2-ancestry mixing, positive discriminant

Percentage Accurate: 44.9% → 95.9%

Time: 14.7s

Alternatives: 6

Speedup: 2.6×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{2 \cdot a}\\ t_1 := \sqrt{g \cdot g - h \cdot h}\\ \sqrt[3]{t\_0 \cdot \left(\left(-g\right) + t\_1\right)} + \sqrt[3]{t\_0 \cdot \left(\left(-g\right) - t\_1\right)} \end{array} \end{array} \]

FPCore

(FPCore (g h a)
 :precision binary64
 (let* ((t_0 (/ 1.0 (* 2.0 a))) (t_1 (sqrt (- (* g g) (* h h)))))
   (+ (cbrt (* t_0 (+ (- g) t_1))) (cbrt (* t_0 (- (- g) t_1))))))

C

double code(double g, double h, double a) {
	double t_0 = 1.0 / (2.0 * a);
	double t_1 = sqrt(((g * g) - (h * h)));
	return cbrt((t_0 * (-g + t_1))) + cbrt((t_0 * (-g - t_1)));
}

Java

public static double code(double g, double h, double a) {
	double t_0 = 1.0 / (2.0 * a);
	double t_1 = Math.sqrt(((g * g) - (h * h)));
	return Math.cbrt((t_0 * (-g + t_1))) + Math.cbrt((t_0 * (-g - t_1)));
}

Julia

function code(g, h, a)
	t_0 = Float64(1.0 / Float64(2.0 * a))
	t_1 = sqrt(Float64(Float64(g * g) - Float64(h * h)))
	return Float64(cbrt(Float64(t_0 * Float64(Float64(-g) + t_1))) + cbrt(Float64(t_0 * Float64(Float64(-g) - t_1))))
end

Wolfram

code[g_, h_, a_] := Block[{t$95$0 = N[(1.0 / N[(2.0 * a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sqrt[N[(N[(g * g), $MachinePrecision] - N[(h * h), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, N[(N[Power[N[(t$95$0 * N[((-g) + t$95$1), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision] + N[Power[N[(t$95$0 * N[((-g) - t$95$1), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]]]

Initial Program: 44.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{2 \cdot a}\\ t_1 := \sqrt{g \cdot g - h \cdot h}\\ \sqrt[3]{t\_0 \cdot \left(\left(-g\right) + t\_1\right)} + \sqrt[3]{t\_0 \cdot \left(\left(-g\right) - t\_1\right)} \end{array} \end{array} \]

FPCore

(FPCore (g h a)
 :precision binary64
 (let* ((t_0 (/ 1.0 (* 2.0 a))) (t_1 (sqrt (- (* g g) (* h h)))))
   (+ (cbrt (* t_0 (+ (- g) t_1))) (cbrt (* t_0 (- (- g) t_1))))))

C

double code(double g, double h, double a) {
	double t_0 = 1.0 / (2.0 * a);
	double t_1 = sqrt(((g * g) - (h * h)));
	return cbrt((t_0 * (-g + t_1))) + cbrt((t_0 * (-g - t_1)));
}

Java

public static double code(double g, double h, double a) {
	double t_0 = 1.0 / (2.0 * a);
	double t_1 = Math.sqrt(((g * g) - (h * h)));
	return Math.cbrt((t_0 * (-g + t_1))) + Math.cbrt((t_0 * (-g - t_1)));
}

Julia

function code(g, h, a)
	t_0 = Float64(1.0 / Float64(2.0 * a))
	t_1 = sqrt(Float64(Float64(g * g) - Float64(h * h)))
	return Float64(cbrt(Float64(t_0 * Float64(Float64(-g) + t_1))) + cbrt(Float64(t_0 * Float64(Float64(-g) - t_1))))
end

Wolfram

code[g_, h_, a_] := Block[{t$95$0 = N[(1.0 / N[(2.0 * a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sqrt[N[(N[(g * g), $MachinePrecision] - N[(h * h), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, N[(N[Power[N[(t$95$0 * N[((-g) + t$95$1), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision] + N[Power[N[(t$95$0 * N[((-g) - t$95$1), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]]]

Alternative 1: 95.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \frac{\sqrt[3]{-g}}{\sqrt[3]{a}} \end{array} \]

FPCore

(FPCore (g h a) :precision binary64 (/ (cbrt (- g)) (cbrt a)))

C

double code(double g, double h, double a) {
	return cbrt(-g) / cbrt(a);
}

Java

public static double code(double g, double h, double a) {
	return Math.cbrt(-g) / Math.cbrt(a);
}

Julia

function code(g, h, a)
	return Float64(cbrt(Float64(-g)) / cbrt(a))
end

Wolfram

code[g_, h_, a_] := N[(N[Power[(-g), 1/3], $MachinePrecision] / N[Power[a, 1/3], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 46.4%

   \[\sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) - \sqrt{g \cdot g - h \cdot h}\right)} \]
2. Add Preprocessing

4. Applied rewrites 49.2%

   \[\leadsto \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \color{blue}{\frac{\sqrt[3]{g + \sqrt{\left(g + h\right) \cdot \left(g - h\right)}}}{\sqrt[3]{a \cdot -2}}} \]

5. Taylor expanded in g around inf

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \frac{\sqrt[3]{2}}{\sqrt[3]{-2}}} \]
6. Step-by-step derivation

   1. associate-*r/ N/A

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

   2. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}}{\sqrt[3]{-2}} \]

   4. lower-cbrt.f64 N/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

   5. lower-/.f64 N/A

      \[\leadsto \frac{\sqrt[3]{\color{blue}{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

   6. lower-cbrt.f64 N/A

      \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \color{blue}{\sqrt[3]{2}}}{\sqrt[3]{-2}} \]

   7. lower-cbrt.f64 72.6

      \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\color{blue}{\sqrt[3]{-2}}} \]

7. Applied rewrites 72.6%

   \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]
8. Step-by-step derivation

9. Applied rewrites 96.5%

   \[\leadsto \frac{\sqrt[3]{-g}}{\color{blue}{\sqrt[3]{a}}} \]
10. Add Preprocessing

Alternative 2: 88.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{1}{2 \cdot a} \leq -5 \cdot 10^{-287}:\\ \;\;\;\;{\left(-a\right)}^{-0.3333333333333333} \cdot \sqrt[3]{g}\\ \mathbf{else}:\\ \;\;\;\;\sqrt[3]{-g} \cdot {a}^{-0.3333333333333333}\\ \end{array} \end{array} \]

FPCore

(FPCore (g h a)
 :precision binary64
 (if (<= (/ 1.0 (* 2.0 a)) -5e-287)
   (* (pow (- a) -0.3333333333333333) (cbrt g))
   (* (cbrt (- g)) (pow a -0.3333333333333333))))

C

double code(double g, double h, double a) {
	double tmp;
	if ((1.0 / (2.0 * a)) <= -5e-287) {
		tmp = pow(-a, -0.3333333333333333) * cbrt(g);
	} else {
		tmp = cbrt(-g) * pow(a, -0.3333333333333333);
	}
	return tmp;
}

Java

public static double code(double g, double h, double a) {
	double tmp;
	if ((1.0 / (2.0 * a)) <= -5e-287) {
		tmp = Math.pow(-a, -0.3333333333333333) * Math.cbrt(g);
	} else {
		tmp = Math.cbrt(-g) * Math.pow(a, -0.3333333333333333);
	}
	return tmp;
}

Julia

function code(g, h, a)
	tmp = 0.0
	if (Float64(1.0 / Float64(2.0 * a)) <= -5e-287)
		tmp = Float64((Float64(-a) ^ -0.3333333333333333) * cbrt(g));
	else
		tmp = Float64(cbrt(Float64(-g)) * (a ^ -0.3333333333333333));
	end
	return tmp
end

Wolfram

code[g_, h_, a_] := If[LessEqual[N[(1.0 / N[(2.0 * a), $MachinePrecision]), $MachinePrecision], -5e-287], N[(N[Power[(-a), -0.3333333333333333], $MachinePrecision] * N[Power[g, 1/3], $MachinePrecision]), $MachinePrecision], N[(N[Power[(-g), 1/3], $MachinePrecision] * N[Power[a, -0.3333333333333333], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 #s(literal 1 binary64) (*.f64 #s(literal 2 binary64) a)) < -5.00000000000000025e-287

   1. Initial program 42.0%

      \[\sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) - \sqrt{g \cdot g - h \cdot h}\right)} \]
   2. Add Preprocessing

   4. Applied rewrites 43.8%

      \[\leadsto \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \color{blue}{\frac{\sqrt[3]{g + \sqrt{\left(g + h\right) \cdot \left(g - h\right)}}}{\sqrt[3]{a \cdot -2}}} \]

   5. Taylor expanded in g around inf

      \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \frac{\sqrt[3]{2}}{\sqrt[3]{-2}}} \]
   6. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

      2. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}}{\sqrt[3]{-2}} \]

      4. lower-cbrt.f64 N/A

         \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\sqrt[3]{\color{blue}{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

      6. lower-cbrt.f64 N/A

         \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \color{blue}{\sqrt[3]{2}}}{\sqrt[3]{-2}} \]

      7. lower-cbrt.f64 71.2

         \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\color{blue}{\sqrt[3]{-2}}} \]

   7. Applied rewrites 71.2%

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 96.2%

      \[\leadsto \frac{\sqrt[3]{-g}}{\color{blue}{\sqrt[3]{a}}} \]
   10. Step-by-step derivation

   11. Applied rewrites 89.9%

       \[\leadsto {\left(-a\right)}^{-0.3333333333333333} \cdot \color{blue}{\sqrt[3]{g}} \]

   if -5.00000000000000025e-287 < (/.f64 #s(literal 1 binary64) (*.f64 #s(literal 2 binary64) a))

   1. Initial program 50.4%

      \[\sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) - \sqrt{g \cdot g - h \cdot h}\right)} \]
   2. Add Preprocessing

   4. Applied rewrites 53.9%

      \[\leadsto \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \color{blue}{\frac{\sqrt[3]{g + \sqrt{\left(g + h\right) \cdot \left(g - h\right)}}}{\sqrt[3]{a \cdot -2}}} \]

   5. Taylor expanded in g around inf

      \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \frac{\sqrt[3]{2}}{\sqrt[3]{-2}}} \]
   6. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

      2. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}}{\sqrt[3]{-2}} \]

      4. lower-cbrt.f64 N/A

         \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\sqrt[3]{\color{blue}{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

      6. lower-cbrt.f64 N/A

         \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \color{blue}{\sqrt[3]{2}}}{\sqrt[3]{-2}} \]

      7. lower-cbrt.f64 73.9

         \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\color{blue}{\sqrt[3]{-2}}} \]

   7. Applied rewrites 73.9%

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 96.7%

      \[\leadsto \frac{\sqrt[3]{-g}}{\color{blue}{\sqrt[3]{a}}} \]
   10. Step-by-step derivation

   11. Applied rewrites 90.4%

       \[\leadsto {a}^{-0.3333333333333333} \cdot \color{blue}{\sqrt[3]{-g}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 90.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{1}{2 \cdot a} \leq -5 \cdot 10^{-287}:\\ \;\;\;\;{\left(-a\right)}^{-0.3333333333333333} \cdot \sqrt[3]{g}\\ \mathbf{else}:\\ \;\;\;\;\sqrt[3]{-g} \cdot {a}^{-0.3333333333333333}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 81.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;2 \cdot a \leq 5 \cdot 10^{-301}:\\ \;\;\;\;\sqrt[3]{\frac{g}{\frac{1}{\frac{-1}{a}}}}\\ \mathbf{else}:\\ \;\;\;\;\sqrt[3]{-g} \cdot {a}^{-0.3333333333333333}\\ \end{array} \end{array} \]

FPCore

(FPCore (g h a)
 :precision binary64
 (if (<= (* 2.0 a) 5e-301)
   (cbrt (/ g (/ 1.0 (/ -1.0 a))))
   (* (cbrt (- g)) (pow a -0.3333333333333333))))

C

double code(double g, double h, double a) {
	double tmp;
	if ((2.0 * a) <= 5e-301) {
		tmp = cbrt((g / (1.0 / (-1.0 / a))));
	} else {
		tmp = cbrt(-g) * pow(a, -0.3333333333333333);
	}
	return tmp;
}

Java

public static double code(double g, double h, double a) {
	double tmp;
	if ((2.0 * a) <= 5e-301) {
		tmp = Math.cbrt((g / (1.0 / (-1.0 / a))));
	} else {
		tmp = Math.cbrt(-g) * Math.pow(a, -0.3333333333333333);
	}
	return tmp;
}

Julia

function code(g, h, a)
	tmp = 0.0
	if (Float64(2.0 * a) <= 5e-301)
		tmp = cbrt(Float64(g / Float64(1.0 / Float64(-1.0 / a))));
	else
		tmp = Float64(cbrt(Float64(-g)) * (a ^ -0.3333333333333333));
	end
	return tmp
end

Wolfram

code[g_, h_, a_] := If[LessEqual[N[(2.0 * a), $MachinePrecision], 5e-301], N[Power[N[(g / N[(1.0 / N[(-1.0 / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision], N[(N[Power[(-g), 1/3], $MachinePrecision] * N[Power[a, -0.3333333333333333], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 2 binary64) a) < 5.00000000000000013e-301

   1. Initial program 42.9%

      \[\sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) - \sqrt{g \cdot g - h \cdot h}\right)} \]
   2. Add Preprocessing

   4. Applied rewrites 44.7%

      \[\leadsto \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \color{blue}{\frac{\sqrt[3]{g + \sqrt{\left(g + h\right) \cdot \left(g - h\right)}}}{\sqrt[3]{a \cdot -2}}} \]

   5. Taylor expanded in g around inf

      \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \frac{\sqrt[3]{2}}{\sqrt[3]{-2}}} \]
   6. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

      2. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}}{\sqrt[3]{-2}} \]

      4. lower-cbrt.f64 N/A

         \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\sqrt[3]{\color{blue}{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

      6. lower-cbrt.f64 N/A

         \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \color{blue}{\sqrt[3]{2}}}{\sqrt[3]{-2}} \]

      7. lower-cbrt.f64 71.7

         \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\color{blue}{\sqrt[3]{-2}}} \]

   7. Applied rewrites 71.7%

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 71.7%

      \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{-a}}} \]
   10. Step-by-step derivation

   11. Applied rewrites 71.7%

       \[\leadsto \sqrt[3]{\frac{g}{\frac{1}{\frac{-1}{a}}}} \]

   if 5.00000000000000013e-301 < (*.f64 #s(literal 2 binary64) a)

   1. Initial program 49.6%

      \[\sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) - \sqrt{g \cdot g - h \cdot h}\right)} \]
   2. Add Preprocessing

   4. Applied rewrites 53.3%

      \[\leadsto \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \color{blue}{\frac{\sqrt[3]{g + \sqrt{\left(g + h\right) \cdot \left(g - h\right)}}}{\sqrt[3]{a \cdot -2}}} \]

   5. Taylor expanded in g around inf

      \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \frac{\sqrt[3]{2}}{\sqrt[3]{-2}}} \]
   6. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

      2. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}}{\sqrt[3]{-2}} \]

      4. lower-cbrt.f64 N/A

         \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\sqrt[3]{\color{blue}{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

      6. lower-cbrt.f64 N/A

         \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \color{blue}{\sqrt[3]{2}}}{\sqrt[3]{-2}} \]

      7. lower-cbrt.f64 73.5

         \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\color{blue}{\sqrt[3]{-2}}} \]

   7. Applied rewrites 73.5%

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 96.7%

      \[\leadsto \frac{\sqrt[3]{-g}}{\color{blue}{\sqrt[3]{a}}} \]
   10. Step-by-step derivation

   11. Applied rewrites 90.4%

       \[\leadsto {a}^{-0.3333333333333333} \cdot \color{blue}{\sqrt[3]{-g}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 81.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;2 \cdot a \leq 5 \cdot 10^{-301}:\\ \;\;\;\;\sqrt[3]{\frac{g}{\frac{1}{\frac{-1}{a}}}}\\ \mathbf{else}:\\ \;\;\;\;\sqrt[3]{-g} \cdot {a}^{-0.3333333333333333}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 74.8% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\sqrt[3]{-\frac{a}{g}}} \end{array} \]

FPCore

(FPCore (g h a) :precision binary64 (/ 1.0 (cbrt (- (/ a g)))))

C

double code(double g, double h, double a) {
	return 1.0 / cbrt(-(a / g));
}

Java

public static double code(double g, double h, double a) {
	return 1.0 / Math.cbrt(-(a / g));
}

Julia

function code(g, h, a)
	return Float64(1.0 / cbrt(Float64(-Float64(a / g))))
end

Wolfram

code[g_, h_, a_] := N[(1.0 / N[Power[(-N[(a / g), $MachinePrecision]), 1/3], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 46.4%

   \[\sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) - \sqrt{g \cdot g - h \cdot h}\right)} \]
2. Add Preprocessing

4. Applied rewrites 49.2%

   \[\leadsto \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \color{blue}{\frac{\sqrt[3]{g + \sqrt{\left(g + h\right) \cdot \left(g - h\right)}}}{\sqrt[3]{a \cdot -2}}} \]

5. Taylor expanded in g around inf

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \frac{\sqrt[3]{2}}{\sqrt[3]{-2}}} \]
6. Step-by-step derivation

   1. associate-*r/ N/A

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

   2. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}}{\sqrt[3]{-2}} \]

   4. lower-cbrt.f64 N/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

   5. lower-/.f64 N/A

      \[\leadsto \frac{\sqrt[3]{\color{blue}{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

   6. lower-cbrt.f64 N/A

      \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \color{blue}{\sqrt[3]{2}}}{\sqrt[3]{-2}} \]

   7. lower-cbrt.f64 72.6

      \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\color{blue}{\sqrt[3]{-2}}} \]

7. Applied rewrites 72.6%

   \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]
8. Step-by-step derivation

9. Applied rewrites 96.5%

   \[\leadsto \frac{\sqrt[3]{-g}}{\color{blue}{\sqrt[3]{a}}} \]
10. Step-by-step derivation

11. Applied rewrites 73.6%

    \[\leadsto \frac{1}{\color{blue}{\sqrt[3]{\frac{a}{-g}}}} \]

12. Final simplification 73.6%

    \[\leadsto \frac{1}{\sqrt[3]{-\frac{a}{g}}} \]
13. Add Preprocessing

Alternative 5: 73.9% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \sqrt[3]{\frac{-1}{\frac{a}{g}}} \end{array} \]

FPCore

(FPCore (g h a) :precision binary64 (cbrt (/ -1.0 (/ a g))))

C

double code(double g, double h, double a) {
	return cbrt((-1.0 / (a / g)));
}

Java

public static double code(double g, double h, double a) {
	return Math.cbrt((-1.0 / (a / g)));
}

Julia

function code(g, h, a)
	return cbrt(Float64(-1.0 / Float64(a / g)))
end

Wolfram

code[g_, h_, a_] := N[Power[N[(-1.0 / N[(a / g), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]

Derivation

1. Initial program 46.4%

   \[\sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) - \sqrt{g \cdot g - h \cdot h}\right)} \]
2. Add Preprocessing

4. Applied rewrites 49.2%

   \[\leadsto \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \color{blue}{\frac{\sqrt[3]{g + \sqrt{\left(g + h\right) \cdot \left(g - h\right)}}}{\sqrt[3]{a \cdot -2}}} \]

5. Taylor expanded in g around inf

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \frac{\sqrt[3]{2}}{\sqrt[3]{-2}}} \]
6. Step-by-step derivation

   1. associate-*r/ N/A

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

   2. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}}{\sqrt[3]{-2}} \]

   4. lower-cbrt.f64 N/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

   5. lower-/.f64 N/A

      \[\leadsto \frac{\sqrt[3]{\color{blue}{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

   6. lower-cbrt.f64 N/A

      \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \color{blue}{\sqrt[3]{2}}}{\sqrt[3]{-2}} \]

   7. lower-cbrt.f64 72.6

      \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\color{blue}{\sqrt[3]{-2}}} \]

7. Applied rewrites 72.6%

   \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]
8. Step-by-step derivation

9. Applied rewrites 72.7%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{-a}}} \]
10. Step-by-step derivation

11. Applied rewrites 72.7%

    \[\leadsto \sqrt[3]{\frac{-1}{\frac{a}{g}}} \]
12. Add Preprocessing

Alternative 6: 74.3% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \sqrt[3]{\frac{g}{-a}} \end{array} \]

FPCore

(FPCore (g h a) :precision binary64 (cbrt (/ g (- a))))

C

double code(double g, double h, double a) {
	return cbrt((g / -a));
}

Java

public static double code(double g, double h, double a) {
	return Math.cbrt((g / -a));
}

Julia

function code(g, h, a)
	return cbrt(Float64(g / Float64(-a)))
end

Wolfram

code[g_, h_, a_] := N[Power[N[(g / (-a)), $MachinePrecision], 1/3], $MachinePrecision]

Derivation

1. Initial program 46.4%

   \[\sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) - \sqrt{g \cdot g - h \cdot h}\right)} \]
2. Add Preprocessing

4. Applied rewrites 49.2%

   \[\leadsto \sqrt[3]{\frac{1}{2 \cdot a} \cdot \left(\left(-g\right) + \sqrt{g \cdot g - h \cdot h}\right)} + \color{blue}{\frac{\sqrt[3]{g + \sqrt{\left(g + h\right) \cdot \left(g - h\right)}}}{\sqrt[3]{a \cdot -2}}} \]

5. Taylor expanded in g around inf

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \frac{\sqrt[3]{2}}{\sqrt[3]{-2}}} \]
6. Step-by-step derivation

   1. associate-*r/ N/A

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

   2. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}}{\sqrt[3]{-2}} \]

   4. lower-cbrt.f64 N/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

   5. lower-/.f64 N/A

      \[\leadsto \frac{\sqrt[3]{\color{blue}{\frac{g}{a}}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}} \]

   6. lower-cbrt.f64 N/A

      \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \color{blue}{\sqrt[3]{2}}}{\sqrt[3]{-2}} \]

   7. lower-cbrt.f64 72.6

      \[\leadsto \frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\color{blue}{\sqrt[3]{-2}}} \]

7. Applied rewrites 72.6%

   \[\leadsto \color{blue}{\frac{\sqrt[3]{\frac{g}{a}} \cdot \sqrt[3]{2}}{\sqrt[3]{-2}}} \]
8. Step-by-step derivation

9. Applied rewrites 72.7%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{-a}}} \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2024234 
(FPCore (g h a)
  :name "2-ancestry mixing, positive discriminant"
  :precision binary64
  (+ (cbrt (* (/ 1.0 (* 2.0 a)) (+ (- g) (sqrt (- (* g g) (* h h)))))) (cbrt (* (/ 1.0 (* 2.0 a)) (- (- g) (sqrt (- (* g g) (* h h))))))))