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

Percentage Accurate: 88.0% → 98.6%

Time: 7.7s

Alternatives: 11

Speedup: 0.9×

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

real(8) function code(x, y, z)
    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: 88.0% 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

real(8) function code(x, y, z)
    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: 98.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;z \cdot z \leq 5 \cdot 10^{+224}:\\ \;\;\;\;\frac{\frac{-1}{y\_m}}{x\_m \cdot \left(-1 - z \cdot z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{-1}{z}}{x\_m}}{\left(-y\_m\right) \cdot z}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= (* z z) 5e+224)
     (/ (/ -1.0 y_m) (* x_m (- -1.0 (* z z))))
     (/ (/ (/ -1.0 z) x_m) (* (- y_m) z))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 5e+224) {
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	} else {
		tmp = ((-1.0 / z) / x_m) / (-y_m * z);
	}
	return x_s * (y_s * tmp);
}

Fortran

y\_m = abs(y)
y\_s = copysign(1.0d0, y)
x\_m = abs(x)
x\_s = copysign(1.0d0, x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
real(8) function code(x_s, y_s, x_m, y_m, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: y_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z * z) <= 5d+224) then
        tmp = ((-1.0d0) / y_m) / (x_m * ((-1.0d0) - (z * z)))
    else
        tmp = (((-1.0d0) / z) / x_m) / (-y_m * z)
    end if
    code = x_s * (y_s * tmp)
end function

Java

y\_m = Math.abs(y);
y\_s = Math.copySign(1.0, y);
x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
assert x_m < y_m && y_m < z;
public static double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 5e+224) {
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	} else {
		tmp = ((-1.0 / z) / x_m) / (-y_m * z);
	}
	return x_s * (y_s * tmp);
}

Python

y\_m = math.fabs(y)
y\_s = math.copysign(1.0, y)
x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
[x_m, y_m, z] = sort([x_m, y_m, z])
def code(x_s, y_s, x_m, y_m, z):
	tmp = 0
	if (z * z) <= 5e+224:
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)))
	else:
		tmp = ((-1.0 / z) / x_m) / (-y_m * z)
	return x_s * (y_s * tmp)

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (Float64(z * z) <= 5e+224)
		tmp = Float64(Float64(-1.0 / y_m) / Float64(x_m * Float64(-1.0 - Float64(z * z))));
	else
		tmp = Float64(Float64(Float64(-1.0 / z) / x_m) / Float64(Float64(-y_m) * z));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

MATLAB

y\_m = abs(y);
y\_s = sign(y) * abs(1.0);
x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
x_m, y_m, z = num2cell(sort([x_m, y_m, z])){:}
function tmp_2 = code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0;
	if ((z * z) <= 5e+224)
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	else
		tmp = ((-1.0 / z) / x_m) / (-y_m * z);
	end
	tmp_2 = x_s * (y_s * tmp);
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[N[(z * z), $MachinePrecision], 5e+224], N[(N[(-1.0 / y$95$m), $MachinePrecision] / N[(x$95$m * N[(-1.0 - N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(-1.0 / z), $MachinePrecision] / x$95$m), $MachinePrecision] / N[((-y$95$m) * z), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 4.99999999999999964e224

   1. Initial program 95.2%

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

      1. *-lft-identity N/A

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

      2. lift-/.f64 N/A

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

      3. frac-2neg N/A

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

      4. associate-*r/ N/A

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

      5. lift-*.f64 N/A

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

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

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

      7. times-frac N/A

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

      8. lower-*.f64 N/A

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

      9. metadata-eval N/A

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

      10. frac-2neg N/A

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

      11. lower-/.f64 N/A

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

      12. lower-/.f64 N/A

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

      13. lift-/.f64 N/A

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

      14. distribute-neg-frac N/A

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

      15. metadata-eval N/A

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

      16. lower-/.f64 98.0

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

      17. lift-+.f64 N/A

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

      18. +-commutative N/A

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

      19. lift-*.f64 N/A

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

      20. lower-fma.f64 98.0

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

   4. Applied rewrites 98.0%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. lower-/.f64 N/A

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

      6. clear-num N/A

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

      7. lift-/.f64 N/A

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

      8. associate-/l/ N/A

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

      9. frac-2neg N/A

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

      10. metadata-eval N/A

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

      11. remove-double-div N/A

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

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

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

      13. lower-*.f64 N/A

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

      14. lift-fma.f64 N/A

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

      15. lift-*.f64 N/A

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

      16. +-commutative N/A

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

      17. distribute-neg-in N/A

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

      18. metadata-eval N/A

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

      19. unsub-neg N/A

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

      20. lower--.f64 98.0

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

   6. Applied rewrites 98.0%

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

   if 4.99999999999999964e224 < (*.f64 z z)

   1. Initial program 75.4%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 74.7

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

   5. Applied rewrites 74.7%

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

   7. Applied rewrites 98.7%

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

   9. Applied rewrites 98.8%

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

4. Final simplification 98.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot z \leq 5 \cdot 10^{+224}:\\ \;\;\;\;\frac{\frac{-1}{y}}{x \cdot \left(-1 - z \cdot z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{-1}{z}}{x}}{\left(-y\right) \cdot z}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 98.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;\left(1 + z \cdot z\right) \cdot y\_m \leq 2 \cdot 10^{+299}:\\ \;\;\;\;\frac{\frac{1}{x\_m}}{\mathsf{fma}\left(y\_m \cdot z, z, y\_m\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(x\_m \cdot z\right) \cdot y\_m\right) \cdot z}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= (* (+ 1.0 (* z z)) y_m) 2e+299)
     (/ (/ 1.0 x_m) (fma (* y_m z) z y_m))
     (/ 1.0 (* (* (* x_m z) y_m) z))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if (((1.0 + (z * z)) * y_m) <= 2e+299) {
		tmp = (1.0 / x_m) / fma((y_m * z), z, y_m);
	} else {
		tmp = 1.0 / (((x_m * z) * y_m) * z);
	}
	return x_s * (y_s * tmp);
}

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (Float64(Float64(1.0 + Float64(z * z)) * y_m) <= 2e+299)
		tmp = Float64(Float64(1.0 / x_m) / fma(Float64(y_m * z), z, y_m));
	else
		tmp = Float64(1.0 / Float64(Float64(Float64(x_m * z) * y_m) * z));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[N[(N[(1.0 + N[(z * z), $MachinePrecision]), $MachinePrecision] * y$95$m), $MachinePrecision], 2e+299], N[(N[(1.0 / x$95$m), $MachinePrecision] / N[(N[(y$95$m * z), $MachinePrecision] * z + y$95$m), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(N[(x$95$m * z), $MachinePrecision] * y$95$m), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y (+.f64 #s(literal 1 binary64) (*.f64 z z))) < 2.0000000000000001e299

   1. Initial program 92.3%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Add Preprocessing
   3. 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

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

      3. +-commutative N/A

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

      4. distribute-lft-in N/A

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

      5. lift-*.f64 N/A

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

      6. associate-*r* N/A

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

      7. *-rgt-identity N/A

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

      8. lower-fma.f64 N/A

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

      9. lower-*.f64 94.9

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

   4. Applied rewrites 94.9%

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

   if 2.0000000000000001e299 < (*.f64 y (+.f64 #s(literal 1 binary64) (*.f64 z z)))

   1. Initial program 68.6%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 68.6

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

   5. Applied rewrites 68.6%

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

   7. Applied rewrites 99.8%

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

4. Final simplification 95.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(1 + z \cdot z\right) \cdot y \leq 2 \cdot 10^{+299}:\\ \;\;\;\;\frac{\frac{1}{x}}{\mathsf{fma}\left(y \cdot z, z, y\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(x \cdot z\right) \cdot y\right) \cdot z}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 98.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;z \cdot z \leq 4 \cdot 10^{+210}:\\ \;\;\;\;\frac{\frac{-1}{y\_m}}{x\_m \cdot \left(-1 - z \cdot z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{-1}{y\_m}}{z}}{\left(-z\right) \cdot x\_m}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= (* z z) 4e+210)
     (/ (/ -1.0 y_m) (* x_m (- -1.0 (* z z))))
     (/ (/ (/ -1.0 y_m) z) (* (- z) x_m))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 4e+210) {
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	} else {
		tmp = ((-1.0 / y_m) / z) / (-z * x_m);
	}
	return x_s * (y_s * tmp);
}

Fortran

y\_m = abs(y)
y\_s = copysign(1.0d0, y)
x\_m = abs(x)
x\_s = copysign(1.0d0, x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
real(8) function code(x_s, y_s, x_m, y_m, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: y_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z * z) <= 4d+210) then
        tmp = ((-1.0d0) / y_m) / (x_m * ((-1.0d0) - (z * z)))
    else
        tmp = (((-1.0d0) / y_m) / z) / (-z * x_m)
    end if
    code = x_s * (y_s * tmp)
end function

Java

y\_m = Math.abs(y);
y\_s = Math.copySign(1.0, y);
x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
assert x_m < y_m && y_m < z;
public static double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 4e+210) {
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	} else {
		tmp = ((-1.0 / y_m) / z) / (-z * x_m);
	}
	return x_s * (y_s * tmp);
}

Python

y\_m = math.fabs(y)
y\_s = math.copysign(1.0, y)
x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
[x_m, y_m, z] = sort([x_m, y_m, z])
def code(x_s, y_s, x_m, y_m, z):
	tmp = 0
	if (z * z) <= 4e+210:
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)))
	else:
		tmp = ((-1.0 / y_m) / z) / (-z * x_m)
	return x_s * (y_s * tmp)

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (Float64(z * z) <= 4e+210)
		tmp = Float64(Float64(-1.0 / y_m) / Float64(x_m * Float64(-1.0 - Float64(z * z))));
	else
		tmp = Float64(Float64(Float64(-1.0 / y_m) / z) / Float64(Float64(-z) * x_m));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

MATLAB

y\_m = abs(y);
y\_s = sign(y) * abs(1.0);
x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
x_m, y_m, z = num2cell(sort([x_m, y_m, z])){:}
function tmp_2 = code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0;
	if ((z * z) <= 4e+210)
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	else
		tmp = ((-1.0 / y_m) / z) / (-z * x_m);
	end
	tmp_2 = x_s * (y_s * tmp);
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[N[(z * z), $MachinePrecision], 4e+210], N[(N[(-1.0 / y$95$m), $MachinePrecision] / N[(x$95$m * N[(-1.0 - N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(-1.0 / y$95$m), $MachinePrecision] / z), $MachinePrecision] / N[((-z) * x$95$m), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 3.99999999999999971e210

   1. Initial program 95.1%

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

      1. *-lft-identity N/A

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

      2. lift-/.f64 N/A

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

      3. frac-2neg N/A

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

      4. associate-*r/ N/A

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

      5. lift-*.f64 N/A

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

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

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

      7. times-frac N/A

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

      8. lower-*.f64 N/A

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

      9. metadata-eval N/A

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

      10. frac-2neg N/A

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

      11. lower-/.f64 N/A

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

      12. lower-/.f64 N/A

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

      13. lift-/.f64 N/A

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

      14. distribute-neg-frac N/A

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

      15. metadata-eval N/A

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

      16. lower-/.f64 98.5

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

      17. lift-+.f64 N/A

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

      18. +-commutative N/A

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

      19. lift-*.f64 N/A

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

      20. lower-fma.f64 98.5

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

   4. Applied rewrites 98.5%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. lower-/.f64 N/A

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

      6. clear-num N/A

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

      7. lift-/.f64 N/A

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

      8. associate-/l/ N/A

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

      9. frac-2neg N/A

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

      10. metadata-eval N/A

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

      11. remove-double-div N/A

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

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

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

      13. lower-*.f64 N/A

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

      14. lift-fma.f64 N/A

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

      15. lift-*.f64 N/A

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

      16. +-commutative N/A

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

      17. distribute-neg-in N/A

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

      18. metadata-eval N/A

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

      19. unsub-neg N/A

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

      20. lower--.f64 98.5

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

   6. Applied rewrites 98.5%

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

   if 3.99999999999999971e210 < (*.f64 z z)

   1. Initial program 76.4%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 75.7

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

   5. Applied rewrites 75.7%

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

   7. Applied rewrites 98.0%

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

   9. Applied rewrites 98.8%

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

4. Final simplification 98.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot z \leq 4 \cdot 10^{+210}:\\ \;\;\;\;\frac{\frac{-1}{y}}{x \cdot \left(-1 - z \cdot z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{-1}{y}}{z}}{\left(-z\right) \cdot x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.7% accurate, 0.8× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;z \cdot z \leq 10^{+243}:\\ \;\;\;\;\frac{\frac{-1}{y\_m}}{x\_m \cdot \left(-1 - z \cdot z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{x\_m \cdot z}}{y\_m \cdot z}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= (* z z) 1e+243)
     (/ (/ -1.0 y_m) (* x_m (- -1.0 (* z z))))
     (/ (/ 1.0 (* x_m z)) (* y_m z))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 1e+243) {
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	} else {
		tmp = (1.0 / (x_m * z)) / (y_m * z);
	}
	return x_s * (y_s * tmp);
}

Fortran

y\_m = abs(y)
y\_s = copysign(1.0d0, y)
x\_m = abs(x)
x\_s = copysign(1.0d0, x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
real(8) function code(x_s, y_s, x_m, y_m, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: y_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z * z) <= 1d+243) then
        tmp = ((-1.0d0) / y_m) / (x_m * ((-1.0d0) - (z * z)))
    else
        tmp = (1.0d0 / (x_m * z)) / (y_m * z)
    end if
    code = x_s * (y_s * tmp)
end function

Java

y\_m = Math.abs(y);
y\_s = Math.copySign(1.0, y);
x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
assert x_m < y_m && y_m < z;
public static double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 1e+243) {
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	} else {
		tmp = (1.0 / (x_m * z)) / (y_m * z);
	}
	return x_s * (y_s * tmp);
}

Python

y\_m = math.fabs(y)
y\_s = math.copysign(1.0, y)
x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
[x_m, y_m, z] = sort([x_m, y_m, z])
def code(x_s, y_s, x_m, y_m, z):
	tmp = 0
	if (z * z) <= 1e+243:
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)))
	else:
		tmp = (1.0 / (x_m * z)) / (y_m * z)
	return x_s * (y_s * tmp)

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (Float64(z * z) <= 1e+243)
		tmp = Float64(Float64(-1.0 / y_m) / Float64(x_m * Float64(-1.0 - Float64(z * z))));
	else
		tmp = Float64(Float64(1.0 / Float64(x_m * z)) / Float64(y_m * z));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

MATLAB

y\_m = abs(y);
y\_s = sign(y) * abs(1.0);
x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
x_m, y_m, z = num2cell(sort([x_m, y_m, z])){:}
function tmp_2 = code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0;
	if ((z * z) <= 1e+243)
		tmp = (-1.0 / y_m) / (x_m * (-1.0 - (z * z)));
	else
		tmp = (1.0 / (x_m * z)) / (y_m * z);
	end
	tmp_2 = x_s * (y_s * tmp);
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[N[(z * z), $MachinePrecision], 1e+243], N[(N[(-1.0 / y$95$m), $MachinePrecision] / N[(x$95$m * N[(-1.0 - N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 / N[(x$95$m * z), $MachinePrecision]), $MachinePrecision] / N[(y$95$m * z), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 1.0000000000000001e243

   1. Initial program 95.4%

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

      1. *-lft-identity N/A

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

      2. lift-/.f64 N/A

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

      3. frac-2neg N/A

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

      4. associate-*r/ N/A

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

      5. lift-*.f64 N/A

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

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

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

      7. times-frac N/A

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

      8. lower-*.f64 N/A

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

      9. metadata-eval N/A

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

      10. frac-2neg N/A

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

      11. lower-/.f64 N/A

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

      12. lower-/.f64 N/A

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

      13. lift-/.f64 N/A

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

      14. distribute-neg-frac N/A

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

      15. metadata-eval N/A

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

      16. lower-/.f64 97.5

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

      17. lift-+.f64 N/A

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

      18. +-commutative N/A

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

      19. lift-*.f64 N/A

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

      20. lower-fma.f64 97.5

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

   4. Applied rewrites 97.5%

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

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. clear-num N/A

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

      4. un-div-inv N/A

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

      5. lower-/.f64 N/A

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

      6. clear-num N/A

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

      7. lift-/.f64 N/A

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

      8. associate-/l/ N/A

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

      9. frac-2neg N/A

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

      10. metadata-eval N/A

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

      11. remove-double-div N/A

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

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

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

      13. lower-*.f64 N/A

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

      14. lift-fma.f64 N/A

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

      15. lift-*.f64 N/A

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

      16. +-commutative N/A

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

      17. distribute-neg-in N/A

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

      18. metadata-eval N/A

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

      19. unsub-neg N/A

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

      20. lower--.f64 97.5

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

   6. Applied rewrites 97.5%

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

   if 1.0000000000000001e243 < (*.f64 z z)

   1. Initial program 73.6%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 72.8

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

   5. Applied rewrites 72.8%

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

   7. Applied rewrites 98.6%

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

4. Final simplification 97.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot z \leq 10^{+243}:\\ \;\;\;\;\frac{\frac{-1}{y}}{x \cdot \left(-1 - z \cdot z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{x \cdot z}}{y \cdot z}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 97.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;z \cdot z \leq 10^{+243}:\\ \;\;\;\;\frac{--1}{\left(x\_m \cdot y\_m\right) \cdot \mathsf{fma}\left(z, z, 1\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{x\_m \cdot z}}{y\_m \cdot z}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= (* z z) 1e+243)
     (/ (- -1.0) (* (* x_m y_m) (fma z z 1.0)))
     (/ (/ 1.0 (* x_m z)) (* y_m z))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 1e+243) {
		tmp = -(-1.0) / ((x_m * y_m) * fma(z, z, 1.0));
	} else {
		tmp = (1.0 / (x_m * z)) / (y_m * z);
	}
	return x_s * (y_s * tmp);
}

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (Float64(z * z) <= 1e+243)
		tmp = Float64(Float64(-(-1.0)) / Float64(Float64(x_m * y_m) * fma(z, z, 1.0)));
	else
		tmp = Float64(Float64(1.0 / Float64(x_m * z)) / Float64(y_m * z));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[N[(z * z), $MachinePrecision], 1e+243], N[((--1.0) / N[(N[(x$95$m * y$95$m), $MachinePrecision] * N[(z * z + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 / N[(x$95$m * z), $MachinePrecision]), $MachinePrecision] / N[(y$95$m * z), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 1.0000000000000001e243

   1. Initial program 95.4%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Add Preprocessing
   3. 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. associate-/l/ N/A

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

      4. frac-2neg N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. *-commutative N/A

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

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

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

      9. lift-*.f64 N/A

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

      10. associate-*r* N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. neg-mul-1 N/A

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

      14. associate-*r* N/A

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

      15. metadata-eval N/A

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

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

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

      17. *-rgt-identity N/A

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

      18. lower-*.f64 N/A

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

      19. lower-neg.f64 98.2

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

      20. lift-+.f64 N/A

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

      21. +-commutative N/A

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

      22. lift-*.f64 N/A

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

      23. lower-fma.f64 98.2

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

   4. Applied rewrites 98.2%

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

   if 1.0000000000000001e243 < (*.f64 z z)

   1. Initial program 73.6%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 72.8

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

   5. Applied rewrites 72.8%

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

   7. Applied rewrites 98.6%

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

4. Final simplification 98.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot z \leq 10^{+243}:\\ \;\;\;\;\frac{--1}{\left(x \cdot y\right) \cdot \mathsf{fma}\left(z, z, 1\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{1}{x \cdot z}}{y \cdot z}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 96.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;z \cdot z \leq 10^{+243}:\\ \;\;\;\;\frac{--1}{\left(x\_m \cdot y\_m\right) \cdot \mathsf{fma}\left(z, z, 1\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(x\_m \cdot z\right) \cdot \left(y\_m \cdot z\right)}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= (* z z) 1e+243)
     (/ (- -1.0) (* (* x_m y_m) (fma z z 1.0)))
     (/ 1.0 (* (* x_m z) (* y_m z)))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 1e+243) {
		tmp = -(-1.0) / ((x_m * y_m) * fma(z, z, 1.0));
	} else {
		tmp = 1.0 / ((x_m * z) * (y_m * z));
	}
	return x_s * (y_s * tmp);
}

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (Float64(z * z) <= 1e+243)
		tmp = Float64(Float64(-(-1.0)) / Float64(Float64(x_m * y_m) * fma(z, z, 1.0)));
	else
		tmp = Float64(1.0 / Float64(Float64(x_m * z) * Float64(y_m * z)));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[N[(z * z), $MachinePrecision], 1e+243], N[((--1.0) / N[(N[(x$95$m * y$95$m), $MachinePrecision] * N[(z * z + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(x$95$m * z), $MachinePrecision] * N[(y$95$m * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 1.0000000000000001e243

   1. Initial program 95.4%

      \[\frac{\frac{1}{x}}{y \cdot \left(1 + z \cdot z\right)} \]
   2. Add Preprocessing
   3. 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. associate-/l/ N/A

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

      4. frac-2neg N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. *-commutative N/A

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

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

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

      9. lift-*.f64 N/A

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

      10. associate-*r* N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. neg-mul-1 N/A

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

      14. associate-*r* N/A

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

      15. metadata-eval N/A

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

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

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

      17. *-rgt-identity N/A

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

      18. lower-*.f64 N/A

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

      19. lower-neg.f64 98.2

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

      20. lift-+.f64 N/A

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

      21. +-commutative N/A

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

      22. lift-*.f64 N/A

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

      23. lower-fma.f64 98.2

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

   4. Applied rewrites 98.2%

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

   if 1.0000000000000001e243 < (*.f64 z z)

   1. Initial program 73.6%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 72.8

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

   5. Applied rewrites 72.8%

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

   7. Applied rewrites 97.9%

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

4. Final simplification 98.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot z \leq 10^{+243}:\\ \;\;\;\;\frac{--1}{\left(x \cdot y\right) \cdot \mathsf{fma}\left(z, z, 1\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(x \cdot z\right) \cdot \left(y \cdot z\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 78.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;z \leq 0.035:\\ \;\;\;\;\frac{\frac{1}{x\_m}}{y\_m}\\ \mathbf{elif}\;z \leq 6.9 \cdot 10^{+121}:\\ \;\;\;\;\frac{1}{\left(x\_m \cdot y\_m\right) \cdot \left(z \cdot z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(x\_m \cdot z\right) \cdot \left(y\_m \cdot z\right)}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= z 0.035)
     (/ (/ 1.0 x_m) y_m)
     (if (<= z 6.9e+121)
       (/ 1.0 (* (* x_m y_m) (* z z)))
       (/ 1.0 (* (* x_m z) (* y_m z))))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if (z <= 0.035) {
		tmp = (1.0 / x_m) / y_m;
	} else if (z <= 6.9e+121) {
		tmp = 1.0 / ((x_m * y_m) * (z * z));
	} else {
		tmp = 1.0 / ((x_m * z) * (y_m * z));
	}
	return x_s * (y_s * tmp);
}

Fortran

y\_m = abs(y)
y\_s = copysign(1.0d0, y)
x\_m = abs(x)
x\_s = copysign(1.0d0, x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
real(8) function code(x_s, y_s, x_m, y_m, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: y_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= 0.035d0) then
        tmp = (1.0d0 / x_m) / y_m
    else if (z <= 6.9d+121) then
        tmp = 1.0d0 / ((x_m * y_m) * (z * z))
    else
        tmp = 1.0d0 / ((x_m * z) * (y_m * z))
    end if
    code = x_s * (y_s * tmp)
end function

Java

y\_m = Math.abs(y);
y\_s = Math.copySign(1.0, y);
x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
assert x_m < y_m && y_m < z;
public static double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if (z <= 0.035) {
		tmp = (1.0 / x_m) / y_m;
	} else if (z <= 6.9e+121) {
		tmp = 1.0 / ((x_m * y_m) * (z * z));
	} else {
		tmp = 1.0 / ((x_m * z) * (y_m * z));
	}
	return x_s * (y_s * tmp);
}

Python

y\_m = math.fabs(y)
y\_s = math.copysign(1.0, y)
x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
[x_m, y_m, z] = sort([x_m, y_m, z])
def code(x_s, y_s, x_m, y_m, z):
	tmp = 0
	if z <= 0.035:
		tmp = (1.0 / x_m) / y_m
	elif z <= 6.9e+121:
		tmp = 1.0 / ((x_m * y_m) * (z * z))
	else:
		tmp = 1.0 / ((x_m * z) * (y_m * z))
	return x_s * (y_s * tmp)

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (z <= 0.035)
		tmp = Float64(Float64(1.0 / x_m) / y_m);
	elseif (z <= 6.9e+121)
		tmp = Float64(1.0 / Float64(Float64(x_m * y_m) * Float64(z * z)));
	else
		tmp = Float64(1.0 / Float64(Float64(x_m * z) * Float64(y_m * z)));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

MATLAB

y\_m = abs(y);
y\_s = sign(y) * abs(1.0);
x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
x_m, y_m, z = num2cell(sort([x_m, y_m, z])){:}
function tmp_2 = code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0;
	if (z <= 0.035)
		tmp = (1.0 / x_m) / y_m;
	elseif (z <= 6.9e+121)
		tmp = 1.0 / ((x_m * y_m) * (z * z));
	else
		tmp = 1.0 / ((x_m * z) * (y_m * z));
	end
	tmp_2 = x_s * (y_s * tmp);
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[z, 0.035], N[(N[(1.0 / x$95$m), $MachinePrecision] / y$95$m), $MachinePrecision], If[LessEqual[z, 6.9e+121], N[(1.0 / N[(N[(x$95$m * y$95$m), $MachinePrecision] * N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(x$95$m * z), $MachinePrecision] * N[(y$95$m * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 3 regimes

2. if z < 0.035000000000000003

   1. Initial program 93.4%

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

   3. Taylor expanded in z around 0

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

      1. associate-/r* N/A

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

      2. lower-/.f64 N/A

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

      3. lower-/.f64 71.1

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

   5. Applied rewrites 71.1%

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

   if 0.035000000000000003 < z < 6.8999999999999996e121

   1. Initial program 84.0%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 84.1

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

   5. Applied rewrites 84.1%

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

   7. Applied rewrites 99.7%

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

   if 6.8999999999999996e121 < z

   1. Initial program 64.0%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 62.1

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

   5. Applied rewrites 62.1%

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

   7. Applied rewrites 97.7%

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

4. Final simplification 78.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq 0.035:\\ \;\;\;\;\frac{\frac{1}{x}}{y}\\ \mathbf{elif}\;z \leq 6.9 \cdot 10^{+121}:\\ \;\;\;\;\frac{1}{\left(x \cdot y\right) \cdot \left(z \cdot z\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(x \cdot z\right) \cdot \left(y \cdot z\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 96.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;z \cdot z \leq 2 \cdot 10^{-10}:\\ \;\;\;\;\frac{\frac{1}{x\_m}}{y\_m}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(x\_m \cdot z\right) \cdot \left(y\_m \cdot z\right)}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= (* z z) 2e-10)
     (/ (/ 1.0 x_m) y_m)
     (/ 1.0 (* (* x_m z) (* y_m z)))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 2e-10) {
		tmp = (1.0 / x_m) / y_m;
	} else {
		tmp = 1.0 / ((x_m * z) * (y_m * z));
	}
	return x_s * (y_s * tmp);
}

Fortran

y\_m = abs(y)
y\_s = copysign(1.0d0, y)
x\_m = abs(x)
x\_s = copysign(1.0d0, x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
real(8) function code(x_s, y_s, x_m, y_m, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: y_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z * z) <= 2d-10) then
        tmp = (1.0d0 / x_m) / y_m
    else
        tmp = 1.0d0 / ((x_m * z) * (y_m * z))
    end if
    code = x_s * (y_s * tmp)
end function

Java

y\_m = Math.abs(y);
y\_s = Math.copySign(1.0, y);
x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
assert x_m < y_m && y_m < z;
public static double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if ((z * z) <= 2e-10) {
		tmp = (1.0 / x_m) / y_m;
	} else {
		tmp = 1.0 / ((x_m * z) * (y_m * z));
	}
	return x_s * (y_s * tmp);
}

Python

y\_m = math.fabs(y)
y\_s = math.copysign(1.0, y)
x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
[x_m, y_m, z] = sort([x_m, y_m, z])
def code(x_s, y_s, x_m, y_m, z):
	tmp = 0
	if (z * z) <= 2e-10:
		tmp = (1.0 / x_m) / y_m
	else:
		tmp = 1.0 / ((x_m * z) * (y_m * z))
	return x_s * (y_s * tmp)

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (Float64(z * z) <= 2e-10)
		tmp = Float64(Float64(1.0 / x_m) / y_m);
	else
		tmp = Float64(1.0 / Float64(Float64(x_m * z) * Float64(y_m * z)));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

MATLAB

y\_m = abs(y);
y\_s = sign(y) * abs(1.0);
x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
x_m, y_m, z = num2cell(sort([x_m, y_m, z])){:}
function tmp_2 = code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0;
	if ((z * z) <= 2e-10)
		tmp = (1.0 / x_m) / y_m;
	else
		tmp = 1.0 / ((x_m * z) * (y_m * z));
	end
	tmp_2 = x_s * (y_s * tmp);
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[N[(z * z), $MachinePrecision], 2e-10], N[(N[(1.0 / x$95$m), $MachinePrecision] / y$95$m), $MachinePrecision], N[(1.0 / N[(N[(x$95$m * z), $MachinePrecision] * N[(y$95$m * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 2.00000000000000007e-10

   1. Initial program 99.7%

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

   3. Taylor expanded in z around 0

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

      1. associate-/r* N/A

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

      2. lower-/.f64 N/A

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

      3. lower-/.f64 99.5

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

   5. Applied rewrites 99.5%

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

   if 2.00000000000000007e-10 < (*.f64 z z)

   1. Initial program 78.7%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 77.9

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

   5. Applied rewrites 77.9%

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

   7. Applied rewrites 94.1%

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

4. Final simplification 96.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \cdot z \leq 2 \cdot 10^{-10}:\\ \;\;\;\;\frac{\frac{1}{x}}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(x \cdot z\right) \cdot \left(y \cdot z\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 78.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} y\_m = \left|y\right| \\ y\_s = \mathsf{copysign}\left(1, y\right) \\ x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ [x_m, y_m, z] = \mathsf{sort}([x_m, y_m, z])\\ \\ x\_s \cdot \left(y\_s \cdot \begin{array}{l} \mathbf{if}\;z \leq 0.035:\\ \;\;\;\;\frac{\frac{1}{x\_m}}{y\_m}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(x\_m \cdot z\right) \cdot y\_m\right) \cdot z}\\ \end{array}\right) \end{array} \]

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (*
  x_s
  (*
   y_s
   (if (<= z 0.035) (/ (/ 1.0 x_m) y_m) (/ 1.0 (* (* (* x_m z) y_m) z))))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if (z <= 0.035) {
		tmp = (1.0 / x_m) / y_m;
	} else {
		tmp = 1.0 / (((x_m * z) * y_m) * z);
	}
	return x_s * (y_s * tmp);
}

Fortran

y\_m = abs(y)
y\_s = copysign(1.0d0, y)
x\_m = abs(x)
x\_s = copysign(1.0d0, x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
real(8) function code(x_s, y_s, x_m, y_m, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: y_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= 0.035d0) then
        tmp = (1.0d0 / x_m) / y_m
    else
        tmp = 1.0d0 / (((x_m * z) * y_m) * z)
    end if
    code = x_s * (y_s * tmp)
end function

Java

y\_m = Math.abs(y);
y\_s = Math.copySign(1.0, y);
x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
assert x_m < y_m && y_m < z;
public static double code(double x_s, double y_s, double x_m, double y_m, double z) {
	double tmp;
	if (z <= 0.035) {
		tmp = (1.0 / x_m) / y_m;
	} else {
		tmp = 1.0 / (((x_m * z) * y_m) * z);
	}
	return x_s * (y_s * tmp);
}

Python

y\_m = math.fabs(y)
y\_s = math.copysign(1.0, y)
x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
[x_m, y_m, z] = sort([x_m, y_m, z])
def code(x_s, y_s, x_m, y_m, z):
	tmp = 0
	if z <= 0.035:
		tmp = (1.0 / x_m) / y_m
	else:
		tmp = 1.0 / (((x_m * z) * y_m) * z)
	return x_s * (y_s * tmp)

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0
	if (z <= 0.035)
		tmp = Float64(Float64(1.0 / x_m) / y_m);
	else
		tmp = Float64(1.0 / Float64(Float64(Float64(x_m * z) * y_m) * z));
	end
	return Float64(x_s * Float64(y_s * tmp))
end

MATLAB

y\_m = abs(y);
y\_s = sign(y) * abs(1.0);
x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
x_m, y_m, z = num2cell(sort([x_m, y_m, z])){:}
function tmp_2 = code(x_s, y_s, x_m, y_m, z)
	tmp = 0.0;
	if (z <= 0.035)
		tmp = (1.0 / x_m) / y_m;
	else
		tmp = 1.0 / (((x_m * z) * y_m) * z);
	end
	tmp_2 = x_s * (y_s * tmp);
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * If[LessEqual[z, 0.035], N[(N[(1.0 / x$95$m), $MachinePrecision] / y$95$m), $MachinePrecision], N[(1.0 / N[(N[(N[(x$95$m * z), $MachinePrecision] * y$95$m), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if z < 0.035000000000000003

   1. Initial program 93.4%

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

   3. Taylor expanded in z around 0

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

      1. associate-/r* N/A

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

      2. lower-/.f64 N/A

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

      3. lower-/.f64 71.1

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

   5. Applied rewrites 71.1%

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

   if 0.035000000000000003 < z

   1. Initial program 73.4%

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

   3. Taylor expanded in z around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. unpow2 N/A

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

      7. lower-*.f64 72.4

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

   5. Applied rewrites 72.4%

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

   7. Applied rewrites 94.1%

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

4. Final simplification 76.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq 0.035:\\ \;\;\;\;\frac{\frac{1}{x}}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(\left(x \cdot z\right) \cdot y\right) \cdot z}\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 58.5% accurate, 1.6× speedup

Math

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

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (* x_s (* y_s (/ (/ 1.0 x_m) y_m))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	return x_s * (y_s * ((1.0 / x_m) / y_m));
}

Fortran

y\_m = abs(y)
y\_s = copysign(1.0d0, y)
x\_m = abs(x)
x\_s = copysign(1.0d0, x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
real(8) function code(x_s, y_s, x_m, y_m, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: y_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z
    code = x_s * (y_s * ((1.0d0 / x_m) / y_m))
end function

Java

y\_m = Math.abs(y);
y\_s = Math.copySign(1.0, y);
x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
assert x_m < y_m && y_m < z;
public static double code(double x_s, double y_s, double x_m, double y_m, double z) {
	return x_s * (y_s * ((1.0 / x_m) / y_m));
}

Python

y\_m = math.fabs(y)
y\_s = math.copysign(1.0, y)
x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
[x_m, y_m, z] = sort([x_m, y_m, z])
def code(x_s, y_s, x_m, y_m, z):
	return x_s * (y_s * ((1.0 / x_m) / y_m))

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	return Float64(x_s * Float64(y_s * Float64(Float64(1.0 / x_m) / y_m)))
end

MATLAB

y\_m = abs(y);
y\_s = sign(y) * abs(1.0);
x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
x_m, y_m, z = num2cell(sort([x_m, y_m, z])){:}
function tmp = code(x_s, y_s, x_m, y_m, z)
	tmp = x_s * (y_s * ((1.0 / x_m) / y_m));
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * N[(N[(1.0 / x$95$m), $MachinePrecision] / y$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 88.4%

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

3. Taylor expanded in z around 0

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

   1. associate-/r* N/A

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

   2. lower-/.f64 N/A

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

   3. lower-/.f64 57.4

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

5. Applied rewrites 57.4%

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

Alternative 11: 58.5% accurate, 2.1× speedup

Math

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

FPCore

y\_m = (fabs.f64 y)
y\_s = (copysign.f64 #s(literal 1 binary64) y)
x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
(FPCore (x_s y_s x_m y_m z)
 :precision binary64
 (* x_s (* y_s (/ 1.0 (* x_m y_m)))))

C

y\_m = fabs(y);
y\_s = copysign(1.0, y);
x\_m = fabs(x);
x\_s = copysign(1.0, x);
assert(x_m < y_m && y_m < z);
double code(double x_s, double y_s, double x_m, double y_m, double z) {
	return x_s * (y_s * (1.0 / (x_m * y_m)));
}

Fortran

y\_m = abs(y)
y\_s = copysign(1.0d0, y)
x\_m = abs(x)
x\_s = copysign(1.0d0, x)
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
real(8) function code(x_s, y_s, x_m, y_m, z)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: y_s
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z
    code = x_s * (y_s * (1.0d0 / (x_m * y_m)))
end function

Java

y\_m = Math.abs(y);
y\_s = Math.copySign(1.0, y);
x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
assert x_m < y_m && y_m < z;
public static double code(double x_s, double y_s, double x_m, double y_m, double z) {
	return x_s * (y_s * (1.0 / (x_m * y_m)));
}

Python

y\_m = math.fabs(y)
y\_s = math.copysign(1.0, y)
x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
[x_m, y_m, z] = sort([x_m, y_m, z])
def code(x_s, y_s, x_m, y_m, z):
	return x_s * (y_s * (1.0 / (x_m * y_m)))

Julia

y\_m = abs(y)
y\_s = copysign(1.0, y)
x\_m = abs(x)
x\_s = copysign(1.0, x)
x_m, y_m, z = sort([x_m, y_m, z])
function code(x_s, y_s, x_m, y_m, z)
	return Float64(x_s * Float64(y_s * Float64(1.0 / Float64(x_m * y_m))))
end

MATLAB

y\_m = abs(y);
y\_s = sign(y) * abs(1.0);
x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
x_m, y_m, z = num2cell(sort([x_m, y_m, z])){:}
function tmp = code(x_s, y_s, x_m, y_m, z)
	tmp = x_s * (y_s * (1.0 / (x_m * y_m)));
end

Wolfram

y\_m = N[Abs[y], $MachinePrecision]
y\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[y]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x_m, y_m, and z should be sorted in increasing order before calling this function.
code[x$95$s_, y$95$s_, x$95$m_, y$95$m_, z_] := N[(x$95$s * N[(y$95$s * N[(1.0 / N[(x$95$m * y$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 88.4%

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

3. Taylor expanded in z around 0

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

   1. associate-/r* N/A

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

   2. lower-/.f64 N/A

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

   3. lower-/.f64 57.4

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

5. Applied rewrites 57.4%

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

7. Applied rewrites 57.2%

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

Developer Target 1: 91.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + z \cdot z\\ t_1 := y \cdot t\_0\\ t_2 := \frac{\frac{1}{y}}{t\_0 \cdot x}\\ \mathbf{if}\;t\_1 < -\infty:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 < 8.680743250567252 \cdot 10^{+305}:\\ \;\;\;\;\frac{\frac{1}{x}}{t\_0 \cdot y}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ 1.0 (* z z))) (t_1 (* y t_0)) (t_2 (/ (/ 1.0 y) (* t_0 x))))
   (if (< t_1 (- INFINITY))
     t_2
     (if (< t_1 8.680743250567252e+305) (/ (/ 1.0 x) (* t_0 y)) t_2))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 + (z * z);
	double t_1 = y * t_0;
	double t_2 = (1.0 / y) / (t_0 * x);
	double tmp;
	if (t_1 < -((double) INFINITY)) {
		tmp = t_2;
	} else if (t_1 < 8.680743250567252e+305) {
		tmp = (1.0 / x) / (t_0 * y);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 + (z * z);
	double t_1 = y * t_0;
	double t_2 = (1.0 / y) / (t_0 * x);
	double tmp;
	if (t_1 < -Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else if (t_1 < 8.680743250567252e+305) {
		tmp = (1.0 / x) / (t_0 * y);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 + (z * z)
	t_1 = y * t_0
	t_2 = (1.0 / y) / (t_0 * x)
	tmp = 0
	if t_1 < -math.inf:
		tmp = t_2
	elif t_1 < 8.680743250567252e+305:
		tmp = (1.0 / x) / (t_0 * y)
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 + Float64(z * z))
	t_1 = Float64(y * t_0)
	t_2 = Float64(Float64(1.0 / y) / Float64(t_0 * x))
	tmp = 0.0
	if (t_1 < Float64(-Inf))
		tmp = t_2;
	elseif (t_1 < 8.680743250567252e+305)
		tmp = Float64(Float64(1.0 / x) / Float64(t_0 * y));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 + (z * z);
	t_1 = y * t_0;
	t_2 = (1.0 / y) / (t_0 * x);
	tmp = 0.0;
	if (t_1 < -Inf)
		tmp = t_2;
	elseif (t_1 < 8.680743250567252e+305)
		tmp = (1.0 / x) / (t_0 * y);
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 + N[(z * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(y * t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(1.0 / y), $MachinePrecision] / N[(t$95$0 * x), $MachinePrecision]), $MachinePrecision]}, If[Less[t$95$1, (-Infinity)], t$95$2, If[Less[t$95$1, 8.680743250567252e+305], N[(N[(1.0 / x), $MachinePrecision] / N[(t$95$0 * y), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

Reproduce

herbie shell --seed 2024331 
(FPCore (x y z)
  :name "Statistics.Distribution.CauchyLorentz:$cdensity from math-functions-0.1.5.2"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< (* y (+ 1 (* z z))) -inf.0) (/ (/ 1 y) (* (+ 1 (* z z)) x)) (if (< (* y (+ 1 (* z z))) 868074325056725200000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (/ (/ 1 x) (* (+ 1 (* z z)) y)) (/ (/ 1 y) (* (+ 1 (* z z)) x)))))

  (/ (/ 1.0 x) (* y (+ 1.0 (* z z)))))