) / (y * x);
	else
		tmp = 1.0 / (((z_m * z_m) * y) * x);
	end
	tmp_2 = tmp;
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 0.86], N[(N[(1.0 - N[(z$95$m * z$95$m), $MachinePrecision]), $MachinePrecision] / N[(y * x), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(N[(z$95$m * z$95$m), $MachinePrecision] * y), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 0.859999999999999987

   1. Initial program 99.7%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{1}{x \cdot y} + \color{blue}{-1 \cdot \frac{{z}^{2}}{x \cdot y}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{1}{x \cdot y} + \frac{-1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      3. div-add-rev N/A

         \[\leadsto \frac{1 + -1 \cdot {z}^{2}}{\color{blue}{x \cdot y}} \]

      4. mul-1-neg N/A

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

      5. pow2 N/A

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

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

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

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

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

      8. pow2 N/A

         \[\leadsto \frac{1 - {z}^{2}}{x \cdot y} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x \cdot y}} \]

      10. lower--.f64 N/A

          \[\leadsto \frac{1 - {z}^{2}}{\color{blue}{x} \cdot y} \]

      11. pow2 N/A

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

      12. lift-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 98.8

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

   4. Applied rewrites 98.8%

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

   if 0.859999999999999987 < z

   1. Initial program 81.2%

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

   2. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{1}{\left({z}^{2} \cdot y\right) \cdot x} \]

      6. pow2 N/A

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

      7. lift-*.f64 80.0

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

   4. Applied rewrites 80.0%

      \[\leadsto \color{blue}{\frac{1}{\left(\left(z \cdot z\right) \cdot y\right) \cdot x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 58.1% accurate, 2.2× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ [x, y, z_m] = \mathsf{sort}([x, y, z_m])\\ \\ \frac{1}{y \cdot x} \end{array} \]

FPCore

z_m = (fabs.f64 z)
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
(FPCore (x y z_m) :precision binary64 (/ 1.0 (* y x)))

C

z_m = fabs(z);
assert(x < y && y < z_m);
double code(double x, double y, double z_m) {
	return 1.0 / (y * x);
}

Fortran

z_m =     private
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
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_m)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    code = 1.0d0 / (y * x)
end function

Java

z_m = Math.abs(z);
assert x < y && y < z_m;
public static double code(double x, double y, double z_m) {
	return 1.0 / (y * x);
}

Python

z_m = math.fabs(z)
[x, y, z_m] = sort([x, y, z_m])
def code(x, y, z_m):
	return 1.0 / (y * x)

Julia

z_m = abs(z)
x, y, z_m = sort([x, y, z_m])
function code(x, y, z_m)
	return Float64(1.0 / Float64(y * x))
end

MATLAB

z_m = abs(z);
x, y, z_m = num2cell(sort([x, y, z_m])){:}
function tmp = code(x, y, z_m)
	tmp = 1.0 / (y * x);
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
NOTE: x, y, and z_m should be sorted in increasing order before calling this function.
code[x_, y_, z$95$m_] := N[(1.0 / N[(y * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 90.3%

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

2. Taylor expanded in z around 0

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

   1. lower-/.f64 N/A

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

   2. *-commutative N/A

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

   3. lower-*.f64 58.1

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

4. Applied rewrites 58.1%

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

Reproduce

herbie shell --seed 2025120 
(FPCore (x y z)
  :name "Statistics.Distribution.CauchyLorentz:$cdensity from math-functions-0.1.5.2"
  :precision binary64
  (/ (/ 1.0 x) (* y (+ 1.0 (* z z)))))