2-ancestry mixing, zero discriminant

Percentage Accurate: 75.3% → 98.6%

Time: 5.7s

Alternatives: 6

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \sqrt[3]{\frac{g}{2 \cdot a}} \end{array} \]

FPCore

(FPCore (g a) :precision binary64 (cbrt (/ g (* 2.0 a))))

C

double code(double g, double a) {
	return cbrt((g / (2.0 * a)));
}

Java

public static double code(double g, double a) {
	return Math.cbrt((g / (2.0 * a)));
}

Julia

function code(g, a)
	return cbrt(Float64(g / Float64(2.0 * a)))
end

Wolfram

code[g_, a_] := N[Power[N[(g / N[(2.0 * a), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]

Initial Program: 75.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt[3]{\frac{g}{2 \cdot a}} \end{array} \]

FPCore

(FPCore (g a) :precision binary64 (cbrt (/ g (* 2.0 a))))

C

double code(double g, double a) {
	return cbrt((g / (2.0 * a)));
}

Java

public static double code(double g, double a) {
	return Math.cbrt((g / (2.0 * a)));
}

Julia

function code(g, a)
	return cbrt(Float64(g / Float64(2.0 * a)))
end

Wolfram

code[g_, a_] := N[Power[N[(g / N[(2.0 * a), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]

Alternative 1: 98.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \frac{\sqrt[3]{0.5}}{\sqrt[3]{a}} \cdot \sqrt[3]{g} \end{array} \]

FPCore

(FPCore (g a) :precision binary64 (* (/ (cbrt 0.5) (cbrt a)) (cbrt g)))

C

double code(double g, double a) {
	return (cbrt(0.5) / cbrt(a)) * cbrt(g);
}

Java

public static double code(double g, double a) {
	return (Math.cbrt(0.5) / Math.cbrt(a)) * Math.cbrt(g);
}

Julia

function code(g, a)
	return Float64(Float64(cbrt(0.5) / cbrt(a)) * cbrt(g))
end

Wolfram

code[g_, a_] := N[(N[(N[Power[0.5, 1/3], $MachinePrecision] / N[Power[a, 1/3], $MachinePrecision]), $MachinePrecision] * N[Power[g, 1/3], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.5%

   \[\sqrt[3]{\frac{g}{2 \cdot a}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. pow1/3 32.5%

      \[\leadsto \color{blue}{{\left(\frac{g}{2 \cdot a}\right)}^{0.3333333333333333}} \]

   2. clear-num 32.4%

      \[\leadsto {\color{blue}{\left(\frac{1}{\frac{2 \cdot a}{g}}\right)}}^{0.3333333333333333} \]

   3. associate-/r/ 32.5%

      \[\leadsto {\color{blue}{\left(\frac{1}{2 \cdot a} \cdot g\right)}}^{0.3333333333333333} \]

   4. unpow-prod-down 23.2%

      \[\leadsto \color{blue}{{\left(\frac{1}{2 \cdot a}\right)}^{0.3333333333333333} \cdot {g}^{0.3333333333333333}} \]

   5. pow1/3 47.5%

      \[\leadsto \color{blue}{\sqrt[3]{\frac{1}{2 \cdot a}}} \cdot {g}^{0.3333333333333333} \]

   6. associate-/r* 47.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{\frac{1}{2}}{a}}} \cdot {g}^{0.3333333333333333} \]

   7. metadata-eval 47.5%

      \[\leadsto \sqrt[3]{\frac{\color{blue}{0.5}}{a}} \cdot {g}^{0.3333333333333333} \]

   8. pow1/3 98.7%

      \[\leadsto \sqrt[3]{\frac{0.5}{a}} \cdot \color{blue}{\sqrt[3]{g}} \]

4. Applied egg-rr 98.7%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a}} \cdot \sqrt[3]{g}} \]
5. Step-by-step derivation

   1. cbrt-div 98.8%

      \[\leadsto \color{blue}{\frac{\sqrt[3]{0.5}}{\sqrt[3]{a}}} \cdot \sqrt[3]{g} \]

   2. div-inv 98.6%

      \[\leadsto \color{blue}{\left(\sqrt[3]{0.5} \cdot \frac{1}{\sqrt[3]{a}}\right)} \cdot \sqrt[3]{g} \]

6. Applied egg-rr 98.6%

   \[\leadsto \color{blue}{\left(\sqrt[3]{0.5} \cdot \frac{1}{\sqrt[3]{a}}\right)} \cdot \sqrt[3]{g} \]
7. Step-by-step derivation

   1. associate-*r/ 98.8%

      \[\leadsto \color{blue}{\frac{\sqrt[3]{0.5} \cdot 1}{\sqrt[3]{a}}} \cdot \sqrt[3]{g} \]

   2. *-rgt-identity 98.8%

      \[\leadsto \frac{\color{blue}{\sqrt[3]{0.5}}}{\sqrt[3]{a}} \cdot \sqrt[3]{g} \]

8. Simplified 98.8%

   \[\leadsto \color{blue}{\frac{\sqrt[3]{0.5}}{\sqrt[3]{a}}} \cdot \sqrt[3]{g} \]

9. Final simplification 98.8%

   \[\leadsto \frac{\sqrt[3]{0.5}}{\sqrt[3]{a}} \cdot \sqrt[3]{g} \]
10. Add Preprocessing

Alternative 2: 98.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\sqrt[3]{a}} \cdot \sqrt[3]{0.5 \cdot g} \end{array} \]

FPCore

(FPCore (g a) :precision binary64 (* (/ 1.0 (cbrt a)) (cbrt (* 0.5 g))))

C

double code(double g, double a) {
	return (1.0 / cbrt(a)) * cbrt((0.5 * g));
}

Java

public static double code(double g, double a) {
	return (1.0 / Math.cbrt(a)) * Math.cbrt((0.5 * g));
}

Julia

function code(g, a)
	return Float64(Float64(1.0 / cbrt(a)) * cbrt(Float64(0.5 * g)))
end

Wolfram

code[g_, a_] := N[(N[(1.0 / N[Power[a, 1/3], $MachinePrecision]), $MachinePrecision] * N[Power[N[(0.5 * g), $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.5%

   \[\sqrt[3]{\frac{g}{2 \cdot a}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. add-log-exp 8.7%

      \[\leadsto \color{blue}{\log \left(e^{\sqrt[3]{\frac{g}{2 \cdot a}}}\right)} \]

   2. *-un-lft-identity 8.7%

      \[\leadsto \log \color{blue}{\left(1 \cdot e^{\sqrt[3]{\frac{g}{2 \cdot a}}}\right)} \]

   3. log-prod 8.7%

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

   4. metadata-eval 8.7%

      \[\leadsto \color{blue}{0} + \log \left(e^{\sqrt[3]{\frac{g}{2 \cdot a}}}\right) \]

   5. add-log-exp 73.5%

      \[\leadsto 0 + \color{blue}{\sqrt[3]{\frac{g}{2 \cdot a}}} \]

   6. *-un-lft-identity 73.5%

      \[\leadsto 0 + \sqrt[3]{\frac{\color{blue}{1 \cdot g}}{2 \cdot a}} \]

   7. times-frac 73.5%

      \[\leadsto 0 + \sqrt[3]{\color{blue}{\frac{1}{2} \cdot \frac{g}{a}}} \]

   8. metadata-eval 73.5%

      \[\leadsto 0 + \sqrt[3]{\color{blue}{0.5} \cdot \frac{g}{a}} \]

4. Applied egg-rr 73.5%

   \[\leadsto \color{blue}{0 + \sqrt[3]{0.5 \cdot \frac{g}{a}}} \]
5. Step-by-step derivation

   1. +-lft-identity 73.5%

      \[\leadsto \color{blue}{\sqrt[3]{0.5 \cdot \frac{g}{a}}} \]

   2. metadata-eval 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{0.5}{1}} \cdot \frac{g}{a}} \]

   3. times-frac 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{0.5 \cdot g}{1 \cdot a}}} \]

   4. *-commutative 73.5%

      \[\leadsto \sqrt[3]{\frac{\color{blue}{g \cdot 0.5}}{1 \cdot a}} \]

   5. times-frac 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{g}{1} \cdot \frac{0.5}{a}}} \]

   6. rem-square-sqrt 35.4%

      \[\leadsto \sqrt[3]{\frac{\color{blue}{\sqrt{g} \cdot \sqrt{g}}}{1} \cdot \frac{0.5}{a}} \]

   7. associate-*r/ 35.4%

      \[\leadsto \sqrt[3]{\color{blue}{\left(\sqrt{g} \cdot \frac{\sqrt{g}}{1}\right)} \cdot \frac{0.5}{a}} \]

   8. /-rgt-identity 35.4%

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

   9. rem-square-sqrt 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{g} \cdot \frac{0.5}{a}} \]

6. Simplified 73.5%

   \[\leadsto \color{blue}{\sqrt[3]{g \cdot \frac{0.5}{a}}} \]
7. Step-by-step derivation

   1. associate-*r/ 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{g \cdot 0.5}{a}}} \]

   2. *-commutative 73.5%

      \[\leadsto \sqrt[3]{\frac{\color{blue}{0.5 \cdot g}}{a}} \]

   3. cbrt-div 98.7%

      \[\leadsto \color{blue}{\frac{\sqrt[3]{0.5 \cdot g}}{\sqrt[3]{a}}} \]

   4. *-un-lft-identity 98.7%

      \[\leadsto \frac{\color{blue}{1 \cdot \sqrt[3]{0.5 \cdot g}}}{\sqrt[3]{a}} \]

   5. associate-*l/ 98.7%

      \[\leadsto \color{blue}{\frac{1}{\sqrt[3]{a}} \cdot \sqrt[3]{0.5 \cdot g}} \]

   6. *-commutative 98.7%

      \[\leadsto \frac{1}{\sqrt[3]{a}} \cdot \sqrt[3]{\color{blue}{g \cdot 0.5}} \]

8. Applied egg-rr 98.7%

   \[\leadsto \color{blue}{\frac{1}{\sqrt[3]{a}} \cdot \sqrt[3]{g \cdot 0.5}} \]

9. Final simplification 98.7%

   \[\leadsto \frac{1}{\sqrt[3]{a}} \cdot \sqrt[3]{0.5 \cdot g} \]
10. Add Preprocessing

Alternative 3: 75.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \sqrt[3]{0.5} \cdot \sqrt[3]{\frac{g}{a}} \end{array} \]

FPCore

(FPCore (g a) :precision binary64 (* (cbrt 0.5) (cbrt (/ g a))))

C

double code(double g, double a) {
	return cbrt(0.5) * cbrt((g / a));
}

Java

public static double code(double g, double a) {
	return Math.cbrt(0.5) * Math.cbrt((g / a));
}

Julia

function code(g, a)
	return Float64(cbrt(0.5) * cbrt(Float64(g / a)))
end

Wolfram

code[g_, a_] := N[(N[Power[0.5, 1/3], $MachinePrecision] * N[Power[N[(g / a), $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.5%

   \[\sqrt[3]{\frac{g}{2 \cdot a}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. pow1/3 32.5%

      \[\leadsto \color{blue}{{\left(\frac{g}{2 \cdot a}\right)}^{0.3333333333333333}} \]

   2. *-un-lft-identity 32.5%

      \[\leadsto {\left(\frac{\color{blue}{1 \cdot g}}{2 \cdot a}\right)}^{0.3333333333333333} \]

   3. times-frac 32.5%

      \[\leadsto {\color{blue}{\left(\frac{1}{2} \cdot \frac{g}{a}\right)}}^{0.3333333333333333} \]

   4. metadata-eval 32.5%

      \[\leadsto {\left(\color{blue}{0.5} \cdot \frac{g}{a}\right)}^{0.3333333333333333} \]

   5. unpow-prod-down 32.5%

      \[\leadsto \color{blue}{{0.5}^{0.3333333333333333} \cdot {\left(\frac{g}{a}\right)}^{0.3333333333333333}} \]

4. Applied egg-rr 32.5%

   \[\leadsto \color{blue}{{0.5}^{0.3333333333333333} \cdot {\left(\frac{g}{a}\right)}^{0.3333333333333333}} \]
5. Step-by-step derivation

   1. unpow1/3 32.5%

      \[\leadsto \color{blue}{\sqrt[3]{0.5}} \cdot {\left(\frac{g}{a}\right)}^{0.3333333333333333} \]

   2. unpow1/3 73.6%

      \[\leadsto \sqrt[3]{0.5} \cdot \color{blue}{\sqrt[3]{\frac{g}{a}}} \]

6. Simplified 73.6%

   \[\leadsto \color{blue}{\sqrt[3]{0.5} \cdot \sqrt[3]{\frac{g}{a}}} \]

7. Final simplification 73.6%

   \[\leadsto \sqrt[3]{0.5} \cdot \sqrt[3]{\frac{g}{a}} \]
8. Add Preprocessing

Alternative 4: 98.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \sqrt[3]{g} \cdot \sqrt[3]{\frac{0.5}{a}} \end{array} \]

FPCore

(FPCore (g a) :precision binary64 (* (cbrt g) (cbrt (/ 0.5 a))))

C

double code(double g, double a) {
	return cbrt(g) * cbrt((0.5 / a));
}

Java

public static double code(double g, double a) {
	return Math.cbrt(g) * Math.cbrt((0.5 / a));
}

Julia

function code(g, a)
	return Float64(cbrt(g) * cbrt(Float64(0.5 / a)))
end

Wolfram

code[g_, a_] := N[(N[Power[g, 1/3], $MachinePrecision] * N[Power[N[(0.5 / a), $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.5%

   \[\sqrt[3]{\frac{g}{2 \cdot a}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. pow1/3 32.5%

      \[\leadsto \color{blue}{{\left(\frac{g}{2 \cdot a}\right)}^{0.3333333333333333}} \]

   2. clear-num 32.4%

      \[\leadsto {\color{blue}{\left(\frac{1}{\frac{2 \cdot a}{g}}\right)}}^{0.3333333333333333} \]

   3. associate-/r/ 32.5%

      \[\leadsto {\color{blue}{\left(\frac{1}{2 \cdot a} \cdot g\right)}}^{0.3333333333333333} \]

   4. unpow-prod-down 23.2%

      \[\leadsto \color{blue}{{\left(\frac{1}{2 \cdot a}\right)}^{0.3333333333333333} \cdot {g}^{0.3333333333333333}} \]

   5. pow1/3 47.5%

      \[\leadsto \color{blue}{\sqrt[3]{\frac{1}{2 \cdot a}}} \cdot {g}^{0.3333333333333333} \]

   6. associate-/r* 47.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{\frac{1}{2}}{a}}} \cdot {g}^{0.3333333333333333} \]

   7. metadata-eval 47.5%

      \[\leadsto \sqrt[3]{\frac{\color{blue}{0.5}}{a}} \cdot {g}^{0.3333333333333333} \]

   8. pow1/3 98.7%

      \[\leadsto \sqrt[3]{\frac{0.5}{a}} \cdot \color{blue}{\sqrt[3]{g}} \]

4. Applied egg-rr 98.7%

   \[\leadsto \color{blue}{\sqrt[3]{\frac{0.5}{a}} \cdot \sqrt[3]{g}} \]

5. Final simplification 98.7%

   \[\leadsto \sqrt[3]{g} \cdot \sqrt[3]{\frac{0.5}{a}} \]
6. Add Preprocessing

Alternative 5: 75.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt[3]{g \cdot \frac{0.5}{a}} \end{array} \]

FPCore

(FPCore (g a) :precision binary64 (cbrt (* g (/ 0.5 a))))

C

double code(double g, double a) {
	return cbrt((g * (0.5 / a)));
}

Java

public static double code(double g, double a) {
	return Math.cbrt((g * (0.5 / a)));
}

Julia

function code(g, a)
	return cbrt(Float64(g * Float64(0.5 / a)))
end

Wolfram

code[g_, a_] := N[Power[N[(g * N[(0.5 / a), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]

Derivation

1. Initial program 73.5%

   \[\sqrt[3]{\frac{g}{2 \cdot a}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. add-log-exp 8.7%

      \[\leadsto \color{blue}{\log \left(e^{\sqrt[3]{\frac{g}{2 \cdot a}}}\right)} \]

   2. *-un-lft-identity 8.7%

      \[\leadsto \log \color{blue}{\left(1 \cdot e^{\sqrt[3]{\frac{g}{2 \cdot a}}}\right)} \]

   3. log-prod 8.7%

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

   4. metadata-eval 8.7%

      \[\leadsto \color{blue}{0} + \log \left(e^{\sqrt[3]{\frac{g}{2 \cdot a}}}\right) \]

   5. add-log-exp 73.5%

      \[\leadsto 0 + \color{blue}{\sqrt[3]{\frac{g}{2 \cdot a}}} \]

   6. *-un-lft-identity 73.5%

      \[\leadsto 0 + \sqrt[3]{\frac{\color{blue}{1 \cdot g}}{2 \cdot a}} \]

   7. times-frac 73.5%

      \[\leadsto 0 + \sqrt[3]{\color{blue}{\frac{1}{2} \cdot \frac{g}{a}}} \]

   8. metadata-eval 73.5%

      \[\leadsto 0 + \sqrt[3]{\color{blue}{0.5} \cdot \frac{g}{a}} \]

4. Applied egg-rr 73.5%

   \[\leadsto \color{blue}{0 + \sqrt[3]{0.5 \cdot \frac{g}{a}}} \]
5. Step-by-step derivation

   1. +-lft-identity 73.5%

      \[\leadsto \color{blue}{\sqrt[3]{0.5 \cdot \frac{g}{a}}} \]

   2. metadata-eval 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{0.5}{1}} \cdot \frac{g}{a}} \]

   3. times-frac 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{0.5 \cdot g}{1 \cdot a}}} \]

   4. *-commutative 73.5%

      \[\leadsto \sqrt[3]{\frac{\color{blue}{g \cdot 0.5}}{1 \cdot a}} \]

   5. times-frac 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{g}{1} \cdot \frac{0.5}{a}}} \]

   6. rem-square-sqrt 35.4%

      \[\leadsto \sqrt[3]{\frac{\color{blue}{\sqrt{g} \cdot \sqrt{g}}}{1} \cdot \frac{0.5}{a}} \]

   7. associate-*r/ 35.4%

      \[\leadsto \sqrt[3]{\color{blue}{\left(\sqrt{g} \cdot \frac{\sqrt{g}}{1}\right)} \cdot \frac{0.5}{a}} \]

   8. /-rgt-identity 35.4%

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

   9. rem-square-sqrt 73.5%

      \[\leadsto \sqrt[3]{\color{blue}{g} \cdot \frac{0.5}{a}} \]

6. Simplified 73.5%

   \[\leadsto \color{blue}{\sqrt[3]{g \cdot \frac{0.5}{a}}} \]

7. Final simplification 73.5%

   \[\leadsto \sqrt[3]{g \cdot \frac{0.5}{a}} \]
8. Add Preprocessing

Alternative 6: 75.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt[3]{\frac{g}{a \cdot 2}} \end{array} \]

FPCore

(FPCore (g a) :precision binary64 (cbrt (/ g (* a 2.0))))

C

double code(double g, double a) {
	return cbrt((g / (a * 2.0)));
}

Java

public static double code(double g, double a) {
	return Math.cbrt((g / (a * 2.0)));
}

Julia

function code(g, a)
	return cbrt(Float64(g / Float64(a * 2.0)))
end

Wolfram

code[g_, a_] := N[Power[N[(g / N[(a * 2.0), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]

Derivation

1. Initial program 73.5%

   \[\sqrt[3]{\frac{g}{2 \cdot a}} \]
2. Add Preprocessing

3. Final simplification 73.5%

   \[\leadsto \sqrt[3]{\frac{g}{a \cdot 2}} \]
4. Add Preprocessing

Reproduce

herbie shell --seed 2024010 
(FPCore (g a)
  :name "2-ancestry mixing, zero discriminant"
  :precision binary64
  (cbrt (/ g (* 2.0 a))))