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

Percentage Accurate: 98.3% → 99.8%

Time: 15.1s

Alternatives: 11

Speedup: 0.7×

• 33.8% 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: 99.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \begin{array}{l} \mathbf{if}\;x \cdot y \leq -1 \cdot 10^{+136}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \left(z \cdot \frac{z}{y}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot z + \left(z \cdot z + \left(z \cdot z + x \cdot y\right)\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= (* x y) -1e+136)
   (* y (+ x (* 3.0 (* z (/ z y)))))
   (+ (* z z) (+ (* z z) (+ (* z z) (* x y))))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	double tmp;
	if ((x * y) <= -1e+136) {
		tmp = y * (x + (3.0 * (z * (z / y))));
	} else {
		tmp = (z * z) + ((z * z) + ((z * z) + (x * y)));
	}
	return tmp;
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x * y) <= (-1d+136)) then
        tmp = y * (x + (3.0d0 * (z * (z / y))))
    else
        tmp = (z * z) + ((z * z) + ((z * z) + (x * y)))
    end if
    code = tmp
end function

Java

assert x < y && y < z;
public static double code(double x, double y, double z) {
	double tmp;
	if ((x * y) <= -1e+136) {
		tmp = y * (x + (3.0 * (z * (z / y))));
	} else {
		tmp = (z * z) + ((z * z) + ((z * z) + (x * y)));
	}
	return tmp;
}

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	tmp = 0
	if (x * y) <= -1e+136:
		tmp = y * (x + (3.0 * (z * (z / y))))
	else:
		tmp = (z * z) + ((z * z) + ((z * z) + (x * y)))
	return tmp

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	tmp = 0.0
	if (Float64(x * y) <= -1e+136)
		tmp = Float64(y * Float64(x + Float64(3.0 * Float64(z * Float64(z / y)))));
	else
		tmp = Float64(Float64(z * z) + Float64(Float64(z * z) + Float64(Float64(z * z) + Float64(x * y))));
	end
	return tmp
end

MATLAB

x, y, z = num2cell(sort([x, y, z])){:}
function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x * y) <= -1e+136)
		tmp = y * (x + (3.0 * (z * (z / y))));
	else
		tmp = (z * z) + ((z * z) + ((z * z) + (x * y)));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := If[LessEqual[N[(x * y), $MachinePrecision], -1e+136], N[(y * N[(x + N[(3.0 * N[(z * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(z * z), $MachinePrecision] + N[(N[(z * z), $MachinePrecision] + N[(N[(z * z), $MachinePrecision] + N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x y) < -1.00000000000000006e136

   1. Initial program 81.5%

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

   3. Taylor expanded in y around inf 94.7%

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

   5. Simplified 94.7%

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

      1. unpow2 94.7%

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

      2. associate-/l* 100.0%

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

   7. Applied egg-rr 100.0%

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

   if -1.00000000000000006e136 < (*.f64 x y)

   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. Recombined 2 regimes into one program.

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot y \leq -1 \cdot 10^{+136}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \left(z \cdot \frac{z}{y}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot z + \left(z \cdot z + \left(z \cdot z + x \cdot y\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.4% accurate, 0.1× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \mathsf{fma}\left(z, z, \mathsf{fma}\left(x, y, 2 \cdot \left(z \cdot z\right)\right)\right) \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z) :precision binary64 (fma z z (fma x y (* 2.0 (* z z)))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	return fma(z, z, fma(x, y, (2.0 * (z * z))));
}

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	return fma(z, z, fma(x, y, Float64(2.0 * Float64(z * z))))
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := N[(z * z + N[(x * y + N[(2.0 * N[(z * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.1%

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

   1. +-commutative 97.1%

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

   2. fma-define 97.2%

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

   3. associate-+l+ 97.2%

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

   4. fma-define 99.2%

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

   5. count-2 99.2%

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

3. Simplified 99.2%

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

Alternative 3: 99.4% accurate, 0.1× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \mathsf{fma}\left(x, y, z \cdot \left(z \cdot 3\right)\right) \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z) :precision binary64 (fma x y (* z (* z 3.0))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	return fma(x, y, (z * (z * 3.0)));
}

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	return fma(x, y, Float64(z * Float64(z * 3.0)))
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := N[(x * y + N[(z * N[(z * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.1%

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

   1. associate-+l+ 97.1%

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

   2. associate-+l+ 97.2%

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

   3. fma-define 99.1%

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

   4. distribute-lft-out 99.1%

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

   5. distribute-lft-out 99.1%

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

   6. count-2 99.1%

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

   7. distribute-rgt1-in 99.1%

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

   8. metadata-eval 99.1%

      \[\leadsto \mathsf{fma}\left(x, y, z \cdot \left(\left(2 + \color{blue}{-1 \cdot -1}\right) \cdot z\right)\right) \]

   9. *-commutative 99.1%

      \[\leadsto \mathsf{fma}\left(x, y, z \cdot \color{blue}{\left(z \cdot \left(2 + -1 \cdot -1\right)\right)}\right) \]

   10. metadata-eval 99.1%

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

   11. metadata-eval 99.1%

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

3. Simplified 99.1%

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

Alternative 4: 97.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \begin{array}{l} \mathbf{if}\;y \leq 1.2 \cdot 10^{-147}:\\ \;\;\;\;x \cdot \left(y + \frac{z \cdot 3}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \frac{z}{\frac{y}{z}}\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= y 1.2e-147)
   (* x (+ y (/ (* z 3.0) (/ x z))))
   (* y (+ x (* 3.0 (/ z (/ y z)))))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.2e-147) {
		tmp = x * (y + ((z * 3.0) / (x / z)));
	} else {
		tmp = y * (x + (3.0 * (z / (y / z))));
	}
	return tmp;
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 1.2d-147) then
        tmp = x * (y + ((z * 3.0d0) / (x / z)))
    else
        tmp = y * (x + (3.0d0 * (z / (y / z))))
    end if
    code = tmp
end function

Java

assert x < y && y < z;
public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 1.2e-147) {
		tmp = x * (y + ((z * 3.0) / (x / z)));
	} else {
		tmp = y * (x + (3.0 * (z / (y / z))));
	}
	return tmp;
}

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	tmp = 0
	if y <= 1.2e-147:
		tmp = x * (y + ((z * 3.0) / (x / z)))
	else:
		tmp = y * (x + (3.0 * (z / (y / z))))
	return tmp

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	tmp = 0.0
	if (y <= 1.2e-147)
		tmp = Float64(x * Float64(y + Float64(Float64(z * 3.0) / Float64(x / z))));
	else
		tmp = Float64(y * Float64(x + Float64(3.0 * Float64(z / Float64(y / z)))));
	end
	return tmp
end

MATLAB

x, y, z = num2cell(sort([x, y, z])){:}
function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 1.2e-147)
		tmp = x * (y + ((z * 3.0) / (x / z)));
	else
		tmp = y * (x + (3.0 * (z / (y / z))));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := If[LessEqual[y, 1.2e-147], N[(x * N[(y + N[(N[(z * 3.0), $MachinePrecision] / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * N[(x + N[(3.0 * N[(z / N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.19999999999999999e-147

   1. Initial program 97.3%

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

   3. Taylor expanded in x around inf 95.1%

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

   5. Simplified 95.1%

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

      1. unpow2 95.1%

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

      2. associate-/l* 95.1%

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

   7. Applied egg-rr 95.1%

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

      1. clear-num 95.0%

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

      2. un-div-inv 95.1%

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

   9. Applied egg-rr 95.1%

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

       1. metadata-eval 95.1%

          \[\leadsto x \cdot \left(y + \frac{z}{\frac{x}{z}} \cdot \color{blue}{{3}^{1}}\right) \]

       2. metadata-eval 95.1%

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

       3. sqrt-pow2 94.8%

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

       4. associate-*l/ 94.9%

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

       5. sqrt-pow2 95.1%

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

       6. metadata-eval 95.1%

          \[\leadsto x \cdot \left(y + \frac{z \cdot {3}^{\color{blue}{1}}}{\frac{x}{z}}\right) \]

       7. metadata-eval 95.1%

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

   11. Applied egg-rr 95.1%

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

   if 1.19999999999999999e-147 < y

   1. Initial program 96.8%

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

   3. Taylor expanded in y around inf 97.8%

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

   5. Simplified 97.8%

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

      1. unpow2 97.8%

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

      2. associate-/l* 99.8%

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

   7. Applied egg-rr 99.8%

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

      1. clear-num 99.8%

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

      2. un-div-inv 99.8%

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

   9. Applied egg-rr 99.8%

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

4. Final simplification 96.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 1.2 \cdot 10^{-147}:\\ \;\;\;\;x \cdot \left(y + \frac{z \cdot 3}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \frac{z}{\frac{y}{z}}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 97.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \begin{array}{l} \mathbf{if}\;y \leq 6 \cdot 10^{-76}:\\ \;\;\;\;x \cdot \left(y + \frac{z \cdot 3}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \left(z \cdot \frac{z}{y}\right)\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= y 6e-76)
   (* x (+ y (/ (* z 3.0) (/ x z))))
   (* y (+ x (* 3.0 (* z (/ z y)))))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	double tmp;
	if (y <= 6e-76) {
		tmp = x * (y + ((z * 3.0) / (x / z)));
	} else {
		tmp = y * (x + (3.0 * (z * (z / y))));
	}
	return tmp;
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (y <= 6d-76) then
        tmp = x * (y + ((z * 3.0d0) / (x / z)))
    else
        tmp = y * (x + (3.0d0 * (z * (z / y))))
    end if
    code = tmp
end function

Java

assert x < y && y < z;
public static double code(double x, double y, double z) {
	double tmp;
	if (y <= 6e-76) {
		tmp = x * (y + ((z * 3.0) / (x / z)));
	} else {
		tmp = y * (x + (3.0 * (z * (z / y))));
	}
	return tmp;
}

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	tmp = 0
	if y <= 6e-76:
		tmp = x * (y + ((z * 3.0) / (x / z)))
	else:
		tmp = y * (x + (3.0 * (z * (z / y))))
	return tmp

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	tmp = 0.0
	if (y <= 6e-76)
		tmp = Float64(x * Float64(y + Float64(Float64(z * 3.0) / Float64(x / z))));
	else
		tmp = Float64(y * Float64(x + Float64(3.0 * Float64(z * Float64(z / y)))));
	end
	return tmp
end

MATLAB

x, y, z = num2cell(sort([x, y, z])){:}
function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= 6e-76)
		tmp = x * (y + ((z * 3.0) / (x / z)));
	else
		tmp = y * (x + (3.0 * (z * (z / y))));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := If[LessEqual[y, 6e-76], N[(x * N[(y + N[(N[(z * 3.0), $MachinePrecision] / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * N[(x + N[(3.0 * N[(z * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 6.00000000000000048e-76

   1. Initial program 97.6%

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

   3. Taylor expanded in x around inf 95.5%

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

   5. Simplified 95.5%

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

      1. unpow2 95.5%

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

      2. associate-/l* 95.5%

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

   7. Applied egg-rr 95.5%

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

      1. clear-num 95.5%

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

      2. un-div-inv 95.6%

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

   9. Applied egg-rr 95.6%

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

       1. metadata-eval 95.6%

          \[\leadsto x \cdot \left(y + \frac{z}{\frac{x}{z}} \cdot \color{blue}{{3}^{1}}\right) \]

       2. metadata-eval 95.6%

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

       3. sqrt-pow2 95.3%

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

       4. associate-*l/ 95.3%

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

       5. sqrt-pow2 95.6%

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

       6. metadata-eval 95.6%

          \[\leadsto x \cdot \left(y + \frac{z \cdot {3}^{\color{blue}{1}}}{\frac{x}{z}}\right) \]

       7. metadata-eval 95.6%

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

   11. Applied egg-rr 95.6%

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

   if 6.00000000000000048e-76 < y

   1. Initial program 96.1%

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

   3. Taylor expanded in y around inf 97.3%

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

   5. Simplified 97.3%

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

      1. unpow2 97.3%

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

      2. associate-/l* 99.8%

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

   7. Applied egg-rr 99.8%

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

4. Final simplification 96.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 6 \cdot 10^{-76}:\\ \;\;\;\;x \cdot \left(y + \frac{z \cdot 3}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \left(z \cdot \frac{z}{y}\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 85.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \begin{array}{l} \mathbf{if}\;z \cdot z \leq 10^{-13}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;\left(z \cdot z\right) \cdot 3\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= (* z z) 1e-13) (* x y) (* (* z z) 3.0)))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	double tmp;
	if ((z * z) <= 1e-13) {
		tmp = x * y;
	} else {
		tmp = (z * z) * 3.0;
	}
	return tmp;
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z * z) <= 1d-13) then
        tmp = x * y
    else
        tmp = (z * z) * 3.0d0
    end if
    code = tmp
end function

Java

assert x < y && y < z;
public static double code(double x, double y, double z) {
	double tmp;
	if ((z * z) <= 1e-13) {
		tmp = x * y;
	} else {
		tmp = (z * z) * 3.0;
	}
	return tmp;
}

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	tmp = 0
	if (z * z) <= 1e-13:
		tmp = x * y
	else:
		tmp = (z * z) * 3.0
	return tmp

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	tmp = 0.0
	if (Float64(z * z) <= 1e-13)
		tmp = Float64(x * y);
	else
		tmp = Float64(Float64(z * z) * 3.0);
	end
	return tmp
end

MATLAB

x, y, z = num2cell(sort([x, y, z])){:}
function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z * z) <= 1e-13)
		tmp = x * y;
	else
		tmp = (z * z) * 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := If[LessEqual[N[(z * z), $MachinePrecision], 1e-13], N[(x * y), $MachinePrecision], N[(N[(z * z), $MachinePrecision] * 3.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 z z) < 1e-13

   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 inf 87.1%

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

   if 1e-13 < (*.f64 z z)

   1. Initial program 94.7%

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

   3. Taylor expanded in x around 0 87.1%

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

   5. Simplified 87.1%

      \[\leadsto \color{blue}{{z}^{2} \cdot 3} \]
   6. Step-by-step derivation

      1. unpow2 87.1%

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

   7. Applied egg-rr 87.1%

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

Alternative 7: 93.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ x \cdot \left(y + \frac{z \cdot 3}{\frac{x}{z}}\right) \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z) :precision binary64 (* x (+ y (/ (* z 3.0) (/ x z)))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	return x * (y + ((z * 3.0) / (x / z)));
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
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 * 3.0d0) / (x / z)))
end function

Java

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

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	return x * (y + ((z * 3.0) / (x / z)))

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	return Float64(x * Float64(y + Float64(Float64(z * 3.0) / Float64(x / z))))
end

MATLAB

x, y, z = num2cell(sort([x, y, z])){:}
function tmp = code(x, y, z)
	tmp = x * (y + ((z * 3.0) / (x / z)));
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := N[(x * N[(y + N[(N[(z * 3.0), $MachinePrecision] / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.1%

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

3. Taylor expanded in x around inf 94.7%

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

5. Simplified 94.7%

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

   1. unpow2 94.7%

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

   2. associate-/l* 95.4%

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

7. Applied egg-rr 95.4%

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

   1. clear-num 95.4%

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

   2. un-div-inv 95.5%

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

9. Applied egg-rr 95.5%

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

    1. metadata-eval 95.5%

       \[\leadsto x \cdot \left(y + \frac{z}{\frac{x}{z}} \cdot \color{blue}{{3}^{1}}\right) \]

    2. metadata-eval 95.5%

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

    3. sqrt-pow2 95.3%

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

    4. associate-*l/ 95.3%

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

    5. sqrt-pow2 95.5%

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

    6. metadata-eval 95.5%

       \[\leadsto x \cdot \left(y + \frac{z \cdot {3}^{\color{blue}{1}}}{\frac{x}{z}}\right) \]

    7. metadata-eval 95.5%

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

11. Applied egg-rr 95.5%

    \[\leadsto x \cdot \left(y + \color{blue}{\frac{z \cdot 3}{\frac{x}{z}}}\right) \]
12. Add Preprocessing

Alternative 8: 93.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ x \cdot \left(y + 3 \cdot \frac{z}{\frac{x}{z}}\right) \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z) :precision binary64 (* x (+ y (* 3.0 (/ z (/ x z))))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	return x * (y + (3.0 * (z / (x / z))));
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
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 + (3.0d0 * (z / (x / z))))
end function

Java

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

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	return x * (y + (3.0 * (z / (x / z))))

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	return Float64(x * Float64(y + Float64(3.0 * Float64(z / Float64(x / z)))))
end

MATLAB

x, y, z = num2cell(sort([x, y, z])){:}
function tmp = code(x, y, z)
	tmp = x * (y + (3.0 * (z / (x / z))));
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := N[(x * N[(y + N[(3.0 * N[(z / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.1%

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

3. Taylor expanded in x around inf 94.7%

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

5. Simplified 94.7%

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

   1. unpow2 94.7%

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

   2. associate-/l* 95.4%

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

7. Applied egg-rr 95.4%

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

   1. clear-num 95.4%

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

   2. un-div-inv 95.5%

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

9. Applied egg-rr 95.5%

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

10. Final simplification 95.5%

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

Alternative 9: 93.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ x \cdot \left(y + 3 \cdot \left(z \cdot \frac{z}{x}\right)\right) \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z) :precision binary64 (* x (+ y (* 3.0 (* z (/ z x))))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	return x * (y + (3.0 * (z * (z / x))));
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
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 + (3.0d0 * (z * (z / x))))
end function

Java

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

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	return x * (y + (3.0 * (z * (z / x))))

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	return Float64(x * Float64(y + Float64(3.0 * Float64(z * Float64(z / x)))))
end

MATLAB

x, y, z = num2cell(sort([x, y, z])){:}
function tmp = code(x, y, z)
	tmp = x * (y + (3.0 * (z * (z / x))));
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := N[(x * N[(y + N[(3.0 * N[(z * N[(z / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.1%

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

3. Taylor expanded in x around inf 94.7%

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

5. Simplified 94.7%

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

   1. unpow2 94.7%

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

   2. associate-/l* 95.4%

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

7. Applied egg-rr 95.4%

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

8. Final simplification 95.4%

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

Alternative 10: 53.5% accurate, 5.0× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ x \cdot y \end{array} \]

FPCore

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

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	return x * y;
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
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
end function

Java

assert x < y && y < z;
public static double code(double x, double y, double z) {
	return x * y;
}

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	return x * y

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 97.1%

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

3. Taylor expanded in x around inf 50.2%

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

Alternative 11: 2.2% accurate, 15.0× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ -3 \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z) :precision binary64 -3.0)

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	return -3.0;
}

Fortran

NOTE: x, y, and z should be sorted in increasing order before calling this function.
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
end function

Java

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

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	return -3.0

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	return -3.0
end

MATLAB

x, y, z = num2cell(sort([x, y, z])){:}
function tmp = code(x, y, z)
	tmp = -3.0;
end

Wolfram

NOTE: x, y, and z should be sorted in increasing order before calling this function.
code[x_, y_, z_] := -3.0

Derivation

1. Initial program 97.1%

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

3. Taylor expanded in x around inf 94.7%

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

5. Simplified 94.7%

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

   1. unpow2 94.7%

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

   2. associate-/l* 95.4%

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

7. Applied egg-rr 95.4%

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

   1. clear-num 95.4%

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

   2. un-div-inv 95.5%

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

9. Applied egg-rr 95.5%

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

10. Taylor expanded in x around 0 58.4%

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

12. Simplified 2.3%

    \[\leadsto \color{blue}{-3} \]
13. Add Preprocessing

Developer Target 1: 98.3% accurate, 1.7× 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 2024165 
(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)))