Linear.Quaternion:$c/ from linear-1.19.1.3, A

Percentage Accurate: 98.3% → 99.6%

Time: 5.3s

Alternatives: 6

Speedup: 1.3×

• 34.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 2 \cdot 10^{+180}:\\ \;\;\;\;\mathsf{fma}\left(2 \cdot z\_m, z\_m, \mathsf{fma}\left(z\_m, z\_m, y \cdot x\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot z\_m\right) \cdot z\_m\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 2e+180)
   (fma (* 2.0 z_m) z_m (fma z_m z_m (* y x)))
   (* (* 3.0 z_m) z_m)))

C

z_m = fabs(z);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 2e+180) {
		tmp = fma((2.0 * z_m), z_m, fma(z_m, z_m, (y * x)));
	} else {
		tmp = (3.0 * z_m) * z_m;
	}
	return tmp;
}

Julia

z_m = abs(z)
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 2e+180)
		tmp = fma(Float64(2.0 * z_m), z_m, fma(z_m, z_m, Float64(y * x)));
	else
		tmp = Float64(Float64(3.0 * z_m) * z_m);
	end
	return tmp
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 2e+180], N[(N[(2.0 * z$95$m), $MachinePrecision] * z$95$m + N[(z$95$m * z$95$m + N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(3.0 * z$95$m), $MachinePrecision] * z$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 2e180

   1. Initial program 98.1%

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

      1. lift-+.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. associate-+l+ N/A

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

      4. +-commutative N/A

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

      5. count-2 N/A

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

      6. lift-*.f64 N/A

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

      7. associate-*r* N/A

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

      8. count-2 N/A

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

      9. lower-fma.f64 N/A

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

      10. count-2 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{2 \cdot z}, z, x \cdot y + z \cdot z\right) \]

      11. lower-*.f64 98.2

          \[\leadsto \mathsf{fma}\left(\color{blue}{2 \cdot z}, z, x \cdot y + z \cdot z\right) \]

      12. lift-+.f64 N/A

          \[\leadsto \mathsf{fma}\left(2 \cdot z, z, \color{blue}{x \cdot y + z \cdot z}\right) \]

      13. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(2 \cdot z, z, \color{blue}{z \cdot z + x \cdot y}\right) \]

      14. lift-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(2 \cdot z, z, \color{blue}{z \cdot z} + x \cdot y\right) \]

      15. lower-fma.f64 98.6

          \[\leadsto \mathsf{fma}\left(2 \cdot z, z, \color{blue}{\mathsf{fma}\left(z, z, x \cdot y\right)}\right) \]

      16. lift-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(2 \cdot z, z, \mathsf{fma}\left(z, z, \color{blue}{x \cdot y}\right)\right) \]

      17. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(2 \cdot z, z, \mathsf{fma}\left(z, z, \color{blue}{y \cdot x}\right)\right) \]

      18. lower-*.f64 98.6

          \[\leadsto \mathsf{fma}\left(2 \cdot z, z, \mathsf{fma}\left(z, z, \color{blue}{y \cdot x}\right)\right) \]

   4. Applied rewrites 98.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(2 \cdot z, z, \mathsf{fma}\left(z, z, y \cdot x\right)\right)} \]

   if 2e180 < z

   1. Initial program 81.3%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
   4. Step-by-step derivation

      1. *-rgt-identity N/A

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

      2. *-inverses N/A

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

      3. associate-/l* N/A

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

      4. associate-*l/ N/A

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

      5. distribute-lft1-in N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

      8. associate-*l* N/A

         \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      10. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

      12. associate-/l* N/A

          \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

      13. *-inverses N/A

          \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

      14. *-rgt-identity N/A

          \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

      15. unpow2 N/A

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

      16. lower-*.f64 100.0

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

   5. Applied rewrites 100.0%

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

   7. Applied rewrites 100.0%

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

Alternative 2: 99.5% accurate, 1.3× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 2 \cdot 10^{+180}:\\ \;\;\;\;\mathsf{fma}\left(3 \cdot z\_m, z\_m, y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot z\_m\right) \cdot z\_m\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 2e+180) (fma (* 3.0 z_m) z_m (* y x)) (* (* 3.0 z_m) z_m)))

C

z_m = fabs(z);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 2e+180) {
		tmp = fma((3.0 * z_m), z_m, (y * x));
	} else {
		tmp = (3.0 * z_m) * z_m;
	}
	return tmp;
}

Julia

z_m = abs(z)
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 2e+180)
		tmp = fma(Float64(3.0 * z_m), z_m, Float64(y * x));
	else
		tmp = Float64(Float64(3.0 * z_m) * z_m);
	end
	return tmp
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 2e+180], N[(N[(3.0 * z$95$m), $MachinePrecision] * z$95$m + N[(y * x), $MachinePrecision]), $MachinePrecision], N[(N[(3.0 * z$95$m), $MachinePrecision] * z$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 2e180

   1. Initial program 98.1%

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

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. associate-+r+ N/A

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

      3. distribute-lft1-in N/A

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

      4. metadata-eval N/A

         \[\leadsto \color{blue}{3} \cdot {z}^{2} + x \cdot y \]

      5. unpow2 N/A

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

      6. associate-*r* N/A

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

      7. lower-fma.f64 N/A

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

      8. lower-*.f64 N/A

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

      9. *-commutative N/A

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

      10. lower-*.f64 98.6

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

   5. Applied rewrites 98.6%

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

   if 2e180 < z

   1. Initial program 81.3%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
   4. Step-by-step derivation

      1. *-rgt-identity N/A

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

      2. *-inverses N/A

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

      3. associate-/l* N/A

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

      4. associate-*l/ N/A

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

      5. distribute-lft1-in N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

      8. associate-*l* N/A

         \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      10. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

      12. associate-/l* N/A

          \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

      13. *-inverses N/A

          \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

      14. *-rgt-identity N/A

          \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

      15. unpow2 N/A

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

      16. lower-*.f64 100.0

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

   5. Applied rewrites 100.0%

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

   7. Applied rewrites 100.0%

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

Alternative 3: 85.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ \begin{array}{l} \mathbf{if}\;z\_m \cdot z\_m \leq 0.0002:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;\left(z\_m \cdot z\_m\right) \cdot 3\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
(FPCore (x y z_m)
 :precision binary64
 (if (<= (* z_m z_m) 0.0002) (* y x) (* (* z_m z_m) 3.0)))

C

z_m = fabs(z);
double code(double x, double y, double z_m) {
	double tmp;
	if ((z_m * z_m) <= 0.0002) {
		tmp = y * x;
	} else {
		tmp = (z_m * z_m) * 3.0;
	}
	return tmp;
}

Fortran

z_m = abs(z)
real(8) function code(x, y, z_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    real(8) :: tmp
    if ((z_m * z_m) <= 0.0002d0) then
        tmp = y * x
    else
        tmp = (z_m * z_m) * 3.0d0
    end if
    code = tmp
end function

Java

z_m = Math.abs(z);
public static double code(double x, double y, double z_m) {
	double tmp;
	if ((z_m * z_m) <= 0.0002) {
		tmp = y * x;
	} else {
		tmp = (z_m * z_m) * 3.0;
	}
	return tmp;
}

Python

z_m = math.fabs(z)
def code(x, y, z_m):
	tmp = 0
	if (z_m * z_m) <= 0.0002:
		tmp = y * x
	else:
		tmp = (z_m * z_m) * 3.0
	return tmp

Julia

z_m = abs(z)
function code(x, y, z_m)
	tmp = 0.0
	if (Float64(z_m * z_m) <= 0.0002)
		tmp = Float64(y * x);
	else
		tmp = Float64(Float64(z_m * z_m) * 3.0);
	end
	return tmp
end

MATLAB

z_m = abs(z);
function tmp_2 = code(x, y, z_m)
	tmp = 0.0;
	if ((z_m * z_m) <= 0.0002)
		tmp = y * x;
	else
		tmp = (z_m * z_m) * 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
code[x_, y_, z$95$m_] := If[LessEqual[N[(z$95$m * z$95$m), $MachinePrecision], 0.0002], N[(y * x), $MachinePrecision], N[(N[(z$95$m * z$95$m), $MachinePrecision] * 3.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 2.0000000000000001e-4

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
   4. Step-by-step derivation

      1. *-rgt-identity N/A

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

      2. *-inverses N/A

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

      3. associate-/l* N/A

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

      4. associate-*l/ N/A

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

      5. distribute-lft1-in N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

      8. associate-*l* N/A

         \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      10. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

      12. associate-/l* N/A

          \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

      13. *-inverses N/A

          \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

      14. *-rgt-identity N/A

          \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

      15. unpow2 N/A

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

      16. lower-*.f64 22.0

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

   5. Applied rewrites 22.0%

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

   7. Applied rewrites 22.1%

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

   8. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 84.5

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

   10. Applied rewrites 84.5%

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

   if 2.0000000000000001e-4 < (*.f64 z z)

   1. Initial program 92.4%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
   4. Step-by-step derivation

      1. *-rgt-identity N/A

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

      2. *-inverses N/A

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

      3. associate-/l* N/A

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

      4. associate-*l/ N/A

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

      5. distribute-lft1-in N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

      8. associate-*l* N/A

         \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      10. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

      12. associate-/l* N/A

          \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

      13. *-inverses N/A

          \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

      14. *-rgt-identity N/A

          \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

      15. unpow2 N/A

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

      16. lower-*.f64 88.8

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

   5. Applied rewrites 88.8%

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

Alternative 4: 85.6% accurate, 1.4× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ \begin{array}{l} \mathbf{if}\;z\_m \cdot z\_m \leq 0.0002:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot z\_m\right) \cdot z\_m\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
(FPCore (x y z_m)
 :precision binary64
 (if (<= (* z_m z_m) 0.0002) (* y x) (* (* 3.0 z_m) z_m)))

C

z_m = fabs(z);
double code(double x, double y, double z_m) {
	double tmp;
	if ((z_m * z_m) <= 0.0002) {
		tmp = y * x;
	} else {
		tmp = (3.0 * z_m) * z_m;
	}
	return tmp;
}

Fortran

z_m = abs(z)
real(8) function code(x, y, z_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    real(8) :: tmp
    if ((z_m * z_m) <= 0.0002d0) then
        tmp = y * x
    else
        tmp = (3.0d0 * z_m) * z_m
    end if
    code = tmp
end function

Java

z_m = Math.abs(z);
public static double code(double x, double y, double z_m) {
	double tmp;
	if ((z_m * z_m) <= 0.0002) {
		tmp = y * x;
	} else {
		tmp = (3.0 * z_m) * z_m;
	}
	return tmp;
}

Python

z_m = math.fabs(z)
def code(x, y, z_m):
	tmp = 0
	if (z_m * z_m) <= 0.0002:
		tmp = y * x
	else:
		tmp = (3.0 * z_m) * z_m
	return tmp

Julia

z_m = abs(z)
function code(x, y, z_m)
	tmp = 0.0
	if (Float64(z_m * z_m) <= 0.0002)
		tmp = Float64(y * x);
	else
		tmp = Float64(Float64(3.0 * z_m) * z_m);
	end
	return tmp
end

MATLAB

z_m = abs(z);
function tmp_2 = code(x, y, z_m)
	tmp = 0.0;
	if ((z_m * z_m) <= 0.0002)
		tmp = y * x;
	else
		tmp = (3.0 * z_m) * z_m;
	end
	tmp_2 = tmp;
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
code[x_, y_, z$95$m_] := If[LessEqual[N[(z$95$m * z$95$m), $MachinePrecision], 0.0002], N[(y * x), $MachinePrecision], N[(N[(3.0 * z$95$m), $MachinePrecision] * z$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 2.0000000000000001e-4

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
   4. Step-by-step derivation

      1. *-rgt-identity N/A

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

      2. *-inverses N/A

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

      3. associate-/l* N/A

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

      4. associate-*l/ N/A

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

      5. distribute-lft1-in N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

      8. associate-*l* N/A

         \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      10. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

      12. associate-/l* N/A

          \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

      13. *-inverses N/A

          \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

      14. *-rgt-identity N/A

          \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

      15. unpow2 N/A

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

      16. lower-*.f64 22.0

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

   5. Applied rewrites 22.0%

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

   7. Applied rewrites 22.1%

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

   8. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 84.5

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

   10. Applied rewrites 84.5%

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

   if 2.0000000000000001e-4 < (*.f64 z z)

   1. Initial program 92.4%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
   4. Step-by-step derivation

      1. *-rgt-identity N/A

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

      2. *-inverses N/A

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

      3. associate-/l* N/A

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

      4. associate-*l/ N/A

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

      5. distribute-lft1-in N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

      8. associate-*l* N/A

         \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      10. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

      12. associate-/l* N/A

          \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

      13. *-inverses N/A

          \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

      14. *-rgt-identity N/A

          \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

      15. unpow2 N/A

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

      16. lower-*.f64 88.8

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

   5. Applied rewrites 88.8%

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

   7. Applied rewrites 88.8%

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

Alternative 5: 85.7% accurate, 1.4× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ \begin{array}{l} \mathbf{if}\;z\_m \leq 2.6:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z\_m, z\_m + z\_m, z\_m \cdot z\_m\right)\\ \end{array} \end{array} \]

FPCore

z_m = (fabs.f64 z)
(FPCore (x y z_m)
 :precision binary64
 (if (<= z_m 2.6) (* y x) (fma z_m (+ z_m z_m) (* z_m z_m))))

C

z_m = fabs(z);
double code(double x, double y, double z_m) {
	double tmp;
	if (z_m <= 2.6) {
		tmp = y * x;
	} else {
		tmp = fma(z_m, (z_m + z_m), (z_m * z_m));
	}
	return tmp;
}

Julia

z_m = abs(z)
function code(x, y, z_m)
	tmp = 0.0
	if (z_m <= 2.6)
		tmp = Float64(y * x);
	else
		tmp = fma(z_m, Float64(z_m + z_m), Float64(z_m * z_m));
	end
	return tmp
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
code[x_, y_, z$95$m_] := If[LessEqual[z$95$m, 2.6], N[(y * x), $MachinePrecision], N[(z$95$m * N[(z$95$m + z$95$m), $MachinePrecision] + N[(z$95$m * z$95$m), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < 2.60000000000000009

   1. Initial program 97.8%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
   4. Step-by-step derivation

      1. *-rgt-identity N/A

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

      2. *-inverses N/A

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

      3. associate-/l* N/A

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

      4. associate-*l/ N/A

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

      5. distribute-lft1-in N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

      8. associate-*l* N/A

         \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      10. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

      12. associate-/l* N/A

          \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

      13. *-inverses N/A

          \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

      14. *-rgt-identity N/A

          \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

      15. unpow2 N/A

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

      16. lower-*.f64 47.7

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

   5. Applied rewrites 47.7%

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

   7. Applied rewrites 47.7%

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

   8. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 58.0

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

   10. Applied rewrites 58.0%

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

   if 2.60000000000000009 < z

   1. Initial program 90.1%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
   4. Step-by-step derivation

      1. *-rgt-identity N/A

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

      2. *-inverses N/A

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

      3. associate-/l* N/A

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

      4. associate-*l/ N/A

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

      5. distribute-lft1-in N/A

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

      6. metadata-eval N/A

         \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

      7. associate-*r/ N/A

         \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

      8. associate-*l* N/A

         \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      10. lower-*.f64 N/A

          \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

      11. associate-*l/ N/A

          \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

      12. associate-/l* N/A

          \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

      13. *-inverses N/A

          \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

      14. *-rgt-identity N/A

          \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

      15. unpow2 N/A

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

      16. lower-*.f64 87.2

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

   5. Applied rewrites 87.2%

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

   7. Applied rewrites 87.3%

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

Alternative 6: 53.1% accurate, 5.0× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ y \cdot x \end{array} \]

FPCore

z_m = (fabs.f64 z)
(FPCore (x y z_m) :precision binary64 (* y x))

C

z_m = fabs(z);
double code(double x, double y, double z_m) {
	return y * x;
}

Fortran

z_m = abs(z)
real(8) function code(x, y, z_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z_m
    code = y * x
end function

Java

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

Python

z_m = math.fabs(z)
def code(x, y, z_m):
	return y * x

Julia

z_m = abs(z)
function code(x, y, z_m)
	return Float64(y * x)
end

MATLAB

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

Wolfram

z_m = N[Abs[z], $MachinePrecision]
code[x_, y_, z$95$m_] := N[(y * x), $MachinePrecision]

Derivation

1. Initial program 96.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{2 \cdot {z}^{2} + {z}^{2}} \]
4. Step-by-step derivation

   1. *-rgt-identity N/A

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

   2. *-inverses N/A

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

   3. associate-/l* N/A

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

   4. associate-*l/ N/A

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

   5. distribute-lft1-in N/A

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

   6. metadata-eval N/A

      \[\leadsto \frac{\color{blue}{3} \cdot {z}^{2}}{x} \cdot x \]

   7. associate-*r/ N/A

      \[\leadsto \color{blue}{\left(3 \cdot \frac{{z}^{2}}{x}\right)} \cdot x \]

   8. associate-*l* N/A

      \[\leadsto \color{blue}{3 \cdot \left(\frac{{z}^{2}}{x} \cdot x\right)} \]

   9. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

   10. lower-*.f64 N/A

       \[\leadsto \color{blue}{\left(\frac{{z}^{2}}{x} \cdot x\right) \cdot 3} \]

   11. associate-*l/ N/A

       \[\leadsto \color{blue}{\frac{{z}^{2} \cdot x}{x}} \cdot 3 \]

   12. associate-/l* N/A

       \[\leadsto \color{blue}{\left({z}^{2} \cdot \frac{x}{x}\right)} \cdot 3 \]

   13. *-inverses N/A

       \[\leadsto \left({z}^{2} \cdot \color{blue}{1}\right) \cdot 3 \]

   14. *-rgt-identity N/A

       \[\leadsto \color{blue}{{z}^{2}} \cdot 3 \]

   15. unpow2 N/A

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

   16. lower-*.f64 57.2

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

5. Applied rewrites 57.2%

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

7. Applied rewrites 57.3%

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

8. Taylor expanded in x around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 48.6

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

10. Applied rewrites 48.6%

    \[\leadsto \color{blue}{y \cdot x} \]
11. Add Preprocessing

Developer Target 1: 98.3% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024317 
(FPCore (x y z)
  :name "Linear.Quaternion:$c/ from linear-1.19.1.3, A"
  :precision binary64

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

  (+ (+ (+ (* x y) (* z z)) (* z z)) (* z z)))