math.cube on complex, real part

Average Accuracy: 88.5% → 99.7%

Time: 10.2s

Precision: binary64

Cost: 7040

Math

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

↓

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

FPCore

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

↓

(FPCore (x.re x.im)
 :precision binary64
 (+ (pow x.re 3.0) (* x.im (* x.re (* 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_re) - (((x_46_re * x_46_im) + (x_46_im * x_46_re)) * x_46_im);
}

↓

double code(double x_46_re, double x_46_im) {
	return pow(x_46_re, 3.0) + (x_46_im * (x_46_re * (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_46re) - (((x_46re * x_46im) + (x_46im * x_46re)) * x_46im)
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 ** 3.0d0) + (x_46im * (x_46re * (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_re) - (((x_46_re * x_46_im) + (x_46_im * x_46_re)) * x_46_im);
}

↓

public static double code(double x_46_re, double x_46_im) {
	return Math.pow(x_46_re, 3.0) + (x_46_im * (x_46_re * (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_re) - (((x_46_re * x_46_im) + (x_46_im * x_46_re)) * x_46_im)

↓

def code(x_46_re, x_46_im):
	return math.pow(x_46_re, 3.0) + (x_46_im * (x_46_re * (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_re) - Float64(Float64(Float64(x_46_re * x_46_im) + Float64(x_46_im * x_46_re)) * x_46_im))
end

↓

function code(x_46_re, x_46_im)
	return Float64((x_46_re ^ 3.0) + Float64(x_46_im * Float64(x_46_re * Float64(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_re) - (((x_46_re * x_46_im) + (x_46_im * x_46_re)) * x_46_im);
end

↓

function tmp = code(x_46_re, x_46_im)
	tmp = (x_46_re ^ 3.0) + (x_46_im * (x_46_re * (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$46$re), $MachinePrecision] - N[(N[(N[(x$46$re * x$46$im), $MachinePrecision] + N[(x$46$im * x$46$re), $MachinePrecision]), $MachinePrecision] * x$46$im), $MachinePrecision]), $MachinePrecision]

↓

code[x$46$re_, x$46$im_] := N[(N[Power[x$46$re, 3.0], $MachinePrecision] + N[(x$46$im * N[(x$46$re * N[(x$46$im * -3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	88.5%
Target	99.6%
Herbie	99.7%

\[\left(x.re \cdot x.re\right) \cdot \left(x.re - x.im\right) + \left(x.re \cdot x.im\right) \cdot \left(x.re - 3 \cdot x.im\right) \]

Derivation

1. Initial program 88.5%

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

2. Simplified 88.6%

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

   [Start] 88.5	\[ \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   *-commutative [=>] 88.5	\[ \color{blue}{x.re \cdot \left(x.re \cdot x.re - x.im \cdot x.im\right)} - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   sub-neg [=>] 88.5	\[ x.re \cdot \color{blue}{\left(x.re \cdot x.re + \left(-x.im \cdot x.im\right)\right)} - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   distribute-rgt-in [=>] 88.5	\[ \color{blue}{\left(\left(x.re \cdot x.re\right) \cdot x.re + \left(-x.im \cdot x.im\right) \cdot x.re\right)} - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   associate--l+ [=>] 88.5	\[ \color{blue}{\left(x.re \cdot x.re\right) \cdot x.re + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im\right)} \]
   *-commutative [=>] 88.5	\[ \color{blue}{x.re \cdot \left(x.re \cdot x.re\right)} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im\right) \]
   cube-unmult [=>] 88.6	\[ \color{blue}{{x.re}^{3}} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im\right) \]
   *-commutative [=>] 88.6	\[ {x.re}^{3} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \left(\color{blue}{x.im \cdot x.re} + x.im \cdot x.re\right) \cdot x.im\right) \]
   distribute-rgt-out [=>] 88.6	\[ {x.re}^{3} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \color{blue}{\left(x.re \cdot \left(x.im + x.im\right)\right)} \cdot x.im\right) \]
   associate-*l* [=>] 88.6	\[ {x.re}^{3} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \color{blue}{x.re \cdot \left(\left(x.im + x.im\right) \cdot x.im\right)}\right) \]
   *-commutative [=>] 88.6	\[ {x.re}^{3} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \color{blue}{\left(\left(x.im + x.im\right) \cdot x.im\right) \cdot x.re}\right) \]
   distribute-rgt-out-- [=>] 88.6	\[ {x.re}^{3} + \color{blue}{x.re \cdot \left(\left(-x.im \cdot x.im\right) - \left(x.im + x.im\right) \cdot x.im\right)} \]
   neg-mul-1 [=>] 88.6	\[ {x.re}^{3} + x.re \cdot \left(\color{blue}{-1 \cdot \left(x.im \cdot x.im\right)} - \left(x.im + x.im\right) \cdot x.im\right) \]
   count-2 [=>] 88.6	\[ {x.re}^{3} + x.re \cdot \left(-1 \cdot \left(x.im \cdot x.im\right) - \color{blue}{\left(2 \cdot x.im\right)} \cdot x.im\right) \]
   associate-*l* [=>] 88.6	\[ {x.re}^{3} + x.re \cdot \left(-1 \cdot \left(x.im \cdot x.im\right) - \color{blue}{2 \cdot \left(x.im \cdot x.im\right)}\right) \]
   distribute-rgt-out-- [=>] 88.6	\[ {x.re}^{3} + x.re \cdot \color{blue}{\left(\left(x.im \cdot x.im\right) \cdot \left(-1 - 2\right)\right)} \]
   associate-*l* [<=] 88.6	\[ {x.re}^{3} + \color{blue}{\left(x.re \cdot \left(x.im \cdot x.im\right)\right) \cdot \left(-1 - 2\right)} \]
   metadata-eval [=>] 88.6	\[ {x.re}^{3} + \left(x.re \cdot \left(x.im \cdot x.im\right)\right) \cdot \color{blue}{-3} \]

3. Taylor expanded in x.re around 0 88.6%

   \[\leadsto {x.re}^{3} + \color{blue}{-3 \cdot \left(x.re \cdot {x.im}^{2}\right)} \]

4. Simplified 99.7%

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

   [Start] 88.6	\[ {x.re}^{3} + -3 \cdot \left(x.re \cdot {x.im}^{2}\right) \]
   *-commutative [=>] 88.6	\[ {x.re}^{3} + \color{blue}{\left(x.re \cdot {x.im}^{2}\right) \cdot -3} \]
   unpow2 [=>] 88.6	\[ {x.re}^{3} + \left(x.re \cdot \color{blue}{\left(x.im \cdot x.im\right)}\right) \cdot -3 \]
   associate-*r* [<=] 88.6	\[ {x.re}^{3} + \color{blue}{x.re \cdot \left(\left(x.im \cdot x.im\right) \cdot -3\right)} \]
   associate-*r* [<=] 88.5	\[ {x.re}^{3} + x.re \cdot \color{blue}{\left(x.im \cdot \left(x.im \cdot -3\right)\right)} \]
   *-commutative [=>] 88.5	\[ {x.re}^{3} + \color{blue}{\left(x.im \cdot \left(x.im \cdot -3\right)\right) \cdot x.re} \]
   associate-*l* [=>] 99.7	\[ {x.re}^{3} + \color{blue}{x.im \cdot \left(\left(x.im \cdot -3\right) \cdot x.re\right)} \]

5. Final simplification 99.7%

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

Alternatives

Alternative 1
Accuracy	99.7%
Cost	1353

\[\begin{array}{l} \mathbf{if}\;x.im \leq -2 \cdot 10^{+154} \lor \neg \left(x.im \leq 5 \cdot 10^{+110}\right):\\ \;\;\;\;-3 \cdot \left(x.im \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(x.re \cdot x.re - x.im \cdot x.im\right) - x.im \cdot \left(x.re \cdot \left(x.im + x.im\right)\right)\\ \end{array} \]

Alternative 2
Accuracy	99.6%
Cost	1216

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

Alternative 3
Accuracy	99.6%
Cost	1088

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

Alternative 4
Accuracy	99.5%
Cost	969

\[\begin{array}{l} \mathbf{if}\;x.im \leq -1.55 \cdot 10^{+92} \lor \neg \left(x.im \leq 2 \cdot 10^{+105}\right):\\ \;\;\;\;-3 \cdot \left(x.im \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(x.re \cdot x.re + x.im \cdot \left(x.im \cdot -3\right)\right)\\ \end{array} \]

Alternative 5
Accuracy	91.8%
Cost	713

\[\begin{array}{l} \mathbf{if}\;x.im \leq -4 \cdot 10^{-43} \lor \neg \left(x.im \leq 4.4 \cdot 10^{-30}\right):\\ \;\;\;\;-3 \cdot \left(x.im \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(x.re \cdot x.re\right)\\ \end{array} \]

Alternative 6
Accuracy	91.9%
Cost	712

\[\begin{array}{l} \mathbf{if}\;x.im \leq -4.1 \cdot 10^{-41}:\\ \;\;\;\;-3 \cdot \left(x.im \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{elif}\;x.im \leq 3.6 \cdot 10^{-30}:\\ \;\;\;\;x.re \cdot \left(x.re \cdot x.re\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x.im \cdot -3\right) \cdot \left(x.re \cdot x.im\right)\\ \end{array} \]

Alternative 7
Accuracy	55.6%
Cost	320

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

Reproduce

herbie shell --seed 2023141 
(FPCore (x.re x.im)
  :name "math.cube on complex, real part"
  :precision binary64

  :herbie-target
  (+ (* (* x.re x.re) (- x.re x.im)) (* (* x.re x.im) (- x.re (* 3.0 x.im))))

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