math.cube on complex, imaginary part

Percentage Accurate: 82.6% → 96.9%

Time: 3.1s

Alternatives: 5

Speedup: 1.3×

• 45.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \end{array} \]

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)))

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);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_46re, x_46im)
use fmin_fmax_functions
    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

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);
}

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)

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

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

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]

Initial Program: 82.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \end{array} \]

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)))

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);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_46re, x_46im)
use fmin_fmax_functions
    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

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);
}

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)

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

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

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]

Alternative 1: 96.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} x.re_m = \left|x.re\right| \\ \begin{array}{l} \mathbf{if}\;x.re\_m \leq 1.05 \cdot 10^{+153}:\\ \;\;\;\;\left(\left(x.re\_m \cdot x.re\_m\right) \cdot 3 - x.im \cdot x.im\right) \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;\left(\left(3 \cdot x.im\right) \cdot x.re\_m\right) \cdot x.re\_m\\ \end{array} \end{array} \]

FPCore

x.re_m = (fabs.f64 x.re)
(FPCore (x.re_m x.im)
 :precision binary64
 (if (<= x.re_m 1.05e+153)
   (* (- (* (* x.re_m x.re_m) 3.0) (* x.im x.im)) x.im)
   (* (* (* 3.0 x.im) x.re_m) x.re_m)))

C

x.re_m = fabs(x_46_re);
double code(double x_46_re_m, double x_46_im) {
	double tmp;
	if (x_46_re_m <= 1.05e+153) {
		tmp = (((x_46_re_m * x_46_re_m) * 3.0) - (x_46_im * x_46_im)) * x_46_im;
	} else {
		tmp = ((3.0 * x_46_im) * x_46_re_m) * x_46_re_m;
	}
	return tmp;
}

Fortran

x.re_m =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_46re_m, x_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re_m
    real(8), intent (in) :: x_46im
    real(8) :: tmp
    if (x_46re_m <= 1.05d+153) then
        tmp = (((x_46re_m * x_46re_m) * 3.0d0) - (x_46im * x_46im)) * x_46im
    else
        tmp = ((3.0d0 * x_46im) * x_46re_m) * x_46re_m
    end if
    code = tmp
end function

Java

x.re_m = Math.abs(x_46_re);
public static double code(double x_46_re_m, double x_46_im) {
	double tmp;
	if (x_46_re_m <= 1.05e+153) {
		tmp = (((x_46_re_m * x_46_re_m) * 3.0) - (x_46_im * x_46_im)) * x_46_im;
	} else {
		tmp = ((3.0 * x_46_im) * x_46_re_m) * x_46_re_m;
	}
	return tmp;
}

Python

x.re_m = math.fabs(x_46_re)
def code(x_46_re_m, x_46_im):
	tmp = 0
	if x_46_re_m <= 1.05e+153:
		tmp = (((x_46_re_m * x_46_re_m) * 3.0) - (x_46_im * x_46_im)) * x_46_im
	else:
		tmp = ((3.0 * x_46_im) * x_46_re_m) * x_46_re_m
	return tmp

Julia

x.re_m = abs(x_46_re)
function code(x_46_re_m, x_46_im)
	tmp = 0.0
	if (x_46_re_m <= 1.05e+153)
		tmp = Float64(Float64(Float64(Float64(x_46_re_m * x_46_re_m) * 3.0) - Float64(x_46_im * x_46_im)) * x_46_im);
	else
		tmp = Float64(Float64(Float64(3.0 * x_46_im) * x_46_re_m) * x_46_re_m);
	end
	return tmp
end

MATLAB

x.re_m = abs(x_46_re);
function tmp_2 = code(x_46_re_m, x_46_im)
	tmp = 0.0;
	if (x_46_re_m <= 1.05e+153)
		tmp = (((x_46_re_m * x_46_re_m) * 3.0) - (x_46_im * x_46_im)) * x_46_im;
	else
		tmp = ((3.0 * x_46_im) * x_46_re_m) * x_46_re_m;
	end
	tmp_2 = tmp;
end

Wolfram

x.re_m = N[Abs[x$46$re], $MachinePrecision]
code[x$46$re$95$m_, x$46$im_] := If[LessEqual[x$46$re$95$m, 1.05e+153], N[(N[(N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * 3.0), $MachinePrecision] - N[(x$46$im * x$46$im), $MachinePrecision]), $MachinePrecision] * x$46$im), $MachinePrecision], N[(N[(N[(3.0 * x$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x.re < 1.05000000000000008e153

   1. Initial program 87.9%

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

   3. Taylor expanded in x.im around 0

      \[\leadsto \color{blue}{x.im \cdot \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      5. distribute-lft1-in N/A

         \[\leadsto \left(\left(2 + 1\right) \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      6. metadata-eval N/A

         \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      7. lower-*.f64 N/A

         \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      8. pow2 N/A

         \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      9. lift-*.f64 N/A

         \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      10. mul-1-neg N/A

          \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + \left(\mathsf{neg}\left({x.im}^{2}\right)\right)\right) \cdot x.im \]

      11. lower-neg.f64 N/A

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

      12. pow2 N/A

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

      13. lift-*.f64 94.0

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

   5. Applied rewrites 94.0%

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

   6. Taylor expanded in x.re around 0

      \[\leadsto \left(3 \cdot {x.re}^{2} - {x.im}^{2}\right) \cdot x.im \]
   7. Step-by-step derivation

      1. lower--.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. pow2 N/A

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

      5. lift-*.f64 N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 94.0

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

   8. Applied rewrites 94.0%

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

   if 1.05000000000000008e153 < x.re

   1. Initial program 53.2%

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

   3. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{2} \cdot \left(x.im + 2 \cdot x.im\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt1-in N/A

         \[\leadsto \left(\left(2 + 1\right) \cdot x.im\right) \cdot {\color{blue}{x.re}}^{2} \]

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 69.2

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

   5. Applied rewrites 69.2%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. metadata-eval N/A

         \[\leadsto \left(x.re \cdot \left(\left(2 + 1\right) \cdot x.im\right)\right) \cdot x.re \]

      7. distribute-rgt1-in N/A

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

      8. lower-*.f64 N/A

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

      9. distribute-rgt1-in N/A

         \[\leadsto \left(x.re \cdot \left(\left(2 + 1\right) \cdot x.im\right)\right) \cdot x.re \]

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. lift-*.f64 91.9

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

   7. Applied rewrites 91.9%

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

Alternative 2: 59.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} x.re_m = \left|x.re\right| \\ \begin{array}{l} t_0 := \left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.im + \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.re\_m\\ \mathbf{if}\;t\_0 \leq -2 \cdot 10^{-303} \lor \neg \left(t\_0 \leq \infty\right):\\ \;\;\;\;\left(\left(-x.im\right) \cdot x.im\right) \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;\left(x.im \cdot x.re\_m\right) \cdot \left(3 \cdot x.re\_m\right)\\ \end{array} \end{array} \]

FPCore

x.re_m = (fabs.f64 x.re)
(FPCore (x.re_m x.im)
 :precision binary64
 (let* ((t_0
         (+
          (* (- (* x.re_m x.re_m) (* x.im x.im)) x.im)
          (* (+ (* x.re_m x.im) (* x.im x.re_m)) x.re_m))))
   (if (or (<= t_0 -2e-303) (not (<= t_0 INFINITY)))
     (* (* (- x.im) x.im) x.im)
     (* (* x.im x.re_m) (* 3.0 x.re_m)))))

C

x.re_m = fabs(x_46_re);
double code(double x_46_re_m, double x_46_im) {
	double t_0 = (((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_re_m);
	double tmp;
	if ((t_0 <= -2e-303) || !(t_0 <= ((double) INFINITY))) {
		tmp = (-x_46_im * x_46_im) * x_46_im;
	} else {
		tmp = (x_46_im * x_46_re_m) * (3.0 * x_46_re_m);
	}
	return tmp;
}

Java

x.re_m = Math.abs(x_46_re);
public static double code(double x_46_re_m, double x_46_im) {
	double t_0 = (((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_re_m);
	double tmp;
	if ((t_0 <= -2e-303) || !(t_0 <= Double.POSITIVE_INFINITY)) {
		tmp = (-x_46_im * x_46_im) * x_46_im;
	} else {
		tmp = (x_46_im * x_46_re_m) * (3.0 * x_46_re_m);
	}
	return tmp;
}

Python

x.re_m = math.fabs(x_46_re)
def code(x_46_re_m, x_46_im):
	t_0 = (((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_re_m)
	tmp = 0
	if (t_0 <= -2e-303) or not (t_0 <= math.inf):
		tmp = (-x_46_im * x_46_im) * x_46_im
	else:
		tmp = (x_46_im * x_46_re_m) * (3.0 * x_46_re_m)
	return tmp

Julia

x.re_m = abs(x_46_re)
function code(x_46_re_m, x_46_im)
	t_0 = Float64(Float64(Float64(Float64(x_46_re_m * x_46_re_m) - Float64(x_46_im * x_46_im)) * x_46_im) + Float64(Float64(Float64(x_46_re_m * x_46_im) + Float64(x_46_im * x_46_re_m)) * x_46_re_m))
	tmp = 0.0
	if ((t_0 <= -2e-303) || !(t_0 <= Inf))
		tmp = Float64(Float64(Float64(-x_46_im) * x_46_im) * x_46_im);
	else
		tmp = Float64(Float64(x_46_im * x_46_re_m) * Float64(3.0 * x_46_re_m));
	end
	return tmp
end

MATLAB

x.re_m = abs(x_46_re);
function tmp_2 = code(x_46_re_m, x_46_im)
	t_0 = (((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_re_m);
	tmp = 0.0;
	if ((t_0 <= -2e-303) || ~((t_0 <= Inf)))
		tmp = (-x_46_im * x_46_im) * x_46_im;
	else
		tmp = (x_46_im * x_46_re_m) * (3.0 * x_46_re_m);
	end
	tmp_2 = tmp;
end

Wolfram

x.re_m = N[Abs[x$46$re], $MachinePrecision]
code[x$46$re$95$m_, x$46$im_] := Block[{t$95$0 = N[(N[(N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] - N[(x$46$im * x$46$im), $MachinePrecision]), $MachinePrecision] * x$46$im), $MachinePrecision] + N[(N[(N[(x$46$re$95$m * x$46$im), $MachinePrecision] + N[(x$46$im * x$46$re$95$m), $MachinePrecision]), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$0, -2e-303], N[Not[LessEqual[t$95$0, Infinity]], $MachinePrecision]], N[(N[((-x$46$im) * x$46$im), $MachinePrecision] * x$46$im), $MachinePrecision], N[(N[(x$46$im * x$46$re$95$m), $MachinePrecision] * N[(3.0 * x$46$re$95$m), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.im) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.re)) < -1.99999999999999986e-303 or +inf.0 < (+.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.im) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.re))

   1. Initial program 71.9%

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

   3. Taylor expanded in x.im around 0

      \[\leadsto \color{blue}{x.im \cdot \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      5. distribute-lft1-in N/A

         \[\leadsto \left(\left(2 + 1\right) \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      6. metadata-eval N/A

         \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      7. lower-*.f64 N/A

         \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      8. pow2 N/A

         \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      9. lift-*.f64 N/A

         \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      10. mul-1-neg N/A

          \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + \left(\mathsf{neg}\left({x.im}^{2}\right)\right)\right) \cdot x.im \]

      11. lower-neg.f64 N/A

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

      12. pow2 N/A

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

      13. lift-*.f64 83.2

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

   5. Applied rewrites 83.2%

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

   6. Taylor expanded in x.re around 0

      \[\leadsto \left(-1 \cdot {x.im}^{2}\right) \cdot x.im \]
   7. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left({x.im}^{2}\right)\right) \cdot x.im \]

      2. pow2 N/A

         \[\leadsto \left(\mathsf{neg}\left(x.im \cdot x.im\right)\right) \cdot x.im \]

      3. distribute-lft-neg-in N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(x.im\right)\right) \cdot x.im\right) \cdot x.im \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(x.im\right)\right) \cdot x.im\right) \cdot x.im \]

      5. lower-neg.f64 61.1

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

   8. Applied rewrites 61.1%

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

   if -1.99999999999999986e-303 < (+.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.im) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.re)) < +inf.0

   1. Initial program 96.4%

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

   3. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{2} \cdot \left(x.im + 2 \cdot x.im\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt1-in N/A

         \[\leadsto \left(\left(2 + 1\right) \cdot x.im\right) \cdot {\color{blue}{x.re}}^{2} \]

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 66.7

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

   5. Applied rewrites 66.7%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. metadata-eval N/A

         \[\leadsto \left(x.re \cdot \left(\left(2 + 1\right) \cdot x.im\right)\right) \cdot x.re \]

      7. distribute-rgt1-in N/A

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

      8. lower-*.f64 N/A

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

      9. distribute-rgt1-in N/A

         \[\leadsto \left(x.re \cdot \left(\left(2 + 1\right) \cdot x.im\right)\right) \cdot x.re \]

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. lift-*.f64 70.1

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

   7. Applied rewrites 70.1%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*r* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 70.1

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

   9. Applied rewrites 70.1%

      \[\leadsto \left(\left(x.re \cdot x.im\right) \cdot 3\right) \cdot x.re \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

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

       2. lift-*.f64 N/A

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

       3. lift-*.f64 N/A

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

       4. associate-*l* N/A

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

       5. *-commutative N/A

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

       6. lower-*.f64 N/A

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

       7. lower-*.f64 N/A

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

       8. lift-*.f64 70.1

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

   11. Applied rewrites 70.1%

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

4. Final simplification 65.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \leq -2 \cdot 10^{-303} \lor \neg \left(\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \leq \infty\right):\\ \;\;\;\;\left(\left(-x.im\right) \cdot x.im\right) \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;\left(x.im \cdot x.re\right) \cdot \left(3 \cdot x.re\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 56.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} x.re_m = \left|x.re\right| \\ \begin{array}{l} t_0 := \left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.im + \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.re\_m\\ \mathbf{if}\;t\_0 \leq -2 \cdot 10^{-303} \lor \neg \left(t\_0 \leq \infty\right):\\ \;\;\;\;\left(\left(-x.im\right) \cdot x.im\right) \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot x.im\right) \cdot \left(x.re\_m \cdot x.re\_m\right)\\ \end{array} \end{array} \]

FPCore

x.re_m = (fabs.f64 x.re)
(FPCore (x.re_m x.im)
 :precision binary64
 (let* ((t_0
         (+
          (* (- (* x.re_m x.re_m) (* x.im x.im)) x.im)
          (* (+ (* x.re_m x.im) (* x.im x.re_m)) x.re_m))))
   (if (or (<= t_0 -2e-303) (not (<= t_0 INFINITY)))
     (* (* (- x.im) x.im) x.im)
     (* (* 3.0 x.im) (* x.re_m x.re_m)))))

C

x.re_m = fabs(x_46_re);
double code(double x_46_re_m, double x_46_im) {
	double t_0 = (((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_re_m);
	double tmp;
	if ((t_0 <= -2e-303) || !(t_0 <= ((double) INFINITY))) {
		tmp = (-x_46_im * x_46_im) * x_46_im;
	} else {
		tmp = (3.0 * x_46_im) * (x_46_re_m * x_46_re_m);
	}
	return tmp;
}

Java

x.re_m = Math.abs(x_46_re);
public static double code(double x_46_re_m, double x_46_im) {
	double t_0 = (((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_re_m);
	double tmp;
	if ((t_0 <= -2e-303) || !(t_0 <= Double.POSITIVE_INFINITY)) {
		tmp = (-x_46_im * x_46_im) * x_46_im;
	} else {
		tmp = (3.0 * x_46_im) * (x_46_re_m * x_46_re_m);
	}
	return tmp;
}

Python

x.re_m = math.fabs(x_46_re)
def code(x_46_re_m, x_46_im):
	t_0 = (((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_re_m)
	tmp = 0
	if (t_0 <= -2e-303) or not (t_0 <= math.inf):
		tmp = (-x_46_im * x_46_im) * x_46_im
	else:
		tmp = (3.0 * x_46_im) * (x_46_re_m * x_46_re_m)
	return tmp

Julia

x.re_m = abs(x_46_re)
function code(x_46_re_m, x_46_im)
	t_0 = Float64(Float64(Float64(Float64(x_46_re_m * x_46_re_m) - Float64(x_46_im * x_46_im)) * x_46_im) + Float64(Float64(Float64(x_46_re_m * x_46_im) + Float64(x_46_im * x_46_re_m)) * x_46_re_m))
	tmp = 0.0
	if ((t_0 <= -2e-303) || !(t_0 <= Inf))
		tmp = Float64(Float64(Float64(-x_46_im) * x_46_im) * x_46_im);
	else
		tmp = Float64(Float64(3.0 * x_46_im) * Float64(x_46_re_m * x_46_re_m));
	end
	return tmp
end

MATLAB

x.re_m = abs(x_46_re);
function tmp_2 = code(x_46_re_m, x_46_im)
	t_0 = (((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im) + (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_re_m);
	tmp = 0.0;
	if ((t_0 <= -2e-303) || ~((t_0 <= Inf)))
		tmp = (-x_46_im * x_46_im) * x_46_im;
	else
		tmp = (3.0 * x_46_im) * (x_46_re_m * x_46_re_m);
	end
	tmp_2 = tmp;
end

Wolfram

x.re_m = N[Abs[x$46$re], $MachinePrecision]
code[x$46$re$95$m_, x$46$im_] := Block[{t$95$0 = N[(N[(N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] - N[(x$46$im * x$46$im), $MachinePrecision]), $MachinePrecision] * x$46$im), $MachinePrecision] + N[(N[(N[(x$46$re$95$m * x$46$im), $MachinePrecision] + N[(x$46$im * x$46$re$95$m), $MachinePrecision]), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$0, -2e-303], N[Not[LessEqual[t$95$0, Infinity]], $MachinePrecision]], N[(N[((-x$46$im) * x$46$im), $MachinePrecision] * x$46$im), $MachinePrecision], N[(N[(3.0 * x$46$im), $MachinePrecision] * N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.im) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.re)) < -1.99999999999999986e-303 or +inf.0 < (+.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.im) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.re))

   1. Initial program 71.9%

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

   3. Taylor expanded in x.im around 0

      \[\leadsto \color{blue}{x.im \cdot \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      5. distribute-lft1-in N/A

         \[\leadsto \left(\left(2 + 1\right) \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      6. metadata-eval N/A

         \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      7. lower-*.f64 N/A

         \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      8. pow2 N/A

         \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      9. lift-*.f64 N/A

         \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      10. mul-1-neg N/A

          \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + \left(\mathsf{neg}\left({x.im}^{2}\right)\right)\right) \cdot x.im \]

      11. lower-neg.f64 N/A

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

      12. pow2 N/A

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

      13. lift-*.f64 83.2

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

   5. Applied rewrites 83.2%

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

   6. Taylor expanded in x.re around 0

      \[\leadsto \left(-1 \cdot {x.im}^{2}\right) \cdot x.im \]
   7. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left({x.im}^{2}\right)\right) \cdot x.im \]

      2. pow2 N/A

         \[\leadsto \left(\mathsf{neg}\left(x.im \cdot x.im\right)\right) \cdot x.im \]

      3. distribute-lft-neg-in N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(x.im\right)\right) \cdot x.im\right) \cdot x.im \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\left(\mathsf{neg}\left(x.im\right)\right) \cdot x.im\right) \cdot x.im \]

      5. lower-neg.f64 61.1

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

   8. Applied rewrites 61.1%

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

   if -1.99999999999999986e-303 < (+.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.im) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.re)) < +inf.0

   1. Initial program 96.4%

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

   3. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{2} \cdot \left(x.im + 2 \cdot x.im\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt1-in N/A

         \[\leadsto \left(\left(2 + 1\right) \cdot x.im\right) \cdot {\color{blue}{x.re}}^{2} \]

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 66.7

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

   5. Applied rewrites 66.7%

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

4. Final simplification 63.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \leq -2 \cdot 10^{-303} \lor \neg \left(\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.im + \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.re \leq \infty\right):\\ \;\;\;\;\left(\left(-x.im\right) \cdot x.im\right) \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot x.im\right) \cdot \left(x.re \cdot x.re\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 96.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} x.re_m = \left|x.re\right| \\ \begin{array}{l} \mathbf{if}\;x.re\_m \leq 1.05 \cdot 10^{+153}:\\ \;\;\;\;\left(\left(3 \cdot x.re\_m\right) \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;\left(\left(3 \cdot x.im\right) \cdot x.re\_m\right) \cdot x.re\_m\\ \end{array} \end{array} \]

FPCore

x.re_m = (fabs.f64 x.re)
(FPCore (x.re_m x.im)
 :precision binary64
 (if (<= x.re_m 1.05e+153)
   (* (- (* (* 3.0 x.re_m) x.re_m) (* x.im x.im)) x.im)
   (* (* (* 3.0 x.im) x.re_m) x.re_m)))

C

x.re_m = fabs(x_46_re);
double code(double x_46_re_m, double x_46_im) {
	double tmp;
	if (x_46_re_m <= 1.05e+153) {
		tmp = (((3.0 * x_46_re_m) * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im;
	} else {
		tmp = ((3.0 * x_46_im) * x_46_re_m) * x_46_re_m;
	}
	return tmp;
}

Fortran

x.re_m =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_46re_m, x_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re_m
    real(8), intent (in) :: x_46im
    real(8) :: tmp
    if (x_46re_m <= 1.05d+153) then
        tmp = (((3.0d0 * x_46re_m) * x_46re_m) - (x_46im * x_46im)) * x_46im
    else
        tmp = ((3.0d0 * x_46im) * x_46re_m) * x_46re_m
    end if
    code = tmp
end function

Java

x.re_m = Math.abs(x_46_re);
public static double code(double x_46_re_m, double x_46_im) {
	double tmp;
	if (x_46_re_m <= 1.05e+153) {
		tmp = (((3.0 * x_46_re_m) * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im;
	} else {
		tmp = ((3.0 * x_46_im) * x_46_re_m) * x_46_re_m;
	}
	return tmp;
}

Python

x.re_m = math.fabs(x_46_re)
def code(x_46_re_m, x_46_im):
	tmp = 0
	if x_46_re_m <= 1.05e+153:
		tmp = (((3.0 * x_46_re_m) * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im
	else:
		tmp = ((3.0 * x_46_im) * x_46_re_m) * x_46_re_m
	return tmp

Julia

x.re_m = abs(x_46_re)
function code(x_46_re_m, x_46_im)
	tmp = 0.0
	if (x_46_re_m <= 1.05e+153)
		tmp = Float64(Float64(Float64(Float64(3.0 * x_46_re_m) * x_46_re_m) - Float64(x_46_im * x_46_im)) * x_46_im);
	else
		tmp = Float64(Float64(Float64(3.0 * x_46_im) * x_46_re_m) * x_46_re_m);
	end
	return tmp
end

MATLAB

x.re_m = abs(x_46_re);
function tmp_2 = code(x_46_re_m, x_46_im)
	tmp = 0.0;
	if (x_46_re_m <= 1.05e+153)
		tmp = (((3.0 * x_46_re_m) * x_46_re_m) - (x_46_im * x_46_im)) * x_46_im;
	else
		tmp = ((3.0 * x_46_im) * x_46_re_m) * x_46_re_m;
	end
	tmp_2 = tmp;
end

Wolfram

x.re_m = N[Abs[x$46$re], $MachinePrecision]
code[x$46$re$95$m_, x$46$im_] := If[LessEqual[x$46$re$95$m, 1.05e+153], N[(N[(N[(N[(3.0 * x$46$re$95$m), $MachinePrecision] * x$46$re$95$m), $MachinePrecision] - N[(x$46$im * x$46$im), $MachinePrecision]), $MachinePrecision] * x$46$im), $MachinePrecision], N[(N[(N[(3.0 * x$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x.re < 1.05000000000000008e153

   1. Initial program 87.9%

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

   3. Taylor expanded in x.im around 0

      \[\leadsto \color{blue}{x.im \cdot \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      5. distribute-lft1-in N/A

         \[\leadsto \left(\left(2 + 1\right) \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      6. metadata-eval N/A

         \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      7. lower-*.f64 N/A

         \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      8. pow2 N/A

         \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      9. lift-*.f64 N/A

         \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

      10. mul-1-neg N/A

          \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + \left(\mathsf{neg}\left({x.im}^{2}\right)\right)\right) \cdot x.im \]

      11. lower-neg.f64 N/A

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

      12. pow2 N/A

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

      13. lift-*.f64 94.0

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

   5. Applied rewrites 94.0%

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

   6. Taylor expanded in x.re around 0

      \[\leadsto \left(3 \cdot {x.re}^{2} - {x.im}^{2}\right) \cdot x.im \]
   7. Step-by-step derivation

      1. lower--.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. pow2 N/A

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

      5. lift-*.f64 N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 94.0

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

   8. Applied rewrites 94.0%

      \[\leadsto \left(\left(x.re \cdot x.re\right) \cdot 3 - x.im \cdot x.im\right) \cdot x.im \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. pow2 N/A

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

      4. *-commutative N/A

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

      5. pow2 N/A

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

      6. associate-*r* N/A

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

      7. lower-*.f64 N/A

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

      8. lower-*.f64 94.0

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

   10. Applied rewrites 94.0%

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

   if 1.05000000000000008e153 < x.re

   1. Initial program 53.2%

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

   3. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{2} \cdot \left(x.im + 2 \cdot x.im\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt1-in N/A

         \[\leadsto \left(\left(2 + 1\right) \cdot x.im\right) \cdot {\color{blue}{x.re}}^{2} \]

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 69.2

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

   5. Applied rewrites 69.2%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. metadata-eval N/A

         \[\leadsto \left(x.re \cdot \left(\left(2 + 1\right) \cdot x.im\right)\right) \cdot x.re \]

      7. distribute-rgt1-in N/A

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

      8. lower-*.f64 N/A

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

      9. distribute-rgt1-in N/A

         \[\leadsto \left(x.re \cdot \left(\left(2 + 1\right) \cdot x.im\right)\right) \cdot x.re \]

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. lift-*.f64 91.9

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

   7. Applied rewrites 91.9%

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

Alternative 5: 58.8% accurate, 3.1× speedup

Math

\[\begin{array}{l} x.re_m = \left|x.re\right| \\ \left(\left(-x.im\right) \cdot x.im\right) \cdot x.im \end{array} \]

FPCore

x.re_m = (fabs.f64 x.re)
(FPCore (x.re_m x.im) :precision binary64 (* (* (- x.im) x.im) x.im))

C

x.re_m = fabs(x_46_re);
double code(double x_46_re_m, double x_46_im) {
	return (-x_46_im * x_46_im) * x_46_im;
}

Fortran

x.re_m =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_46re_m, x_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re_m
    real(8), intent (in) :: x_46im
    code = (-x_46im * x_46im) * x_46im
end function

Java

x.re_m = Math.abs(x_46_re);
public static double code(double x_46_re_m, double x_46_im) {
	return (-x_46_im * x_46_im) * x_46_im;
}

Python

x.re_m = math.fabs(x_46_re)
def code(x_46_re_m, x_46_im):
	return (-x_46_im * x_46_im) * x_46_im

Julia

x.re_m = abs(x_46_re)
function code(x_46_re_m, x_46_im)
	return Float64(Float64(Float64(-x_46_im) * x_46_im) * x_46_im)
end

MATLAB

x.re_m = abs(x_46_re);
function tmp = code(x_46_re_m, x_46_im)
	tmp = (-x_46_im * x_46_im) * x_46_im;
end

Wolfram

x.re_m = N[Abs[x$46$re], $MachinePrecision]
code[x$46$re$95$m_, x$46$im_] := N[(N[((-x$46$im) * x$46$im), $MachinePrecision] * x$46$im), $MachinePrecision]

Derivation

1. Initial program 84.5%

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

3. Taylor expanded in x.im around 0

   \[\leadsto \color{blue}{x.im \cdot \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

   2. lower-*.f64 N/A

      \[\leadsto \left(-1 \cdot {x.im}^{2} + \left(2 \cdot {x.re}^{2} + {x.re}^{2}\right)\right) \cdot \color{blue}{x.im} \]

   3. +-commutative N/A

      \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

   4. lower-+.f64 N/A

      \[\leadsto \left(\left(2 \cdot {x.re}^{2} + {x.re}^{2}\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

   5. distribute-lft1-in N/A

      \[\leadsto \left(\left(2 + 1\right) \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

   6. metadata-eval N/A

      \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

   7. lower-*.f64 N/A

      \[\leadsto \left(3 \cdot {x.re}^{2} + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

   8. pow2 N/A

      \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

   9. lift-*.f64 N/A

      \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + -1 \cdot {x.im}^{2}\right) \cdot x.im \]

   10. mul-1-neg N/A

       \[\leadsto \left(3 \cdot \left(x.re \cdot x.re\right) + \left(\mathsf{neg}\left({x.im}^{2}\right)\right)\right) \cdot x.im \]

   11. lower-neg.f64 N/A

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

   12. pow2 N/A

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

   13. lift-*.f64 90.0

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

5. Applied rewrites 90.0%

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

6. Taylor expanded in x.re around 0

   \[\leadsto \left(-1 \cdot {x.im}^{2}\right) \cdot x.im \]
7. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \left(\mathsf{neg}\left({x.im}^{2}\right)\right) \cdot x.im \]

   2. pow2 N/A

      \[\leadsto \left(\mathsf{neg}\left(x.im \cdot x.im\right)\right) \cdot x.im \]

   3. distribute-lft-neg-in N/A

      \[\leadsto \left(\left(\mathsf{neg}\left(x.im\right)\right) \cdot x.im\right) \cdot x.im \]

   4. lower-*.f64 N/A

      \[\leadsto \left(\left(\mathsf{neg}\left(x.im\right)\right) \cdot x.im\right) \cdot x.im \]

   5. lower-neg.f64 62.7

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

8. Applied rewrites 62.7%

   \[\leadsto \left(\left(-x.im\right) \cdot x.im\right) \cdot x.im \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2025058 
(FPCore (x.re x.im)
  :name "math.cube on complex, imaginary part"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (* (* x.re x.im) (* 2 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)))