math.cube on complex, imaginary part

Average Accuracy: 88.4% → 99.7%

Time: 11.4s

Precision: binary64

Cost: 7040

Math

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

↓

\[x.re \cdot \left(x.re \cdot \left(x.im \cdot 3\right)\right) - {x.im}^{3} \]

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
 (- (* x.re (* x.re (* x.im 3.0))) (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) {
	return (x_46_re * (x_46_re * (x_46_im * 3.0))) - pow(x_46_im, 3.0);
}

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
    code = (x_46re * (x_46re * (x_46im * 3.0d0))) - (x_46im ** 3.0d0)
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) {
	return (x_46_re * (x_46_re * (x_46_im * 3.0))) - Math.pow(x_46_im, 3.0);
}

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):
	return (x_46_re * (x_46_re * (x_46_im * 3.0))) - math.pow(x_46_im, 3.0)

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)
	return Float64(Float64(x_46_re * Float64(x_46_re * Float64(x_46_im * 3.0))) - (x_46_im ^ 3.0))
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 = code(x_46_re, x_46_im)
	tmp = (x_46_re * (x_46_re * (x_46_im * 3.0))) - (x_46_im ^ 3.0);
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_] := N[(N[(x$46$re * N[(x$46$re * N[(x$46$im * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Power[x$46$im, 3.0], $MachinePrecision]), $MachinePrecision]

Target

Original	88.4%
Target	99.6%
Herbie	99.7%

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

Derivation

1. Initial program 88.4%

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

2. Simplified 99.7%

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

   [Start] 88.4	\[ \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \]
   +-commutative [=>] 88.4	\[ \color{blue}{\left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im} \]
   *-commutative [=>] 88.4	\[ \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \color{blue}{x.im \cdot \left(x.re \cdot x.re - x.im \cdot x.im\right)} \]
   distribute-rgt-out-- [<=] 88.4	\[ \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \color{blue}{\left(\left(x.re \cdot x.re\right) \cdot x.im - \left(x.im \cdot x.im\right) \cdot x.im\right)} \]
   associate-+r- [=>] 88.4	\[ \color{blue}{\left(\left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \left(x.re \cdot x.re\right) \cdot x.im\right) - \left(x.im \cdot x.im\right) \cdot x.im} \]
   *-commutative [<=] 88.4	\[ \left(\left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re + \left(x.re \cdot x.re\right) \cdot x.im\right) - \color{blue}{x.im \cdot \left(x.im \cdot x.im\right)} \]
   *-commutative [=>] 88.4	\[ \left(\color{blue}{x.re \cdot \left(x.re \cdot x.im + x.im \cdot x.re\right)} + \left(x.re \cdot x.re\right) \cdot x.im\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
   *-commutative [<=] 88.4	\[ \left(x.re \cdot \left(x.re \cdot x.im + \color{blue}{x.re \cdot x.im}\right) + \left(x.re \cdot x.re\right) \cdot x.im\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
   distribute-lft-out [=>] 88.4	\[ \left(x.re \cdot \color{blue}{\left(x.re \cdot \left(x.im + x.im\right)\right)} + \left(x.re \cdot x.re\right) \cdot x.im\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
   associate-*r* [=>] 88.4	\[ \left(\color{blue}{\left(x.re \cdot x.re\right) \cdot \left(x.im + x.im\right)} + \left(x.re \cdot x.re\right) \cdot x.im\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
   distribute-lft-out [=>] 88.4	\[ \color{blue}{\left(x.re \cdot x.re\right) \cdot \left(\left(x.im + x.im\right) + x.im\right)} - x.im \cdot \left(x.im \cdot x.im\right) \]
   associate-*l* [=>] 99.6	\[ \color{blue}{x.re \cdot \left(x.re \cdot \left(\left(x.im + x.im\right) + x.im\right)\right)} - x.im \cdot \left(x.im \cdot x.im\right) \]
   count-2 [=>] 99.6	\[ x.re \cdot \left(x.re \cdot \left(\color{blue}{2 \cdot x.im} + x.im\right)\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
   distribute-lft1-in [=>] 99.6	\[ x.re \cdot \left(x.re \cdot \color{blue}{\left(\left(2 + 1\right) \cdot x.im\right)}\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
   *-commutative [=>] 99.6	\[ x.re \cdot \left(x.re \cdot \color{blue}{\left(x.im \cdot \left(2 + 1\right)\right)}\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
   metadata-eval [=>] 99.6	\[ x.re \cdot \left(x.re \cdot \left(x.im \cdot \color{blue}{3}\right)\right) - x.im \cdot \left(x.im \cdot x.im\right) \]
   cube-unmult [=>] 99.7	\[ x.re \cdot \left(x.re \cdot \left(x.im \cdot 3\right)\right) - \color{blue}{{x.im}^{3}} \]

3. Final simplification 99.7%

   \[\leadsto x.re \cdot \left(x.re \cdot \left(x.im \cdot 3\right)\right) - {x.im}^{3} \]

Alternatives

Alternative 1
Accuracy	99.6%
Cost	1353

\[\begin{array}{l} \mathbf{if}\;x.re \leq -2.85 \cdot 10^{+107} \lor \neg \left(x.re \leq 6 \cdot 10^{+104}\right):\\ \;\;\;\;x.re \cdot \left(3 \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.re \cdot x.re - x.im \cdot x.im\right) + x.re \cdot \left(\left(x.re \cdot x.im\right) \cdot 2\right)\\ \end{array} \]

Alternative 2
Accuracy	99.6%
Cost	1344

\[x.re \cdot \left(x.re \cdot x.im + x.re \cdot x.im\right) + \left(x.re - x.im\right) \cdot \frac{x.re + x.im}{\frac{1}{x.im}} \]

Alternative 3
Accuracy	99.6%
Cost	1344

\[\frac{x.re + x.im}{\frac{\frac{1}{x.re - x.im}}{x.im}} + x.re \cdot \left(x.re \cdot x.im + x.re \cdot x.im\right) \]

Alternative 4
Accuracy	99.2%
Cost	968

\[\begin{array}{l} \mathbf{if}\;x.re \leq -2.1 \cdot 10^{+123}:\\ \;\;\;\;x.re \cdot \left(3 \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{elif}\;x.re \leq 1.3 \cdot 10^{+51}:\\ \;\;\;\;x.im \cdot \left(x.re \cdot \left(x.re \cdot 3\right) - x.im \cdot x.im\right)\\ \mathbf{else}:\\ \;\;\;\;3 \cdot \left(x.re \cdot \left(x.re \cdot x.im\right)\right)\\ \end{array} \]

Alternative 5
Accuracy	80.7%
Cost	713

\[\begin{array}{l} \mathbf{if}\;x.re \leq -8.5 \cdot 10^{-34} \lor \neg \left(x.re \leq 7.4 \cdot 10^{-70}\right):\\ \;\;\;\;3 \cdot \left(x.im \cdot \left(x.re \cdot x.re\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot \left(-x.im\right)\right)\\ \end{array} \]

Alternative 6
Accuracy	91.9%
Cost	713

\[\begin{array}{l} \mathbf{if}\;x.re \leq -2.6 \cdot 10^{-35} \lor \neg \left(x.re \leq 7.2 \cdot 10^{-69}\right):\\ \;\;\;\;3 \cdot \left(x.re \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot \left(-x.im\right)\right)\\ \end{array} \]

Alternative 7
Accuracy	91.5%
Cost	712

\[\begin{array}{l} \mathbf{if}\;x.re \leq -3.1 \cdot 10^{-78}:\\ \;\;\;\;x.re \cdot \left(3 \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{elif}\;x.re \leq 1.38 \cdot 10^{-71}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot \left(-x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;3 \cdot \left(x.re \cdot \left(x.re \cdot x.im\right)\right)\\ \end{array} \]

Alternative 8
Accuracy	55.5%
Cost	384

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

Reproduce

herbie shell --seed 2023130 
(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)))