Diagrams.TwoD.Apollonian:initialConfig from diagrams-contrib-1.3.0.5, B

Percentage Accurate: 67.9% → 99.5%

Time: 8.5s

Alternatives: 5

Speedup: 18.0×

Specification

Math

\[\begin{array}{l} \\ x \cdot \sqrt{y \cdot y - z \cdot z} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* x (sqrt (- (* y y) (* z z)))))

C

double code(double x, double y, double z) {
	return x * sqrt(((y * y) - (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 * sqrt(((y * y) - (z * z)))
end function

Java

public static double code(double x, double y, double z) {
	return x * Math.sqrt(((y * y) - (z * z)));
}

Python

def code(x, y, z):
	return x * math.sqrt(((y * y) - (z * z)))

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 67.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \sqrt{y \cdot y - z \cdot z} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* x (sqrt (- (* y y) (* z z)))))

C

double code(double x, double y, double z) {
	return x * sqrt(((y * y) - (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 * sqrt(((y * y) - (z * z)))
end function

Java

public static double code(double x, double y, double z) {
	return x * Math.sqrt(((y * y) - (z * z)));
}

Python

def code(x, y, z):
	return x * math.sqrt(((y * y) - (z * z)))

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-274}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(\sqrt{y + z} \cdot \sqrt{y - z}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -5e-274)
   (* x (- (* 0.5 (/ z (/ y z))) y))
   (* x (* (sqrt (+ y z)) (sqrt (- y z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -5e-274) {
		tmp = x * ((0.5 * (z / (y / z))) - y);
	} else {
		tmp = x * (sqrt((y + z)) * sqrt((y - z)));
	}
	return tmp;
}

Fortran

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 <= (-5d-274)) then
        tmp = x * ((0.5d0 * (z / (y / z))) - y)
    else
        tmp = x * (sqrt((y + z)) * sqrt((y - z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -5e-274) {
		tmp = x * ((0.5 * (z / (y / z))) - y);
	} else {
		tmp = x * (Math.sqrt((y + z)) * Math.sqrt((y - z)));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -5e-274:
		tmp = x * ((0.5 * (z / (y / z))) - y)
	else:
		tmp = x * (math.sqrt((y + z)) * math.sqrt((y - z)))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -5e-274)
		tmp = Float64(x * Float64(Float64(0.5 * Float64(z / Float64(y / z))) - y));
	else
		tmp = Float64(x * Float64(sqrt(Float64(y + z)) * sqrt(Float64(y - z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -5e-274)
		tmp = x * ((0.5 * (z / (y / z))) - y);
	else
		tmp = x * (sqrt((y + z)) * sqrt((y - z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -5e-274], N[(x * N[(N[(0.5 * N[(z / N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - y), $MachinePrecision]), $MachinePrecision], N[(x * N[(N[Sqrt[N[(y + z), $MachinePrecision]], $MachinePrecision] * N[Sqrt[N[(y - z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -5e-274

   1. Initial program 68.9%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around -inf 94.3%

      \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{{z}^{2}}{y} + -1 \cdot y\right)} \]
   3. Step-by-step derivation

      1. mul-1-neg 94.3%

         \[\leadsto x \cdot \left(0.5 \cdot \frac{{z}^{2}}{y} + \color{blue}{\left(-y\right)}\right) \]

      2. unsub-neg 94.3%

         \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{{z}^{2}}{y} - y\right)} \]

      3. unpow2 94.3%

         \[\leadsto x \cdot \left(0.5 \cdot \frac{\color{blue}{z \cdot z}}{y} - y\right) \]

      4. associate-/l* 99.7%

         \[\leadsto x \cdot \left(0.5 \cdot \color{blue}{\frac{z}{\frac{y}{z}}} - y\right) \]

   4. Simplified 99.7%

      \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)} \]

   if -5e-274 < y

   1. Initial program 66.4%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]
   2. Step-by-step derivation

      1. difference-of-squares 66.7%

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

      2. sqrt-prod 99.5%

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

   3. Applied egg-rr 99.5%

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

      1. +-commutative 99.5%

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

   5. Simplified 99.5%

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

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-274}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(\sqrt{y + z} \cdot \sqrt{y - z}\right)\\ \end{array} \]

Alternative 2: 99.4% accurate, 8.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-274}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -5e-274) (* x (- (* 0.5 (/ z (/ y z))) y)) (* y x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -5e-274) {
		tmp = x * ((0.5 * (z / (y / z))) - y);
	} else {
		tmp = y * x;
	}
	return tmp;
}

Fortran

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 <= (-5d-274)) then
        tmp = x * ((0.5d0 * (z / (y / z))) - y)
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -5e-274) {
		tmp = x * ((0.5 * (z / (y / z))) - y);
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -5e-274:
		tmp = x * ((0.5 * (z / (y / z))) - y)
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -5e-274)
		tmp = Float64(x * Float64(Float64(0.5 * Float64(z / Float64(y / z))) - y));
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -5e-274)
		tmp = x * ((0.5 * (z / (y / z))) - y);
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -5e-274], N[(x * N[(N[(0.5 * N[(z / N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - y), $MachinePrecision]), $MachinePrecision], N[(y * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -5e-274

   1. Initial program 68.9%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around -inf 94.3%

      \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{{z}^{2}}{y} + -1 \cdot y\right)} \]
   3. Step-by-step derivation

      1. mul-1-neg 94.3%

         \[\leadsto x \cdot \left(0.5 \cdot \frac{{z}^{2}}{y} + \color{blue}{\left(-y\right)}\right) \]

      2. unsub-neg 94.3%

         \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{{z}^{2}}{y} - y\right)} \]

      3. unpow2 94.3%

         \[\leadsto x \cdot \left(0.5 \cdot \frac{\color{blue}{z \cdot z}}{y} - y\right) \]

      4. associate-/l* 99.7%

         \[\leadsto x \cdot \left(0.5 \cdot \color{blue}{\frac{z}{\frac{y}{z}}} - y\right) \]

   4. Simplified 99.7%

      \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)} \]

   if -5e-274 < y

   1. Initial program 66.4%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around inf 98.5%

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

4. Final simplification 99.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-274}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 3: 99.6% accurate, 8.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-274}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y - z \cdot \left(0.5 \cdot \frac{z}{y}\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -5e-274)
   (* x (- (* 0.5 (/ z (/ y z))) y))
   (* x (- y (* z (* 0.5 (/ z y)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -5e-274) {
		tmp = x * ((0.5 * (z / (y / z))) - y);
	} else {
		tmp = x * (y - (z * (0.5 * (z / y))));
	}
	return tmp;
}

Fortran

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 <= (-5d-274)) then
        tmp = x * ((0.5d0 * (z / (y / z))) - y)
    else
        tmp = x * (y - (z * (0.5d0 * (z / y))))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -5e-274) {
		tmp = x * ((0.5 * (z / (y / z))) - y);
	} else {
		tmp = x * (y - (z * (0.5 * (z / y))));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -5e-274:
		tmp = x * ((0.5 * (z / (y / z))) - y)
	else:
		tmp = x * (y - (z * (0.5 * (z / y))))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -5e-274)
		tmp = Float64(x * Float64(Float64(0.5 * Float64(z / Float64(y / z))) - y));
	else
		tmp = Float64(x * Float64(y - Float64(z * Float64(0.5 * Float64(z / y)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -5e-274)
		tmp = x * ((0.5 * (z / (y / z))) - y);
	else
		tmp = x * (y - (z * (0.5 * (z / y))));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -5e-274], N[(x * N[(N[(0.5 * N[(z / N[(y / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - y), $MachinePrecision]), $MachinePrecision], N[(x * N[(y - N[(z * N[(0.5 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -5e-274

   1. Initial program 68.9%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around -inf 94.3%

      \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{{z}^{2}}{y} + -1 \cdot y\right)} \]
   3. Step-by-step derivation

      1. mul-1-neg 94.3%

         \[\leadsto x \cdot \left(0.5 \cdot \frac{{z}^{2}}{y} + \color{blue}{\left(-y\right)}\right) \]

      2. unsub-neg 94.3%

         \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{{z}^{2}}{y} - y\right)} \]

      3. unpow2 94.3%

         \[\leadsto x \cdot \left(0.5 \cdot \frac{\color{blue}{z \cdot z}}{y} - y\right) \]

      4. associate-/l* 99.7%

         \[\leadsto x \cdot \left(0.5 \cdot \color{blue}{\frac{z}{\frac{y}{z}}} - y\right) \]

   4. Simplified 99.7%

      \[\leadsto x \cdot \color{blue}{\left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)} \]

   if -5e-274 < y

   1. Initial program 66.4%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around inf 92.5%

      \[\leadsto x \cdot \color{blue}{\left(y + -0.5 \cdot \frac{{z}^{2}}{y}\right)} \]
   3. Step-by-step derivation

      1. +-commutative 92.5%

         \[\leadsto x \cdot \color{blue}{\left(-0.5 \cdot \frac{{z}^{2}}{y} + y\right)} \]

      2. fma-def 92.5%

         \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(-0.5, \frac{{z}^{2}}{y}, y\right)} \]

      3. unpow2 92.5%

         \[\leadsto x \cdot \mathsf{fma}\left(-0.5, \frac{\color{blue}{z \cdot z}}{y}, y\right) \]

      4. associate-/l* 99.1%

         \[\leadsto x \cdot \mathsf{fma}\left(-0.5, \color{blue}{\frac{z}{\frac{y}{z}}}, y\right) \]

   4. Simplified 99.1%

      \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(-0.5, \frac{z}{\frac{y}{z}}, y\right)} \]
   5. Step-by-step derivation

      1. fma-udef 99.1%

         \[\leadsto x \cdot \color{blue}{\left(-0.5 \cdot \frac{z}{\frac{y}{z}} + y\right)} \]

      2. distribute-rgt-in 99.1%

         \[\leadsto \color{blue}{\left(-0.5 \cdot \frac{z}{\frac{y}{z}}\right) \cdot x + y \cdot x} \]

      3. associate-/r/ 99.1%

         \[\leadsto \left(-0.5 \cdot \color{blue}{\left(\frac{z}{y} \cdot z\right)}\right) \cdot x + y \cdot x \]

      4. *-commutative 99.1%

         \[\leadsto \left(-0.5 \cdot \color{blue}{\left(z \cdot \frac{z}{y}\right)}\right) \cdot x + y \cdot x \]

      5. associate-*r* 99.1%

         \[\leadsto \color{blue}{\left(\left(-0.5 \cdot z\right) \cdot \frac{z}{y}\right)} \cdot x + y \cdot x \]

   6. Applied egg-rr 99.1%

      \[\leadsto \color{blue}{\left(\left(-0.5 \cdot z\right) \cdot \frac{z}{y}\right) \cdot x + y \cdot x} \]

   7. Taylor expanded in x around -inf 92.5%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(0.5 \cdot \frac{{z}^{2}}{y} + -1 \cdot y\right)\right)} \]
   8. Step-by-step derivation

      1. mul-1-neg 92.5%

         \[\leadsto \color{blue}{-x \cdot \left(0.5 \cdot \frac{{z}^{2}}{y} + -1 \cdot y\right)} \]

      2. distribute-rgt-neg-in 92.5%

         \[\leadsto \color{blue}{x \cdot \left(-\left(0.5 \cdot \frac{{z}^{2}}{y} + -1 \cdot y\right)\right)} \]

      3. neg-mul-1 92.5%

         \[\leadsto x \cdot \left(-\left(0.5 \cdot \frac{{z}^{2}}{y} + \color{blue}{\left(-y\right)}\right)\right) \]

      4. unsub-neg 92.5%

         \[\leadsto x \cdot \left(-\color{blue}{\left(0.5 \cdot \frac{{z}^{2}}{y} - y\right)}\right) \]

      5. *-commutative 92.5%

         \[\leadsto x \cdot \left(-\left(\color{blue}{\frac{{z}^{2}}{y} \cdot 0.5} - y\right)\right) \]

      6. unpow2 92.5%

         \[\leadsto x \cdot \left(-\left(\frac{\color{blue}{z \cdot z}}{y} \cdot 0.5 - y\right)\right) \]

      7. associate-*r/ 99.1%

         \[\leadsto x \cdot \left(-\left(\color{blue}{\left(z \cdot \frac{z}{y}\right)} \cdot 0.5 - y\right)\right) \]

      8. associate-*l* 99.1%

         \[\leadsto x \cdot \left(-\left(\color{blue}{z \cdot \left(\frac{z}{y} \cdot 0.5\right)} - y\right)\right) \]

   9. Simplified 99.1%

      \[\leadsto \color{blue}{x \cdot \left(-\left(z \cdot \left(\frac{z}{y} \cdot 0.5\right) - y\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-274}:\\ \;\;\;\;x \cdot \left(0.5 \cdot \frac{z}{\frac{y}{z}} - y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y - z \cdot \left(0.5 \cdot \frac{z}{y}\right)\right)\\ \end{array} \]

Alternative 4: 99.2% accurate, 18.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-274}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (if (<= y -5e-274) (* y (- x)) (* y x)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -5e-274) {
		tmp = y * -x;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Fortran

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 <= (-5d-274)) then
        tmp = y * -x
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -5e-274) {
		tmp = y * -x;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -5e-274:
		tmp = y * -x
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -5e-274)
		tmp = Float64(y * Float64(-x));
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -5e-274)
		tmp = y * -x;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -5e-274], N[(y * (-x)), $MachinePrecision], N[(y * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -5e-274

   1. Initial program 68.9%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around -inf 98.9%

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

      1. associate-*r* 98.9%

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

      2. mul-1-neg 98.9%

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

   4. Simplified 98.9%

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

   if -5e-274 < y

   1. Initial program 66.4%

      \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

   2. Taylor expanded in y around inf 98.5%

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

4. Final simplification 98.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5 \cdot 10^{-274}:\\ \;\;\;\;y \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 5: 52.3% accurate, 36.3× speedup

Math

\[\begin{array}{l} \\ y \cdot x \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 67.7%

   \[x \cdot \sqrt{y \cdot y - z \cdot z} \]

2. Taylor expanded in y around inf 48.9%

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

3. Final simplification 48.9%

   \[\leadsto y \cdot x \]

Developer target: 99.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y < 2.5816096488251695 \cdot 10^{-278}:\\ \;\;\;\;-x \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(\sqrt{y + z} \cdot \sqrt{y - z}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (< y 2.5816096488251695e-278)
   (- (* x y))
   (* x (* (sqrt (+ y z)) (sqrt (- y z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y < 2.5816096488251695e-278) {
		tmp = -(x * y);
	} else {
		tmp = x * (sqrt((y + z)) * sqrt((y - z)));
	}
	return tmp;
}

Fortran

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 < 2.5816096488251695d-278) then
        tmp = -(x * y)
    else
        tmp = x * (sqrt((y + z)) * sqrt((y - z)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y < 2.5816096488251695e-278) {
		tmp = -(x * y);
	} else {
		tmp = x * (Math.sqrt((y + z)) * Math.sqrt((y - z)));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y < 2.5816096488251695e-278:
		tmp = -(x * y)
	else:
		tmp = x * (math.sqrt((y + z)) * math.sqrt((y - z)))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y < 2.5816096488251695e-278)
		tmp = Float64(-Float64(x * y));
	else
		tmp = Float64(x * Float64(sqrt(Float64(y + z)) * sqrt(Float64(y - z))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y < 2.5816096488251695e-278)
		tmp = -(x * y);
	else
		tmp = x * (sqrt((y + z)) * sqrt((y - z)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Less[y, 2.5816096488251695e-278], (-N[(x * y), $MachinePrecision]), N[(x * N[(N[Sqrt[N[(y + z), $MachinePrecision]], $MachinePrecision] * N[Sqrt[N[(y - z), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Reproduce

herbie shell --seed 2023195 
(FPCore (x y z)
  :name "Diagrams.TwoD.Apollonian:initialConfig from diagrams-contrib-1.3.0.5, B"
  :precision binary64

  :herbie-target
  (if (< y 2.5816096488251695e-278) (- (* x y)) (* x (* (sqrt (+ y z)) (sqrt (- y z)))))

  (* x (sqrt (- (* y y) (* z z)))))