math.cube on complex, real part

Average Accuracy: 89.5% → 99.7%

Time: 11.7s

Precision: binary64

Cost: 7040

Math

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

↓

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

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 (* -3.0 (* x.re x.im)))))

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 * (-3.0 * (x_46_re * x_46_im)));
}

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 * ((-3.0d0) * (x_46re * x_46im)))
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 * (-3.0 * (x_46_re * x_46_im)));
}

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 * (-3.0 * (x_46_re * x_46_im)))

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(-3.0 * Float64(x_46_re * x_46_im))))
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 * (-3.0 * (x_46_re * x_46_im)));
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[(-3.0 * N[(x$46$re * x$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	89.5%
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 89.5%

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

2. Simplified 89.6%

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

   [Start] 89.5	\[ \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   *-commutative [=>] 89.5	\[ \color{blue}{x.re \cdot \left(x.re \cdot x.re - x.im \cdot x.im\right)} - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   sub-neg [=>] 89.5	\[ x.re \cdot \color{blue}{\left(x.re \cdot x.re + \left(-x.im \cdot x.im\right)\right)} - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   distribute-rgt-in [=>] 89.5	\[ \color{blue}{\left(\left(x.re \cdot x.re\right) \cdot x.re + \left(-x.im \cdot x.im\right) \cdot x.re\right)} - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   associate--l+ [=>] 89.5	\[ \color{blue}{\left(x.re \cdot x.re\right) \cdot x.re + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im\right)} \]
   *-commutative [=>] 89.5	\[ \color{blue}{x.re \cdot \left(x.re \cdot x.re\right)} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im\right) \]
   cube-unmult [=>] 89.6	\[ \color{blue}{{x.re}^{3}} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im\right) \]
   *-commutative [=>] 89.6	\[ {x.re}^{3} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \left(\color{blue}{x.im \cdot x.re} + x.im \cdot x.re\right) \cdot x.im\right) \]
   distribute-rgt-out [=>] 89.6	\[ {x.re}^{3} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \color{blue}{\left(x.re \cdot \left(x.im + x.im\right)\right)} \cdot x.im\right) \]
   associate-*l* [=>] 89.6	\[ {x.re}^{3} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \color{blue}{x.re \cdot \left(\left(x.im + x.im\right) \cdot x.im\right)}\right) \]
   *-commutative [=>] 89.6	\[ {x.re}^{3} + \left(\left(-x.im \cdot x.im\right) \cdot x.re - \color{blue}{\left(\left(x.im + x.im\right) \cdot x.im\right) \cdot x.re}\right) \]
   distribute-rgt-out-- [=>] 89.6	\[ {x.re}^{3} + \color{blue}{x.re \cdot \left(\left(-x.im \cdot x.im\right) - \left(x.im + x.im\right) \cdot x.im\right)} \]
   neg-mul-1 [=>] 89.6	\[ {x.re}^{3} + x.re \cdot \left(\color{blue}{-1 \cdot \left(x.im \cdot x.im\right)} - \left(x.im + x.im\right) \cdot x.im\right) \]
   count-2 [=>] 89.6	\[ {x.re}^{3} + x.re \cdot \left(-1 \cdot \left(x.im \cdot x.im\right) - \color{blue}{\left(2 \cdot x.im\right)} \cdot x.im\right) \]
   associate-*l* [=>] 89.6	\[ {x.re}^{3} + x.re \cdot \left(-1 \cdot \left(x.im \cdot x.im\right) - \color{blue}{2 \cdot \left(x.im \cdot x.im\right)}\right) \]
   distribute-rgt-out-- [=>] 89.6	\[ {x.re}^{3} + x.re \cdot \color{blue}{\left(\left(x.im \cdot x.im\right) \cdot \left(-1 - 2\right)\right)} \]
   associate-*l* [<=] 89.6	\[ {x.re}^{3} + \color{blue}{\left(x.re \cdot \left(x.im \cdot x.im\right)\right) \cdot \left(-1 - 2\right)} \]
   metadata-eval [=>] 89.6	\[ {x.re}^{3} + \left(x.re \cdot \left(x.im \cdot x.im\right)\right) \cdot \color{blue}{-3} \]

3. Taylor expanded in x.re around 0 89.6%

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

4. Simplified 99.7%

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

   [Start] 89.6	\[ {x.re}^{3} + -3 \cdot \left(x.re \cdot {x.im}^{2}\right) \]
   unpow2 [=>] 89.6	\[ {x.re}^{3} + -3 \cdot \left(x.re \cdot \color{blue}{\left(x.im \cdot x.im\right)}\right) \]
   associate-*r* [=>] 99.7	\[ {x.re}^{3} + -3 \cdot \color{blue}{\left(\left(x.re \cdot x.im\right) \cdot x.im\right)} \]
   associate-*r* [=>] 99.7	\[ {x.re}^{3} + \color{blue}{\left(-3 \cdot \left(x.re \cdot x.im\right)\right) \cdot x.im} \]
   *-commutative [<=] 99.7	\[ {x.re}^{3} + \color{blue}{x.im \cdot \left(-3 \cdot \left(x.re \cdot x.im\right)\right)} \]

5. Final simplification 99.7%

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

Alternatives

Alternative 1
Accuracy	99.6%
Cost	1088

\[x.im \cdot \left(x.im \cdot \left(x.re \cdot -3\right)\right) + \left(x.re \cdot x.re\right) \cdot \left(x.re + x.im \cdot 0\right) \]

Alternative 2
Accuracy	99.6%
Cost	1088

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

Alternative 3
Accuracy	99.5%
Cost	969

\[\begin{array}{l} \mathbf{if}\;x.im \leq -3.6 \cdot 10^{+99} \lor \neg \left(x.im \leq 9.5 \cdot 10^{+79}\right):\\ \;\;\;\;x.im \cdot \left(-3 \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(x.re \cdot x.re + -3 \cdot \left(x.im \cdot x.im\right)\right)\\ \end{array} \]

Alternative 4
Accuracy	92.0%
Cost	840

\[\begin{array}{l} \mathbf{if}\;x.im \leq -5.3 \cdot 10^{-30}:\\ \;\;\;\;-3 \cdot \left(x.im \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{elif}\;x.im \leq 2 \cdot 10^{-28}:\\ \;\;\;\;x.re \cdot \left(\left(x.re + x.im\right) \cdot \left(x.re - x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot \left(x.re \cdot -3\right)\right)\\ \end{array} \]

Alternative 5
Accuracy	91.6%
Cost	713

\[\begin{array}{l} \mathbf{if}\;x.im \leq -2.8 \cdot 10^{-30} \lor \neg \left(x.im \leq 5.8 \cdot 10^{-77}\right):\\ \;\;\;\;-3 \cdot \left(x.im \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(x.re \cdot x.re\right)\\ \end{array} \]

Alternative 6
Accuracy	91.6%
Cost	712

\[\begin{array}{l} \mathbf{if}\;x.im \leq -2.2 \cdot 10^{-30}:\\ \;\;\;\;-3 \cdot \left(x.im \cdot \left(x.re \cdot x.im\right)\right)\\ \mathbf{elif}\;x.im \leq 1.15 \cdot 10^{-76}:\\ \;\;\;\;x.re \cdot \left(x.re \cdot x.re\right)\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(x.im \cdot \left(x.re \cdot -3\right)\right)\\ \end{array} \]

Alternative 7
Accuracy	59.2%
Cost	649

\[\begin{array}{l} \mathbf{if}\;x.re \leq -2.2 \cdot 10^{-115} \lor \neg \left(x.re \leq 4.2 \cdot 10^{-109}\right):\\ \;\;\;\;x.re \cdot \left(x.re \cdot x.re\right)\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot \left(x.im \cdot \left(-x.im\right)\right)\\ \end{array} \]

Alternative 8
Accuracy	56.9%
Cost	320

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

Reproduce

herbie shell --seed 2023125 
(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)))