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

Percentage Accurate: 98.2% → 99.8%

Time: 9.1s

Alternatives: 10

Speedup: 0.7×

• 35.2% 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: 98.2% 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

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \begin{array}{l} \mathbf{if}\;x \leq -5.4 \cdot 10^{+72}:\\ \;\;\;\;x \cdot \left(y + 3 \cdot \left(z \cdot \left(z \cdot \frac{1}{x}\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot z + \left(z \cdot z + \left(z \cdot z + x \cdot y\right)\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= x -5.4e+72)
   (* x (+ y (* 3.0 (* z (* z (/ 1.0 x))))))
   (+ (* 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 <= -5.4e+72) {
		tmp = x * (y + (3.0 * (z * (z * (1.0 / x)))));
	} 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 <= (-5.4d+72)) then
        tmp = x * (y + (3.0d0 * (z * (z * (1.0d0 / x)))))
    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 <= -5.4e+72) {
		tmp = x * (y + (3.0 * (z * (z * (1.0 / x)))));
	} 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 <= -5.4e+72:
		tmp = x * (y + (3.0 * (z * (z * (1.0 / x)))))
	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 (x <= -5.4e+72)
		tmp = Float64(x * Float64(y + Float64(3.0 * Float64(z * Float64(z * Float64(1.0 / x))))));
	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 <= -5.4e+72)
		tmp = x * (y + (3.0 * (z * (z * (1.0 / x)))));
	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[x, -5.4e+72], N[(x * N[(y + N[(3.0 * N[(z * N[(z * N[(1.0 / x), $MachinePrecision]), $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 x < -5.4000000000000001e72

   1. Initial program 90.8%

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

   3. Taylor expanded in x around inf 98.1%

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

   5. Simplified 98.1%

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

      1. div-inv 98.1%

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

      2. unpow2 98.1%

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

      3. associate-*l* 99.9%

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

   7. Applied egg-rr 99.9%

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

   if -5.4000000000000001e72 < x

   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. Recombined 2 regimes into one program.

4. Final simplification 97.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -5.4 \cdot 10^{+72}:\\ \;\;\;\;x \cdot \left(y + 3 \cdot \left(z \cdot \left(z \cdot \frac{1}{x}\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;z \cdot z + \left(z \cdot z + \left(z \cdot z + x \cdot y\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.5% 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 95.9%

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

   1. +-commutative 95.9%

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

   2. fma-define 96.0%

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

   3. associate-+l+ 96.0%

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

   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. Final simplification 99.2%

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

Alternative 3: 99.5% 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 95.9%

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

   1. associate-+l+ 95.9%

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

   2. associate-+l+ 95.9%

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

   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. associate-+r+ 99.1%

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

   5. distribute-lft-out 99.1%

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

   6. distribute-lft-out 99.1%

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

   7. remove-double-neg 99.1%

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

   8. unsub-neg 99.1%

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

   9. count-2 99.1%

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

   10. neg-mul-1 99.1%

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

   11. distribute-rgt-out-- 99.1%

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

   12. 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. Final simplification 99.1%

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

Alternative 4: 97.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{-130}:\\ \;\;\;\;x \cdot \left(y + 3 \cdot \frac{z}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \left(z \cdot \frac{z}{y}\right)\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= x -3.8e-130)
   (* x (+ y (* 3.0 (/ z (/ 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 (x <= -3.8e-130) {
		tmp = x * (y + (3.0 * (z / (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 (x <= (-3.8d-130)) then
        tmp = x * (y + (3.0d0 * (z / (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 (x <= -3.8e-130) {
		tmp = x * (y + (3.0 * (z / (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 x <= -3.8e-130:
		tmp = x * (y + (3.0 * (z / (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 (x <= -3.8e-130)
		tmp = Float64(x * Float64(y + Float64(3.0 * Float64(z / 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 (x <= -3.8e-130)
		tmp = x * (y + (3.0 * (z / (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[x, -3.8e-130], N[(x * N[(y + N[(3.0 * N[(z / N[(x / z), $MachinePrecision]), $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 x < -3.7999999999999998e-130

   1. Initial program 94.3%

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

   3. Taylor expanded in x around inf 96.7%

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

   5. Simplified 96.7%

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

      1. unpow2 96.7%

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

      2. associate-/l* 97.8%

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

   7. Applied egg-rr 97.8%

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

      1. clear-num 97.7%

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

      2. un-div-inv 97.8%

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

   9. Applied egg-rr 97.8%

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

   if -3.7999999999999998e-130 < x

   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 91.8%

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

   5. Simplified 91.8%

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

      1. unpow2 91.8%

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

      2. associate-/l* 92.4%

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

   7. Applied egg-rr 92.4%

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

4. Final simplification 94.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{-130}:\\ \;\;\;\;x \cdot \left(y + 3 \cdot \frac{z}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \left(z \cdot \frac{z}{y}\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 97.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \begin{array}{l} \mathbf{if}\;x \leq -9.6 \cdot 10^{-136}:\\ \;\;\;\;x \cdot \left(y + 3 \cdot \frac{z}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \frac{z}{\frac{y}{z}}\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= x -9.6e-136)
   (* x (+ y (* 3.0 (/ z (/ 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 (x <= -9.6e-136) {
		tmp = x * (y + (3.0 * (z / (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 (x <= (-9.6d-136)) then
        tmp = x * (y + (3.0d0 * (z / (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 (x <= -9.6e-136) {
		tmp = x * (y + (3.0 * (z / (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 x <= -9.6e-136:
		tmp = x * (y + (3.0 * (z / (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 (x <= -9.6e-136)
		tmp = Float64(x * Float64(y + Float64(3.0 * Float64(z / 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 (x <= -9.6e-136)
		tmp = x * (y + (3.0 * (z / (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[x, -9.6e-136], N[(x * N[(y + N[(3.0 * N[(z / N[(x / z), $MachinePrecision]), $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 x < -9.5999999999999994e-136

   1. Initial program 94.3%

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

   3. Taylor expanded in x around inf 96.7%

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

   5. Simplified 96.7%

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

      1. unpow2 96.7%

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

      2. associate-/l* 97.8%

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

   7. Applied egg-rr 97.8%

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

      1. clear-num 97.7%

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

      2. un-div-inv 97.8%

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

   9. Applied egg-rr 97.8%

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

   if -9.5999999999999994e-136 < x

   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 91.8%

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

   5. Simplified 91.8%

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

      1. unpow2 91.8%

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

      2. associate-/l* 92.4%

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

   7. Applied egg-rr 92.4%

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

      1. clear-num 92.4%

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

      2. un-div-inv 92.5%

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

   9. Applied egg-rr 92.5%

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

4. Final simplification 94.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9.6 \cdot 10^{-136}:\\ \;\;\;\;x \cdot \left(y + 3 \cdot \frac{z}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 3 \cdot \frac{z}{\frac{y}{z}}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 97.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} [x, y, z] = \mathsf{sort}([x, y, z])\\ \\ \begin{array}{l} \mathbf{if}\;x \leq -2.5 \cdot 10^{-95}:\\ \;\;\;\;x \cdot \left(y + 3 \cdot \frac{z}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + \frac{z \cdot 3}{\frac{y}{z}}\right)\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, and z should be sorted in increasing order before calling this function.
(FPCore (x y z)
 :precision binary64
 (if (<= x -2.5e-95)
   (* x (+ y (* 3.0 (/ z (/ x z)))))
   (* y (+ x (/ (* z 3.0) (/ y z))))))

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	double tmp;
	if (x <= -2.5e-95) {
		tmp = x * (y + (3.0 * (z / (x / z))));
	} else {
		tmp = y * (x + ((z * 3.0) / (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 (x <= (-2.5d-95)) then
        tmp = x * (y + (3.0d0 * (z / (x / z))))
    else
        tmp = y * (x + ((z * 3.0d0) / (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 (x <= -2.5e-95) {
		tmp = x * (y + (3.0 * (z / (x / z))));
	} else {
		tmp = y * (x + ((z * 3.0) / (y / z)));
	}
	return tmp;
}

Python

[x, y, z] = sort([x, y, z])
def code(x, y, z):
	tmp = 0
	if x <= -2.5e-95:
		tmp = x * (y + (3.0 * (z / (x / z))))
	else:
		tmp = y * (x + ((z * 3.0) / (y / z)))
	return tmp

Julia

x, y, z = sort([x, y, z])
function code(x, y, z)
	tmp = 0.0
	if (x <= -2.5e-95)
		tmp = Float64(x * Float64(y + Float64(3.0 * Float64(z / Float64(x / z)))));
	else
		tmp = Float64(y * Float64(x + Float64(Float64(z * 3.0) / 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 (x <= -2.5e-95)
		tmp = x * (y + (3.0 * (z / (x / z))));
	else
		tmp = y * (x + ((z * 3.0) / (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[x, -2.5e-95], N[(x * N[(y + N[(3.0 * N[(z / N[(x / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y * N[(x + N[(N[(z * 3.0), $MachinePrecision] / N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.4999999999999999e-95

   1. Initial program 93.8%

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

   3. Taylor expanded in x around inf 98.6%

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

   5. Simplified 98.6%

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

      1. unpow2 98.6%

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

      2. associate-/l* 99.8%

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

   7. Applied egg-rr 99.8%

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

      1. clear-num 99.8%

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

      2. un-div-inv 99.8%

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

   9. Applied egg-rr 99.8%

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

   if -2.4999999999999999e-95 < x

   1. Initial program 97.0%

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

   3. Taylor expanded in y around inf 91.7%

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

   5. Simplified 91.7%

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

      1. unpow2 91.7%

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

      2. associate-/l* 92.3%

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

   7. Applied egg-rr 92.3%

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

      1. clear-num 92.3%

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

      2. un-div-inv 92.3%

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

   9. Applied egg-rr 92.3%

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

       1. associate-*l/ 92.3%

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

   11. Applied egg-rr 92.3%

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

4. Final simplification 94.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2.5 \cdot 10^{-95}:\\ \;\;\;\;x \cdot \left(y + 3 \cdot \frac{z}{\frac{x}{z}}\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + \frac{z \cdot 3}{\frac{y}{z}}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 94.3% 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 95.9%

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

3. Taylor expanded in x around inf 90.7%

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

5. Simplified 90.7%

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

   1. unpow2 90.7%

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

   2. associate-/l* 91.5%

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

7. Applied egg-rr 91.5%

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

8. Final simplification 91.5%

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

Alternative 8: 94.3% 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 95.9%

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

3. Taylor expanded in x around inf 90.7%

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

5. Simplified 90.7%

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

   1. unpow2 90.7%

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

   2. associate-/l* 91.5%

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

7. Applied egg-rr 91.5%

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

   1. clear-num 91.4%

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

   2. un-div-inv 91.5%

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

9. Applied egg-rr 91.5%

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

    1. associate-*l/ 91.5%

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

11. Applied egg-rr 91.5%

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

12. Final simplification 91.5%

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

Alternative 9: 77.7% accurate, 2.1× speedup

Math

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

FPCore

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

C

assert(x < y && y < z);
double code(double x, double y, double z) {
	return (z * z) + (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 = (z * z) + (x * y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 95.9%

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

3. Taylor expanded in x around inf 75.2%

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

4. Taylor expanded in x around inf 74.6%

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

5. Final simplification 74.6%

   \[\leadsto z \cdot z + x \cdot y \]
6. Add Preprocessing

Alternative 10: 52.6% 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 95.9%

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

3. Taylor expanded in y around inf 91.4%

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

5. Simplified 91.4%

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

6. Taylor expanded in x around inf 49.4%

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

7. Final simplification 49.4%

   \[\leadsto x \cdot y \]
8. Add Preprocessing

Developer target: 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 2024071 
(FPCore (x y z)
  :name "Linear.Quaternion:$c/ from linear-1.19.1.3, A"
  :precision binary64

  :alt
  (+ (* (* 3.0 z) z) (* y x))

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