math.cube on complex, imaginary part

Percentage Accurate: 82.3% → 99.4%

Time: 16.9s

Precision: binary64

Cost: 7305

• Unsound rule application detected in e-graph. Results may not be sound.

• 43.2% of points produce a very large (infinite) output. You may want to add a precondition.

Math

\[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]

↓

\[\begin{array}{l} \mathbf{if}\;x.im \leq -2.3 \cdot 10^{+113} \lor \neg \left(x.im \leq 6 \cdot 10^{+72}\right):\\ \;\;\;\;x.im \cdot \left(\left(x.re - x.im\right) \cdot \left(x.im + x.re\right)\right) + x.re \cdot 8\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(3 \cdot \left(x.im \cdot x.re\right)\right) - {x.im}^{3}\\ \end{array} \]

FPCore

(FPCore (x.re x.im)
 :precision binary64
 (+
  (* (- (* x.re x.re) (* x.im x.im)) x.im)
  (* (+ (* x.re x.im) (* x.im x.re)) x.re)))

↓

(FPCore (x.re x.im)
 :precision binary64
 (if (or (<= x.im -2.3e+113) (not (<= x.im 6e+72)))
   (+ (* x.im (* (- x.re x.im) (+ x.im x.re))) (* x.re 8.0))
   (- (* x.re (* 3.0 (* x.im x.re))) (pow x.im 3.0))))

C

double code(double x_46_re, double x_46_im) {
	return (((x_46_re * x_46_re) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re * x_46_im) + (x_46_im * x_46_re)) * x_46_re);
}

↓

double code(double x_46_re, double x_46_im) {
	double tmp;
	if ((x_46_im <= -2.3e+113) || !(x_46_im <= 6e+72)) {
		tmp = (x_46_im * ((x_46_re - x_46_im) * (x_46_im + x_46_re))) + (x_46_re * 8.0);
	} else {
		tmp = (x_46_re * (3.0 * (x_46_im * x_46_re))) - pow(x_46_im, 3.0);
	}
	return tmp;
}

Fortran

real(8) function code(x_46re, x_46im)
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    code = (((x_46re * x_46re) - (x_46im * x_46im)) * x_46im) + (((x_46re * x_46im) + (x_46im * x_46re)) * x_46re)
end function

↓

real(8) function code(x_46re, x_46im)
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8) :: tmp
    if ((x_46im <= (-2.3d+113)) .or. (.not. (x_46im <= 6d+72))) then
        tmp = (x_46im * ((x_46re - x_46im) * (x_46im + x_46re))) + (x_46re * 8.0d0)
    else
        tmp = (x_46re * (3.0d0 * (x_46im * x_46re))) - (x_46im ** 3.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im) {
	return (((x_46_re * x_46_re) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re * x_46_im) + (x_46_im * x_46_re)) * x_46_re);
}

↓

public static double code(double x_46_re, double x_46_im) {
	double tmp;
	if ((x_46_im <= -2.3e+113) || !(x_46_im <= 6e+72)) {
		tmp = (x_46_im * ((x_46_re - x_46_im) * (x_46_im + x_46_re))) + (x_46_re * 8.0);
	} else {
		tmp = (x_46_re * (3.0 * (x_46_im * x_46_re))) - Math.pow(x_46_im, 3.0);
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im):
	return (((x_46_re * x_46_re) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re * x_46_im) + (x_46_im * x_46_re)) * x_46_re)

↓

def code(x_46_re, x_46_im):
	tmp = 0
	if (x_46_im <= -2.3e+113) or not (x_46_im <= 6e+72):
		tmp = (x_46_im * ((x_46_re - x_46_im) * (x_46_im + x_46_re))) + (x_46_re * 8.0)
	else:
		tmp = (x_46_re * (3.0 * (x_46_im * x_46_re))) - math.pow(x_46_im, 3.0)
	return tmp

Julia

function code(x_46_re, x_46_im)
	return Float64(Float64(Float64(Float64(x_46_re * x_46_re) - Float64(x_46_im * x_46_im)) * x_46_im) + Float64(Float64(Float64(x_46_re * x_46_im) + Float64(x_46_im * x_46_re)) * x_46_re))
end

↓

function code(x_46_re, x_46_im)
	tmp = 0.0
	if ((x_46_im <= -2.3e+113) || !(x_46_im <= 6e+72))
		tmp = Float64(Float64(x_46_im * Float64(Float64(x_46_re - x_46_im) * Float64(x_46_im + x_46_re))) + Float64(x_46_re * 8.0));
	else
		tmp = Float64(Float64(x_46_re * Float64(3.0 * Float64(x_46_im * x_46_re))) - (x_46_im ^ 3.0));
	end
	return tmp
end

MATLAB

function tmp = code(x_46_re, x_46_im)
	tmp = (((x_46_re * x_46_re) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re * x_46_im) + (x_46_im * x_46_re)) * x_46_re);
end

↓

function tmp_2 = code(x_46_re, x_46_im)
	tmp = 0.0;
	if ((x_46_im <= -2.3e+113) || ~((x_46_im <= 6e+72)))
		tmp = (x_46_im * ((x_46_re - x_46_im) * (x_46_im + x_46_re))) + (x_46_re * 8.0);
	else
		tmp = (x_46_re * (3.0 * (x_46_im * x_46_re))) - (x_46_im ^ 3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_] := N[(N[(N[(N[(x$46$re * x$46$re), $MachinePrecision] - N[(x$46$im * x$46$im), $MachinePrecision]), $MachinePrecision] * x$46$im), $MachinePrecision] + N[(N[(N[(x$46$re * x$46$im), $MachinePrecision] + N[(x$46$im * x$46$re), $MachinePrecision]), $MachinePrecision] * x$46$re), $MachinePrecision]), $MachinePrecision]

↓

code[x$46$re_, x$46$im_] := If[Or[LessEqual[x$46$im, -2.3e+113], N[Not[LessEqual[x$46$im, 6e+72]], $MachinePrecision]], N[(N[(x$46$im * N[(N[(x$46$re - x$46$im), $MachinePrecision] * N[(x$46$im + x$46$re), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x$46$re * 8.0), $MachinePrecision]), $MachinePrecision], N[(N[(x$46$re * N[(3.0 * N[(x$46$im * x$46$re), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Power[x$46$im, 3.0], $MachinePrecision]), $MachinePrecision]]

Target

Original	82.3%
Target	91.1%
Herbie	99.4%

\[\left(x.re \cdot x.im\right) \cdot \left(2 \cdot x.re\right) + \left(x.im \cdot \left(x.re - x.im\right)\right) \cdot \left(x.re + x.im\right) \]

Derivation

1. Split input into 2 regimes

2. if x.im < -2.29999999999999997e113 or 6.00000000000000006e72 < x.im

   1. Initial program 57.1%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]

   2. Applied egg-rr 72.6%

      \[\leadsto \color{blue}{\left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right)} \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]
      Step-by-step derivation

      [Start] 57.1%	\[ \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]
      difference-of-squares [=>] 72.6%	\[ \color{blue}{\left(\left(x.re + x.im\right) \cdot \left(x.re - x.im\right)\right)} \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]
      *-commutative [=>] 72.6%	\[ \color{blue}{\left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right)} \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]

   3. Applied egg-rr 54.8%

      \[\leadsto \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \color{blue}{\left(8 \cdot {x.im}^{3}\right)} \cdot x.re \]
      Step-by-step derivation

      [Start] 72.6%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]
      *-commutative [<=] 72.6%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \left(x.re \cdot x.im + \color{blue}{x.re \cdot x.im}\right) \cdot x.re \]
      flip-+ [=>] 0.0%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \color{blue}{\frac{\left(x.re \cdot x.im\right) \cdot \left(x.re \cdot x.im\right) - \left(x.re \cdot x.im\right) \cdot \left(x.re \cdot x.im\right)}{x.re \cdot x.im - x.re \cdot x.im}} \cdot x.re \]
      +-inverses [=>] 0.0%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \frac{\color{blue}{0}}{x.re \cdot x.im - x.re \cdot x.im} \cdot x.re \]
      metadata-eval [<=] 0.0%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \frac{\color{blue}{{0}^{3}}}{x.re \cdot x.im - x.re \cdot x.im} \cdot x.re \]
      +-inverses [<=] 0.0%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \frac{{\color{blue}{\left(x.im \cdot x.im - x.im \cdot x.im\right)}}^{3}}{x.re \cdot x.im - x.re \cdot x.im} \cdot x.re \]
      +-inverses [=>] 0.0%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \frac{{\left(x.im \cdot x.im - x.im \cdot x.im\right)}^{3}}{\color{blue}{0}} \cdot x.re \]
      metadata-eval [<=] 0.0%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \frac{{\left(x.im \cdot x.im - x.im \cdot x.im\right)}^{3}}{\color{blue}{{0}^{3}}} \cdot x.re \]
      +-inverses [<=] 0.0%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \frac{{\left(x.im \cdot x.im - x.im \cdot x.im\right)}^{3}}{{\color{blue}{\left(x.im - x.im\right)}}^{3}} \cdot x.re \]
      cube-div [<=] 0.0%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \color{blue}{{\left(\frac{x.im \cdot x.im - x.im \cdot x.im}{x.im - x.im}\right)}^{3}} \cdot x.re \]
      flip-+ [<=] 54.8%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + {\color{blue}{\left(x.im + x.im\right)}}^{3} \cdot x.re \]
      count-2 [=>] 54.8%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + {\color{blue}{\left(2 \cdot x.im\right)}}^{3} \cdot x.re \]
      unpow-prod-down [=>] 54.8%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \color{blue}{\left({2}^{3} \cdot {x.im}^{3}\right)} \cdot x.re \]
      metadata-eval [=>] 54.8%	\[ \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \left(\color{blue}{8} \cdot {x.im}^{3}\right) \cdot x.re \]

   4. Simplified 100.0%

      \[\leadsto \left(\left(x.re - x.im\right) \cdot \left(x.re + x.im\right)\right) \cdot x.im + \color{blue}{8} \cdot x.re \]

   if -2.29999999999999997e113 < x.im < 6.00000000000000006e72

   1. Initial program 92.1%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]

   2. Simplified 99.8%

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

      [Start] 92.1%	\[ \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]
      +-commutative [=>] 92.1%	\[ \color{blue}{\left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im} \]
      *-commutative [=>] 92.1%	\[ \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \color{blue}{x.im \cdot \left(x.re \cdot x.re - x.im \cdot x.im\right)} \]
      sub-neg [=>] 92.1%	\[ \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + x.im \cdot \color{blue}{\left(x.re \cdot x.re + \left(-x.im \cdot x.im\right)\right)} \]
      distribute-lft-in [=>] 92.1%	\[ \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \color{blue}{\left(x.im \cdot \left(x.re \cdot x.re\right) + x.im \cdot \left(-x.im \cdot x.im\right)\right)} \]
      associate-+r+ [=>] 92.1%	\[ \color{blue}{\left(\left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + x.im \cdot \left(x.re \cdot x.re\right)\right) + x.im \cdot \left(-x.im \cdot x.im\right)} \]
      distribute-rgt-neg-out [=>] 92.1%	\[ \left(\left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + x.im \cdot \left(x.re \cdot x.re\right)\right) + \color{blue}{\left(-x.im \cdot \left(x.im \cdot x.im\right)\right)} \]
      unsub-neg [=>] 92.1%	\[ \color{blue}{\left(\left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + x.im \cdot \left(x.re \cdot x.re\right)\right) - x.im \cdot \left(x.im \cdot x.im\right)} \]
      associate-*r* [=>] 99.7%	\[ \left(\left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \color{blue}{\left(x.im \cdot x.re\right) \cdot x.re}\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
      distribute-rgt-out [=>] 99.8%	\[ \color{blue}{x.re \cdot \left(\left(x.re \cdot x.im + x.im \cdot x.re\right) + x.im \cdot x.re\right)} - x.im \cdot \left(x.im \cdot x.im\right) \]
      *-commutative [=>] 99.8%	\[ x.re \cdot \left(\left(\color{blue}{x.im \cdot x.re} + x.im \cdot x.re\right) + x.im \cdot x.re\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
      count-2 [=>] 99.8%	\[ x.re \cdot \left(\color{blue}{2 \cdot \left(x.im \cdot x.re\right)} + x.im \cdot x.re\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
      distribute-lft1-in [=>] 99.8%	\[ x.re \cdot \color{blue}{\left(\left(2 + 1\right) \cdot \left(x.im \cdot x.re\right)\right)} - x.im \cdot \left(x.im \cdot x.im\right) \]
      metadata-eval [=>] 99.8%	\[ x.re \cdot \left(\color{blue}{3} \cdot \left(x.im \cdot x.re\right)\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
      *-commutative [=>] 99.8%	\[ x.re \cdot \color{blue}{\left(\left(x.im \cdot x.re\right) \cdot 3\right)} - x.im \cdot \left(x.im \cdot x.im\right) \]
      *-commutative [<=] 99.8%	\[ x.re \cdot \left(\color{blue}{\left(x.re \cdot x.im\right)} \cdot 3\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
      associate-*r* [<=] 99.7%	\[ x.re \cdot \color{blue}{\left(x.re \cdot \left(x.im \cdot 3\right)\right)} - x.im \cdot \left(x.im \cdot x.im\right) \]
      cube-unmult [=>] 99.8%	\[ x.re \cdot \left(x.re \cdot \left(x.im \cdot 3\right)\right) - \color{blue}{{x.im}^{3}} \]

   3. Taylor expanded in x.re around 0 99.9%

      \[\leadsto x.re \cdot \color{blue}{\left(3 \cdot \left(x.re \cdot x.im\right)\right)} - {x.im}^{3} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x.im \leq -2.3 \cdot 10^{+113} \lor \neg \left(x.im \leq 6 \cdot 10^{+72}\right):\\ \;\;\;\;x.im \cdot \left(\left(x.re - x.im\right) \cdot \left(x.im + x.re\right)\right) + x.re \cdot 8\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(3 \cdot \left(x.im \cdot x.re\right)\right) - {x.im}^{3}\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	99.4%
Cost	7305

\[\begin{array}{l} \mathbf{if}\;x.im \leq -2.3 \cdot 10^{+113} \lor \neg \left(x.im \leq 6 \cdot 10^{+72}\right):\\ \;\;\;\;x.im \cdot \left(\left(x.re - x.im\right) \cdot \left(x.im + x.re\right)\right) + x.re \cdot 8\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(3 \cdot \left(x.im \cdot x.re\right)\right) - {x.im}^{3}\\ \end{array} \]

Alternative 2
Accuracy	93.6%
Cost	2372

\[\begin{array}{l} \mathbf{if}\;x.im \cdot \left(x.re \cdot x.re - x.im \cdot x.im\right) + x.re \cdot \left(x.im \cdot x.re + x.im \cdot x.re\right) \leq 5 \cdot 10^{+248}:\\ \;\;\;\;x.im \cdot \left(\left(x.re - x.im\right) \cdot \left(x.im + x.re\right)\right) + x.re \cdot \left(\left(x.im \cdot x.re\right) \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x.im + x.im\right) + \left(x.re - x.im\right) \cdot \left(x.im \cdot \left(x.im + x.re\right)\right)\\ \end{array} \]

Alternative 3
Accuracy	94.4%
Cost	1225

\[\begin{array}{l} \mathbf{if}\;x.re \leq -7.6 \cdot 10^{+153} \lor \neg \left(x.re \leq 8 \cdot 10^{+153}\right):\\ \;\;\;\;\left(x.im + x.im\right) + \left(x.re - x.im\right) \cdot \left(x.im \cdot \left(x.im + x.re\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(\left(x.re \cdot x.re - x.im \cdot x.im\right) + \left(x.re \cdot x.re\right) \cdot 2\right)\\ \end{array} \]

Alternative 4
Accuracy	93.1%
Cost	1097

\[\begin{array}{l} \mathbf{if}\;x.im \leq -2 \cdot 10^{+156} \lor \neg \left(x.im \leq 3.1 \cdot 10^{+28}\right):\\ \;\;\;\;x.im \cdot \left(\left(x.re - x.im\right) \cdot \left(x.im + x.re\right)\right) + x.re \cdot 8\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(3 \cdot \left(x.re \cdot x.re\right) - x.im \cdot x.im\right)\\ \end{array} \]

Alternative 5
Accuracy	94.3%
Cost	1097

\[\begin{array}{l} \mathbf{if}\;x.re \leq -7.6 \cdot 10^{+153} \lor \neg \left(x.re \leq 8 \cdot 10^{+153}\right):\\ \;\;\;\;\left(x.im + x.im\right) + \left(x.re - x.im\right) \cdot \left(x.im \cdot \left(x.im + x.re\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(3 \cdot \left(x.re \cdot x.re\right) - x.im \cdot x.im\right)\\ \end{array} \]

Alternative 6
Accuracy	90.8%
Cost	969

\[\begin{array}{l} \mathbf{if}\;x.re \leq -7.6 \cdot 10^{+153} \lor \neg \left(x.re \leq 8 \cdot 10^{+153}\right):\\ \;\;\;\;x.re \cdot \left(x.im \cdot x.re\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(3 \cdot \left(x.re \cdot x.re\right) - x.im \cdot x.im\right)\\ \end{array} \]

Alternative 7
Accuracy	28.8%
Cost	717

\[\begin{array}{l} \mathbf{if}\;x.re \leq -2.5 \cdot 10^{+154}:\\ \;\;\;\;x.im \cdot \left(-x.re\right)\\ \mathbf{elif}\;x.re \leq -31000000000 \lor \neg \left(x.re \leq 6 \cdot 10^{+227}\right):\\ \;\;\;\;x.im \cdot \left(x.re \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot -3\right)\\ \end{array} \]

Alternative 8
Accuracy	59.4%
Cost	712

\[\begin{array}{l} \mathbf{if}\;x.im \leq -5 \cdot 10^{+187}:\\ \;\;\;\;x.im \cdot \left(x.re \cdot 2\right)\\ \mathbf{elif}\;x.im \leq 5.4 \cdot 10^{+190}:\\ \;\;\;\;x.im \cdot \left(3 \cdot \left(x.re \cdot x.re\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot -3\right)\\ \end{array} \]

Alternative 9
Accuracy	44.0%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x.im \leq -8 \cdot 10^{+188}:\\ \;\;\;\;x.im \cdot \left(x.re \cdot 2\right)\\ \mathbf{elif}\;x.im \leq 5.4 \cdot 10^{+190}:\\ \;\;\;\;x.im \cdot \left(x.re \cdot x.re\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot -3\right)\\ \end{array} \]

Alternative 10
Accuracy	44.7%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x.im \leq -1.5 \cdot 10^{+188}:\\ \;\;\;\;x.im \cdot \left(x.re \cdot 2\right)\\ \mathbf{elif}\;x.im \leq 5.4 \cdot 10^{+190}:\\ \;\;\;\;x.re \cdot \left(x.im \cdot x.re\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot -3\right)\\ \end{array} \]

Alternative 11
Accuracy	21.9%
Cost	452

\[\begin{array}{l} \mathbf{if}\;x.re \leq -2.5 \cdot 10^{+154}:\\ \;\;\;\;x.im \cdot \left(-x.re\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.re \cdot 2\right)\\ \end{array} \]

Alternative 12
Accuracy	20.7%
Cost	256

\[x.im \cdot \left(-x.re\right) \]

Alternative 13
Accuracy	3.7%
Cost	192

\[x.re \cdot 4 \]

Alternative 14
Accuracy	2.7%
Cost	64

\[-10 \]

Alternative 15
Accuracy	2.7%
Cost	64

\[-3 \]

Alternative 16
Accuracy	2.7%
Cost	64

\[0.1 \]

Alternative 17
Accuracy	2.7%
Cost	64

\[10 \]

Reproduce

herbie shell --seed 2023277 
(FPCore (x.re x.im)
  :name "math.cube on complex, imaginary part"
  :precision binary64

  :herbie-target
  (+ (* (* x.re x.im) (* 2.0 x.re)) (* (* x.im (- x.re x.im)) (+ x.re x.im)))

  (+ (* (- (* x.re x.re) (* x.im x.im)) x.im) (* (+ (* x.re x.im) (* x.im x.re)) x.re)))