2-ancestry mixing, positive discriminant

Percentage Accurate: 43.5% → 95.8%

Time: 14.6s

Alternatives: 7

Speedup: 4.2×

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: 43.5% 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: 95.8% accurate, 2.1× speedup

Math

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

FPCore

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

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 44.6%

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

3. Simplified 44.6%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a} \cdot \left(\sqrt{g \cdot g - h \cdot h} - g\right)} + \sqrt[3]{\left(g + \sqrt{g \cdot g - h \cdot h}\right) \cdot \frac{-0.5}{a}}} \]
4. Add Preprocessing

5. Taylor expanded in g around inf 71.1%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \left(\sqrt[3]{-0.5} \cdot \sqrt[3]{2}\right)} \]

7. Applied egg-rr 96.2%

   \[\leadsto \color{blue}{\frac{-1}{\frac{\sqrt[3]{a}}{\sqrt[3]{g}}}} \]
8. Step-by-step derivation

   1. associate-/r/ 96.2%

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

9. Simplified 96.2%

   \[\leadsto \color{blue}{\frac{-1}{\sqrt[3]{a}} \cdot \sqrt[3]{g}} \]
10. Add Preprocessing

Alternative 2: 95.9% accurate, 2.1× speedup

Math

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

FPCore

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

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 44.6%

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

3. Simplified 44.6%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a} \cdot \left(\sqrt{g \cdot g - h \cdot h} - g\right)} + \sqrt[3]{\left(g + \sqrt{g \cdot g - h \cdot h}\right) \cdot \frac{-0.5}{a}}} \]
4. Add Preprocessing

5. Taylor expanded in g around inf 71.1%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \left(\sqrt[3]{-0.5} \cdot \sqrt[3]{2}\right)} \]

7. Applied egg-rr 96.2%

   \[\leadsto \color{blue}{\frac{-1}{\frac{\sqrt[3]{a}}{\sqrt[3]{g}}}} \]
8. Step-by-step derivation

   1. associate-/r/ 96.2%

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

9. Simplified 96.2%

   \[\leadsto \color{blue}{\frac{-1}{\sqrt[3]{a}} \cdot \sqrt[3]{g}} \]
10. Step-by-step derivation

    1. div-inv 96.2%

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

11. Applied egg-rr 96.2%

    \[\leadsto \color{blue}{\left(-1 \cdot \frac{1}{\sqrt[3]{a}}\right)} \cdot \sqrt[3]{g} \]
12. Step-by-step derivation

    1. associate-*r/ 96.2%

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

    2. metadata-eval 96.2%

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

    3. rem-cbrt-cube 96.0%

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

    4. cube-div 96.0%

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

    5. metadata-eval 96.0%

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

    6. rem-cube-cbrt 96.2%

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

13. Simplified 96.2%

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

14. Final simplification 96.2%

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

Alternative 3: 95.9% accurate, 2.1× 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(g) / Float64(-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 44.6%

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

3. Simplified 44.6%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a} \cdot \left(\sqrt{g \cdot g - h \cdot h} - g\right)} + \sqrt[3]{\left(g + \sqrt{g \cdot g - h \cdot h}\right) \cdot \frac{-0.5}{a}}} \]
4. Add Preprocessing

5. Taylor expanded in g around inf 71.1%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \left(\sqrt[3]{-0.5} \cdot \sqrt[3]{2}\right)} \]

7. Applied egg-rr 96.1%

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

8. Final simplification 96.1%

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

Alternative 4: 74.2% accurate, 4.1× 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(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 44.6%

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

3. Simplified 44.6%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a} \cdot \left(\sqrt{g \cdot g - h \cdot h} - g\right)} + \sqrt[3]{\left(g + \sqrt{g \cdot g - h \cdot h}\right) \cdot \frac{-0.5}{a}}} \]
4. Add Preprocessing

5. Taylor expanded in g around inf 71.1%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \left(\sqrt[3]{-0.5} \cdot \sqrt[3]{2}\right)} \]

7. Applied egg-rr 96.2%

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

8. Taylor expanded in a around 0 74.0%

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

Alternative 5: 73.6% accurate, 4.2× 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 Float64(-cbrt(Float64(g / a)))
end

Wolfram

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

Derivation

1. Initial program 44.6%

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

3. Simplified 44.6%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a} \cdot \left(\sqrt{g \cdot g - h \cdot h} - g\right)} + \sqrt[3]{\left(g + \sqrt{g \cdot g - h \cdot h}\right) \cdot \frac{-0.5}{a}}} \]
4. Add Preprocessing

5. Taylor expanded in g around inf 71.1%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \left(\sqrt[3]{-0.5} \cdot \sqrt[3]{2}\right)} \]

7. Applied egg-rr 96.2%

   \[\leadsto \color{blue}{\frac{-1}{\frac{\sqrt[3]{a}}{\sqrt[3]{g}}}} \]
8. Step-by-step derivation

   1. associate-/r/ 96.2%

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

9. Simplified 96.2%

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

10. Taylor expanded in a around 0 71.8%

    \[\leadsto \color{blue}{-1 \cdot \sqrt[3]{\frac{g}{a}}} \]
11. Step-by-step derivation

    1. mul-1-neg 71.8%

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

12. Simplified 71.8%

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

Alternative 6: 5.8% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ \sqrt[3]{a \cdot \left(-g\right)} \end{array} \]

FPCore

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

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 44.6%

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

3. Simplified 44.6%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a} \cdot \left(\sqrt{g \cdot g - h \cdot h} - g\right)} + \sqrt[3]{\left(g + \sqrt{g \cdot g - h \cdot h}\right) \cdot \frac{-0.5}{a}}} \]
4. Add Preprocessing

5. Taylor expanded in g around inf 71.1%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \left(\sqrt[3]{-0.5} \cdot \sqrt[3]{2}\right)} \]

7. Applied egg-rr 5.8%

   \[\leadsto \color{blue}{\sqrt[3]{\left(g \cdot a\right) \cdot -1}} \]
8. Step-by-step derivation

   1. *-commutative 5.8%

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

   2. neg-mul-1 5.8%

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

   3. distribute-rgt-neg-in 5.8%

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

9. Simplified 5.8%

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

10. Final simplification 5.8%

    \[\leadsto \sqrt[3]{a \cdot \left(-g\right)} \]
11. Add Preprocessing

Alternative 7: 1.3% accurate, 4.2× speedup

Math

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

FPCore

(FPCore (g h a) :precision binary64 (cbrt (* a g)))

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 44.6%

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

3. Simplified 44.6%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a} \cdot \left(\sqrt{g \cdot g - h \cdot h} - g\right)} + \sqrt[3]{\left(g + \sqrt{g \cdot g - h \cdot h}\right) \cdot \frac{-0.5}{a}}} \]
4. Add Preprocessing

5. Taylor expanded in g around inf 71.1%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{g}{a}} \cdot \left(\sqrt[3]{-0.5} \cdot \sqrt[3]{2}\right)} \]

7. Applied egg-rr 1.0%

   \[\leadsto \color{blue}{{\left(g \cdot a\right)}^{0.3333333333333333}} \]
8. Step-by-step derivation

   1. unpow1/3 1.3%

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

9. Simplified 1.3%

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

10. Final simplification 1.3%

    \[\leadsto \sqrt[3]{a \cdot g} \]
11. Add Preprocessing

Reproduce

herbie shell --seed 2024181 
(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))))))))