2-ancestry mixing, zero discriminant

Percentage Accurate: 76.6% → 98.7%

Time: 4.2s

Alternatives: 9

Speedup: 1.0×

Specification

Math

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

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: 76.6% accurate, 1.0× speedup

Math

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

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: 98.7% accurate, 0.6× speedup

Math

\[\frac{\sqrt[3]{g + g}}{\sqrt[3]{4 \cdot a}} \]

FPCore

(FPCore (g a) :precision binary64 (/ (cbrt (+ g g)) (cbrt (* 4.0 a))))

C

double code(double g, double a) {
	return cbrt((g + g)) / cbrt((4.0 * a));
}

Java

public static double code(double g, double a) {
	return Math.cbrt((g + g)) / Math.cbrt((4.0 * a));
}

Julia

function code(g, a)
	return Float64(cbrt(Float64(g + g)) / cbrt(Float64(4.0 * a)))
end

Wolfram

code[g_, a_] := N[(N[Power[N[(g + g), $MachinePrecision], 1/3], $MachinePrecision] / N[Power[N[(4.0 * a), $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 76.6%

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

   1. lift-cbrt.f64 N/A

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

   2. *-lft-identity N/A

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

   3. lift-/.f64 N/A

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

   4. mult-flip N/A

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

   5. *-commutative N/A

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

   6. metadata-eval N/A

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

   7. *-commutative N/A

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

   8. mult-flip N/A

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

   9. frac-times N/A

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

   10. cbrt-div N/A

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

   11. lower-/.f64 N/A

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

   12. lower-cbrt.f64 N/A

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

   13. lower-*.f64 N/A

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

   14. lower-cbrt.f64 N/A

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

   15. lower-*.f64 98.6

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

   16. lift-*.f64 N/A

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

   17. count-2-rev N/A

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

   18. lower-+.f64 98.6

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

3. Applied rewrites 98.6%

   \[\leadsto \color{blue}{\frac{\sqrt[3]{2 \cdot g}}{\sqrt[3]{2 \cdot \left(a + a\right)}}} \]
4. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. count-2-rev N/A

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

   3. lower-+.f64 98.6

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

   4. lift-*.f64 N/A

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

   5. lift-+.f64 N/A

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

   6. distribute-lft-in N/A

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

   7. distribute-rgt-out N/A

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

   8. *-commutative N/A

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

   9. lower-*.f64 N/A

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

   10. metadata-eval 98.6

       \[\leadsto \frac{\sqrt[3]{g + g}}{\sqrt[3]{\color{blue}{4} \cdot a}} \]

5. Applied rewrites 98.6%

   \[\leadsto \color{blue}{\frac{\sqrt[3]{g + g}}{\sqrt[3]{4 \cdot a}}} \]
6. Add Preprocessing

Alternative 2: 98.7% accurate, 0.6× speedup

Math

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

FPCore

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

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 76.6%

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

   1. lift-cbrt.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. lift-*.f64 N/A

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

   4. associate-/r* N/A

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

   5. cbrt-div N/A

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

   6. lower-/.f64 N/A

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

   7. lower-cbrt.f64 N/A

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

   8. mult-flip N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. metadata-eval N/A

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

   12. lower-cbrt.f64 98.7

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

3. Applied rewrites 98.7%

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

Alternative 3: 98.6% accurate, 0.6× speedup

Math

\[\frac{\sqrt[3]{g}}{\sqrt[3]{a + a}} \]

FPCore

(FPCore (g a) :precision binary64 (/ (cbrt g) (cbrt (+ a a))))

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 76.6%

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

   1. lift-cbrt.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. cbrt-div N/A

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

   4. lower-/.f64 N/A

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

   5. lower-cbrt.f64 N/A

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

   6. lower-cbrt.f64 98.7

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

   7. lift-*.f64 N/A

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

   8. count-2-rev N/A

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

   9. lower-+.f64 98.7

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

3. Applied rewrites 98.7%

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

Alternative 4: 96.4% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_0 := e^{\log \left(\left|g\right|\right) \cdot 0.3333333333333333 - \log \left(\left|a\right| + \left|a\right|\right) \cdot 0.3333333333333333}\\ t_1 := \sqrt[3]{\frac{\left|g\right|}{2 \cdot \left|a\right|}}\\ \mathsf{copysign}\left(1, g\right) \cdot \left(\mathsf{copysign}\left(1, a\right) \cdot \begin{array}{l} \mathbf{if}\;t\_1 \leq 0:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+102}:\\ \;\;\;\;\sqrt[3]{\frac{\left|g\right|}{\left|a\right|} \cdot 0.5}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array}\right) \end{array} \]

FPCore

(FPCore (g a)
 :precision binary64
 (let* ((t_0
         (exp
          (-
           (* (log (fabs g)) 0.3333333333333333)
           (* (log (+ (fabs a) (fabs a))) 0.3333333333333333))))
        (t_1 (cbrt (/ (fabs g) (* 2.0 (fabs a))))))
   (*
    (copysign 1.0 g)
    (*
     (copysign 1.0 a)
     (if (<= t_1 0.0)
       t_0
       (if (<= t_1 5e+102) (cbrt (* (/ (fabs g) (fabs a)) 0.5)) t_0))))))

C

double code(double g, double a) {
	double t_0 = exp(((log(fabs(g)) * 0.3333333333333333) - (log((fabs(a) + fabs(a))) * 0.3333333333333333)));
	double t_1 = cbrt((fabs(g) / (2.0 * fabs(a))));
	double tmp;
	if (t_1 <= 0.0) {
		tmp = t_0;
	} else if (t_1 <= 5e+102) {
		tmp = cbrt(((fabs(g) / fabs(a)) * 0.5));
	} else {
		tmp = t_0;
	}
	return copysign(1.0, g) * (copysign(1.0, a) * tmp);
}

Java

public static double code(double g, double a) {
	double t_0 = Math.exp(((Math.log(Math.abs(g)) * 0.3333333333333333) - (Math.log((Math.abs(a) + Math.abs(a))) * 0.3333333333333333)));
	double t_1 = Math.cbrt((Math.abs(g) / (2.0 * Math.abs(a))));
	double tmp;
	if (t_1 <= 0.0) {
		tmp = t_0;
	} else if (t_1 <= 5e+102) {
		tmp = Math.cbrt(((Math.abs(g) / Math.abs(a)) * 0.5));
	} else {
		tmp = t_0;
	}
	return Math.copySign(1.0, g) * (Math.copySign(1.0, a) * tmp);
}

Julia

function code(g, a)
	t_0 = exp(Float64(Float64(log(abs(g)) * 0.3333333333333333) - Float64(log(Float64(abs(a) + abs(a))) * 0.3333333333333333)))
	t_1 = cbrt(Float64(abs(g) / Float64(2.0 * abs(a))))
	tmp = 0.0
	if (t_1 <= 0.0)
		tmp = t_0;
	elseif (t_1 <= 5e+102)
		tmp = cbrt(Float64(Float64(abs(g) / abs(a)) * 0.5));
	else
		tmp = t_0;
	end
	return Float64(copysign(1.0, g) * Float64(copysign(1.0, a) * tmp))
end

Wolfram

code[g_, a_] := Block[{t$95$0 = N[Exp[N[(N[(N[Log[N[Abs[g], $MachinePrecision]], $MachinePrecision] * 0.3333333333333333), $MachinePrecision] - N[(N[Log[N[(N[Abs[a], $MachinePrecision] + N[Abs[a], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * 0.3333333333333333), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Power[N[(N[Abs[g], $MachinePrecision] / N[(2.0 * N[Abs[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[g]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$1, 0.0], t$95$0, If[LessEqual[t$95$1, 5e+102], N[Power[N[(N[(N[Abs[g], $MachinePrecision] / N[Abs[a], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], 1/3], $MachinePrecision], t$95$0]]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a))) < 0.0 or 5e102 < (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a)))

   1. Initial program 76.6%

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

      1. lift-cbrt.f64 N/A

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

      2. pow1/3 N/A

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

      3. pow-to-exp N/A

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

      4. lower-unsound-exp.f64 N/A

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

      5. lower-unsound-*.f64 N/A

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

      6. lower-unsound-log.f64 36.3

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

      7. lift-*.f64 N/A

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

      8. count-2-rev N/A

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

      9. lower-+.f64 36.3

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

   3. Applied rewrites 36.3%

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

      1. lift-log.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. log-div N/A

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

      4. lower-unsound--.f64 N/A

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

      5. lower-unsound-log.f64 N/A

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

      6. lower-unsound-log.f64 23.1

         \[\leadsto e^{\left(\log g - \color{blue}{\log \left(a + a\right)}\right) \cdot 0.3333333333333333} \]

   5. Applied rewrites 23.1%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift--.f64 N/A

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

      4. sub-flip N/A

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

      5. distribute-rgt-in N/A

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

      6. fp-cancel-sub-sign N/A

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

      7. lower--.f64 N/A

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

      8. lower-*.f64 N/A

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

      9. lower-*.f64 23.0

         \[\leadsto e^{\log g \cdot 0.3333333333333333 - \color{blue}{\log \left(a + a\right) \cdot 0.3333333333333333}} \]

   7. Applied rewrites 23.0%

      \[\leadsto e^{\color{blue}{\log g \cdot 0.3333333333333333 - \log \left(a + a\right) \cdot 0.3333333333333333}} \]

   if 0.0 < (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a))) < 5e102

   1. Initial program 76.6%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/r* N/A

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

      4. mult-flip N/A

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

      5. associate-*l/ N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. metadata-eval 76.6

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

   3. Applied rewrites 76.6%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{g}{a} \cdot 0.5}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 96.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_0 := \sqrt[3]{\frac{\left|g\right|}{2 \cdot \left|a\right|}}\\ \mathsf{copysign}\left(1, g\right) \cdot \left(\mathsf{copysign}\left(1, a\right) \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq 0:\\ \;\;\;\;e^{\left(\log \left(\left|g\right|\right) - \log \left(\left|a\right| + \left|a\right|\right)\right) \cdot 0.3333333333333333}\\ \mathbf{elif}\;t\_0 \leq 5 \cdot 10^{+102}:\\ \;\;\;\;\sqrt[3]{\frac{\left|g\right|}{\left|a\right|} \cdot 0.5}\\ \mathbf{else}:\\ \;\;\;\;e^{\left(\log \left(0.5 \cdot \left|g\right|\right) - \log \left(\left|a\right|\right)\right) \cdot 0.3333333333333333}\\ \end{array}\right) \end{array} \]

FPCore

(FPCore (g a)
 :precision binary64
 (let* ((t_0 (cbrt (/ (fabs g) (* 2.0 (fabs a))))))
   (*
    (copysign 1.0 g)
    (*
     (copysign 1.0 a)
     (if (<= t_0 0.0)
       (exp
        (* (- (log (fabs g)) (log (+ (fabs a) (fabs a)))) 0.3333333333333333))
       (if (<= t_0 5e+102)
         (cbrt (* (/ (fabs g) (fabs a)) 0.5))
         (exp
          (*
           (- (log (* 0.5 (fabs g))) (log (fabs a)))
           0.3333333333333333))))))))

C

double code(double g, double a) {
	double t_0 = cbrt((fabs(g) / (2.0 * fabs(a))));
	double tmp;
	if (t_0 <= 0.0) {
		tmp = exp(((log(fabs(g)) - log((fabs(a) + fabs(a)))) * 0.3333333333333333));
	} else if (t_0 <= 5e+102) {
		tmp = cbrt(((fabs(g) / fabs(a)) * 0.5));
	} else {
		tmp = exp(((log((0.5 * fabs(g))) - log(fabs(a))) * 0.3333333333333333));
	}
	return copysign(1.0, g) * (copysign(1.0, a) * tmp);
}

Java

public static double code(double g, double a) {
	double t_0 = Math.cbrt((Math.abs(g) / (2.0 * Math.abs(a))));
	double tmp;
	if (t_0 <= 0.0) {
		tmp = Math.exp(((Math.log(Math.abs(g)) - Math.log((Math.abs(a) + Math.abs(a)))) * 0.3333333333333333));
	} else if (t_0 <= 5e+102) {
		tmp = Math.cbrt(((Math.abs(g) / Math.abs(a)) * 0.5));
	} else {
		tmp = Math.exp(((Math.log((0.5 * Math.abs(g))) - Math.log(Math.abs(a))) * 0.3333333333333333));
	}
	return Math.copySign(1.0, g) * (Math.copySign(1.0, a) * tmp);
}

Julia

function code(g, a)
	t_0 = cbrt(Float64(abs(g) / Float64(2.0 * abs(a))))
	tmp = 0.0
	if (t_0 <= 0.0)
		tmp = exp(Float64(Float64(log(abs(g)) - log(Float64(abs(a) + abs(a)))) * 0.3333333333333333));
	elseif (t_0 <= 5e+102)
		tmp = cbrt(Float64(Float64(abs(g) / abs(a)) * 0.5));
	else
		tmp = exp(Float64(Float64(log(Float64(0.5 * abs(g))) - log(abs(a))) * 0.3333333333333333));
	end
	return Float64(copysign(1.0, g) * Float64(copysign(1.0, a) * tmp))
end

Wolfram

code[g_, a_] := Block[{t$95$0 = N[Power[N[(N[Abs[g], $MachinePrecision] / N[(2.0 * N[Abs[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[g]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$0, 0.0], N[Exp[N[(N[(N[Log[N[Abs[g], $MachinePrecision]], $MachinePrecision] - N[Log[N[(N[Abs[a], $MachinePrecision] + N[Abs[a], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * 0.3333333333333333), $MachinePrecision]], $MachinePrecision], If[LessEqual[t$95$0, 5e+102], N[Power[N[(N[(N[Abs[g], $MachinePrecision] / N[Abs[a], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], 1/3], $MachinePrecision], N[Exp[N[(N[(N[Log[N[(0.5 * N[Abs[g], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - N[Log[N[Abs[a], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * 0.3333333333333333), $MachinePrecision]], $MachinePrecision]]]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 3 regimes

2. if (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a))) < 0.0

   1. Initial program 76.6%

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

      1. lift-cbrt.f64 N/A

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

      2. pow1/3 N/A

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

      3. pow-to-exp N/A

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

      4. lower-unsound-exp.f64 N/A

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

      5. lower-unsound-*.f64 N/A

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

      6. lower-unsound-log.f64 36.3

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

      7. lift-*.f64 N/A

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

      8. count-2-rev N/A

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

      9. lower-+.f64 36.3

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

   3. Applied rewrites 36.3%

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

      1. lift-log.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. log-div N/A

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

      4. lower-unsound--.f64 N/A

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

      5. lower-unsound-log.f64 N/A

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

      6. lower-unsound-log.f64 23.1

         \[\leadsto e^{\left(\log g - \color{blue}{\log \left(a + a\right)}\right) \cdot 0.3333333333333333} \]

   5. Applied rewrites 23.1%

      \[\leadsto e^{\color{blue}{\left(\log g - \log \left(a + a\right)\right)} \cdot 0.3333333333333333} \]

   if 0.0 < (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a))) < 5e102

   1. Initial program 76.6%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/r* N/A

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

      4. mult-flip N/A

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

      5. associate-*l/ N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. metadata-eval 76.6

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

   3. Applied rewrites 76.6%

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

   if 5e102 < (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a)))

   1. Initial program 76.6%

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

      1. lift-cbrt.f64 N/A

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

      2. pow1/3 N/A

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

      3. pow-to-exp N/A

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

      4. lower-unsound-exp.f64 N/A

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

      5. lower-unsound-*.f64 N/A

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

      6. lower-unsound-log.f64 36.3

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

      7. lift-*.f64 N/A

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

      8. count-2-rev N/A

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

      9. lower-+.f64 36.3

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

   3. Applied rewrites 36.3%

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

      1. lift-log.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. count-2 N/A

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

      5. associate-/r* N/A

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

      6. log-div N/A

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

      7. mult-flip-rev N/A

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

      8. metadata-eval N/A

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

      9. *-commutative N/A

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

      10. lift-*.f64 N/A

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

      11. lower-unsound--.f64 N/A

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

      12. lower-unsound-log.f64 N/A

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

      13. lower-unsound-log.f64 23.1

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

   5. Applied rewrites 23.1%

      \[\leadsto e^{\color{blue}{\left(\log \left(0.5 \cdot g\right) - \log a\right)} \cdot 0.3333333333333333} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 96.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_0 := e^{\left(\log \left(\left|g\right|\right) - \log \left(\left|a\right| + \left|a\right|\right)\right) \cdot 0.3333333333333333}\\ t_1 := \sqrt[3]{\frac{\left|g\right|}{2 \cdot \left|a\right|}}\\ \mathsf{copysign}\left(1, g\right) \cdot \left(\mathsf{copysign}\left(1, a\right) \cdot \begin{array}{l} \mathbf{if}\;t\_1 \leq 0:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+102}:\\ \;\;\;\;\sqrt[3]{\frac{\left|g\right|}{\left|a\right|} \cdot 0.5}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array}\right) \end{array} \]

FPCore

(FPCore (g a)
 :precision binary64
 (let* ((t_0
         (exp
          (*
           (- (log (fabs g)) (log (+ (fabs a) (fabs a))))
           0.3333333333333333)))
        (t_1 (cbrt (/ (fabs g) (* 2.0 (fabs a))))))
   (*
    (copysign 1.0 g)
    (*
     (copysign 1.0 a)
     (if (<= t_1 0.0)
       t_0
       (if (<= t_1 5e+102) (cbrt (* (/ (fabs g) (fabs a)) 0.5)) t_0))))))

C

double code(double g, double a) {
	double t_0 = exp(((log(fabs(g)) - log((fabs(a) + fabs(a)))) * 0.3333333333333333));
	double t_1 = cbrt((fabs(g) / (2.0 * fabs(a))));
	double tmp;
	if (t_1 <= 0.0) {
		tmp = t_0;
	} else if (t_1 <= 5e+102) {
		tmp = cbrt(((fabs(g) / fabs(a)) * 0.5));
	} else {
		tmp = t_0;
	}
	return copysign(1.0, g) * (copysign(1.0, a) * tmp);
}

Java

public static double code(double g, double a) {
	double t_0 = Math.exp(((Math.log(Math.abs(g)) - Math.log((Math.abs(a) + Math.abs(a)))) * 0.3333333333333333));
	double t_1 = Math.cbrt((Math.abs(g) / (2.0 * Math.abs(a))));
	double tmp;
	if (t_1 <= 0.0) {
		tmp = t_0;
	} else if (t_1 <= 5e+102) {
		tmp = Math.cbrt(((Math.abs(g) / Math.abs(a)) * 0.5));
	} else {
		tmp = t_0;
	}
	return Math.copySign(1.0, g) * (Math.copySign(1.0, a) * tmp);
}

Julia

function code(g, a)
	t_0 = exp(Float64(Float64(log(abs(g)) - log(Float64(abs(a) + abs(a)))) * 0.3333333333333333))
	t_1 = cbrt(Float64(abs(g) / Float64(2.0 * abs(a))))
	tmp = 0.0
	if (t_1 <= 0.0)
		tmp = t_0;
	elseif (t_1 <= 5e+102)
		tmp = cbrt(Float64(Float64(abs(g) / abs(a)) * 0.5));
	else
		tmp = t_0;
	end
	return Float64(copysign(1.0, g) * Float64(copysign(1.0, a) * tmp))
end

Wolfram

code[g_, a_] := Block[{t$95$0 = N[Exp[N[(N[(N[Log[N[Abs[g], $MachinePrecision]], $MachinePrecision] - N[Log[N[(N[Abs[a], $MachinePrecision] + N[Abs[a], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * 0.3333333333333333), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Power[N[(N[Abs[g], $MachinePrecision] / N[(2.0 * N[Abs[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[g]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$1, 0.0], t$95$0, If[LessEqual[t$95$1, 5e+102], N[Power[N[(N[(N[Abs[g], $MachinePrecision] / N[Abs[a], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], 1/3], $MachinePrecision], t$95$0]]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a))) < 0.0 or 5e102 < (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a)))

   1. Initial program 76.6%

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

      1. lift-cbrt.f64 N/A

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

      2. pow1/3 N/A

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

      3. pow-to-exp N/A

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

      4. lower-unsound-exp.f64 N/A

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

      5. lower-unsound-*.f64 N/A

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

      6. lower-unsound-log.f64 36.3

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

      7. lift-*.f64 N/A

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

      8. count-2-rev N/A

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

      9. lower-+.f64 36.3

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

   3. Applied rewrites 36.3%

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

      1. lift-log.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. log-div N/A

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

      4. lower-unsound--.f64 N/A

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

      5. lower-unsound-log.f64 N/A

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

      6. lower-unsound-log.f64 23.1

         \[\leadsto e^{\left(\log g - \color{blue}{\log \left(a + a\right)}\right) \cdot 0.3333333333333333} \]

   5. Applied rewrites 23.1%

      \[\leadsto e^{\color{blue}{\left(\log g - \log \left(a + a\right)\right)} \cdot 0.3333333333333333} \]

   if 0.0 < (cbrt.f64 (/.f64 g (*.f64 #s(literal 2 binary64) a))) < 5e102

   1. Initial program 76.6%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/r* N/A

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

      4. mult-flip N/A

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

      5. associate-*l/ N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

      8. metadata-eval 76.6

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

   3. Applied rewrites 76.6%

      \[\leadsto \sqrt[3]{\color{blue}{\frac{g}{a} \cdot 0.5}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 76.7% accurate, 0.9× speedup

Math

\[\frac{1}{\sqrt[3]{\frac{a + a}{g}}} \]

FPCore

(FPCore (g a) :precision binary64 (/ 1.0 (cbrt (/ (+ a a) g))))

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 76.6%

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

   1. lift-cbrt.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. lift-*.f64 N/A

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

   4. associate-/r* N/A

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

   5. cbrt-div N/A

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

   6. div-flip N/A

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

   7. lower-unsound-/.f64 N/A

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

   8. lower-unsound-/.f64 N/A

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

   9. lower-cbrt.f64 N/A

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

   10. lower-cbrt.f64 N/A

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

   11. mult-flip N/A

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

   12. *-commutative N/A

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

   13. lower-*.f64 N/A

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

   14. metadata-eval 98.7

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

3. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\frac{1}{\frac{\sqrt[3]{a}}{\sqrt[3]{0.5 \cdot g}}}} \]
4. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. lift-cbrt.f64 N/A

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

   3. lift-cbrt.f64 N/A

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

   4. cbrt-undiv N/A

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

   5. lower-cbrt.f64 N/A

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

   6. lift-*.f64 N/A

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

   7. associate-/r* N/A

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

   8. mult-flip N/A

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

   9. metadata-eval N/A

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

   10. *-commutative N/A

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

   11. count-2 N/A

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

   12. lift-+.f64 N/A

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

   13. lower-/.f64 76.7

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

5. Applied rewrites 76.7%

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

Alternative 8: 76.6% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 76.6%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/r* N/A

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

   4. mult-flip N/A

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

   5. associate-*l/ N/A

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

   6. lower-*.f64 N/A

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

   7. lower-/.f64 N/A

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

   8. metadata-eval 76.6

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

3. Applied rewrites 76.6%

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

Alternative 9: 76.6% accurate, 1.0× speedup

Math

\[\sqrt[3]{\frac{g}{a + a}} \]

FPCore

(FPCore (g a) :precision binary64 (cbrt (/ g (+ a a))))

C

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

Java

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

Julia

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

Wolfram

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

Derivation

1. Initial program 76.6%

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

   1. lift-*.f64 N/A

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

   2. count-2-rev N/A

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

   3. lower-+.f64 76.6

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

3. Applied rewrites 76.6%

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

Reproduce

herbie shell --seed 2025173 
(FPCore (g a)
  :name "2-ancestry mixing, zero discriminant"
  :precision binary64
  (cbrt (/ g (* 2.0 a))))