math.cube on complex, real part

Percentage Accurate: 81.7% → 98.1%

Time: 2.8s

Alternatives: 8

Speedup: 1.4×

• 44.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

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)))

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);
}

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_46re) - (((x_46re * x_46im) + (x_46im * 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);
}

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)

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

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

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]

Initial Program: 81.7% accurate, 1.0× speedup

Math

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

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)))

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);
}

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_46re) - (((x_46re * x_46im) + (x_46im * 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);
}

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)

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

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

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]

Alternative 1: 98.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;x.re\_m \leq 6 \cdot 10^{-57}:\\ \;\;\;\;\mathsf{fma}\left(x.im + x.re\_m, \left(x.re\_m - x.im\right) \cdot x.re\_m, \left(-2 \cdot \left(x.im \cdot x.re\_m\right)\right) \cdot x.im\right)\\ \mathbf{elif}\;x.re\_m \leq 2 \cdot 10^{+229}:\\ \;\;\;\;\mathsf{fma}\left(x.re\_m + x.im, x.re\_m - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re\_m\\ \mathbf{else}:\\ \;\;\;\;\left(x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\\ \end{array} \end{array} \]

FPCore

x.re\_m = (fabs.f64 x.re)
x.re\_s = (copysign.f64 #s(literal 1 binary64) x.re)
(FPCore (x.re_s x.re_m x.im)
 :precision binary64
 (*
  x.re_s
  (if (<= x.re_m 6e-57)
    (fma
     (+ x.im x.re_m)
     (* (- x.re_m x.im) x.re_m)
     (* (* -2.0 (* x.im x.re_m)) x.im))
    (if (<= x.re_m 2e+229)
      (* (fma (+ x.re_m x.im) (- x.re_m x.im) (* -2.0 (* x.im x.im))) x.re_m)
      (* (* x.re_m x.re_m) x.re_m)))))

C

x.re\_m = fabs(x_46_re);
x.re\_s = copysign(1.0, x_46_re);
double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (x_46_re_m <= 6e-57) {
		tmp = fma((x_46_im + x_46_re_m), ((x_46_re_m - x_46_im) * x_46_re_m), ((-2.0 * (x_46_im * x_46_re_m)) * x_46_im));
	} else if (x_46_re_m <= 2e+229) {
		tmp = fma((x_46_re_m + x_46_im), (x_46_re_m - x_46_im), (-2.0 * (x_46_im * x_46_im))) * x_46_re_m;
	} else {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	}
	return x_46_re_s * tmp;
}

Julia

x.re\_m = abs(x_46_re)
x.re\_s = copysign(1.0, x_46_re)
function code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0
	if (x_46_re_m <= 6e-57)
		tmp = fma(Float64(x_46_im + x_46_re_m), Float64(Float64(x_46_re_m - x_46_im) * x_46_re_m), Float64(Float64(-2.0 * Float64(x_46_im * x_46_re_m)) * x_46_im));
	elseif (x_46_re_m <= 2e+229)
		tmp = Float64(fma(Float64(x_46_re_m + x_46_im), Float64(x_46_re_m - x_46_im), Float64(-2.0 * Float64(x_46_im * x_46_im))) * x_46_re_m);
	else
		tmp = Float64(Float64(x_46_re_m * x_46_re_m) * x_46_re_m);
	end
	return Float64(x_46_re_s * tmp)
end

Wolfram

x.re\_m = N[Abs[x$46$re], $MachinePrecision]
x.re\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x$46$re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$46$re$95$s_, x$46$re$95$m_, x$46$im_] := N[(x$46$re$95$s * If[LessEqual[x$46$re$95$m, 6e-57], N[(N[(x$46$im + x$46$re$95$m), $MachinePrecision] * N[(N[(x$46$re$95$m - x$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision] + N[(N[(-2.0 * N[(x$46$im * x$46$re$95$m), $MachinePrecision]), $MachinePrecision] * x$46$im), $MachinePrecision]), $MachinePrecision], If[LessEqual[x$46$re$95$m, 2e+229], N[(N[(N[(x$46$re$95$m + x$46$im), $MachinePrecision] * N[(x$46$re$95$m - x$46$im), $MachinePrecision] + N[(-2.0 * N[(x$46$im * x$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x$46$re$95$m), $MachinePrecision], N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]]]), $MachinePrecision]

Derivation

1. Split input into 3 regimes

2. if x.re < 6.00000000000000001e-57

   1. Initial program 85.7%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   2. Step-by-step derivation

      1. lift--.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-*.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. lift-+.f64 N/A

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

      10. fp-cancel-sub-sign-inv N/A

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

      11. pow2 N/A

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

      12. pow2 N/A

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

      13. negate-sub N/A

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

      14. mul-1-neg N/A

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

      15. +-commutative N/A

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

      16. lower-fma.f64 N/A

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

   3. Applied rewrites 85.7%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-neg.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. associate-*l* N/A

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

      10. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(x.re + x.im, \left(x.re - x.im\right) \cdot x.re, \left(\mathsf{neg}\left(2 \cdot \left(x.im \cdot x.re\right)\right)\right) \cdot x.im\right)} \]

      11. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{x.im + x.re}, \left(x.re - x.im\right) \cdot x.re, \left(\mathsf{neg}\left(2 \cdot \left(x.im \cdot x.re\right)\right)\right) \cdot x.im\right) \]

      12. lower-+.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{x.im + x.re}, \left(x.re - x.im\right) \cdot x.re, \left(\mathsf{neg}\left(2 \cdot \left(x.im \cdot x.re\right)\right)\right) \cdot x.im\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(x.im + x.re, \color{blue}{\left(x.re - x.im\right) \cdot x.re}, \left(\mathsf{neg}\left(2 \cdot \left(x.im \cdot x.re\right)\right)\right) \cdot x.im\right) \]

      14. lift--.f64 N/A

          \[\leadsto \mathsf{fma}\left(x.im + x.re, \color{blue}{\left(x.re - x.im\right)} \cdot x.re, \left(\mathsf{neg}\left(2 \cdot \left(x.im \cdot x.re\right)\right)\right) \cdot x.im\right) \]

      15. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(x.im + x.re, \left(x.re - x.im\right) \cdot x.re, \color{blue}{\left(\mathsf{neg}\left(2 \cdot \left(x.im \cdot x.re\right)\right)\right) \cdot x.im}\right) \]

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

          \[\leadsto \mathsf{fma}\left(x.im + x.re, \left(x.re - x.im\right) \cdot x.re, \color{blue}{\left(\left(\mathsf{neg}\left(2\right)\right) \cdot \left(x.im \cdot x.re\right)\right)} \cdot x.im\right) \]

      17. metadata-eval N/A

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

      18. lower-*.f64 N/A

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

      19. lift-*.f64 99.7

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

   5. Applied rewrites 99.7%

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

   if 6.00000000000000001e-57 < x.re < 2e229

   1. Initial program 87.3%

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

   2. Taylor expanded in x.re around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. fp-cancel-sub-sign-inv N/A

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

      4. +-commutative N/A

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

      5. mul-1-neg N/A

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

      6. negate-sub N/A

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

      7. pow2 N/A

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

      8. pow2 N/A

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

      9. difference-of-squares N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-+.f64 N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval N/A

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

      14. lower-*.f64 N/A

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

      15. pow2 N/A

          \[\leadsto \mathsf{fma}\left(x.re + x.im, x.re - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re \]

      16. lift-*.f64 97.9

          \[\leadsto \mathsf{fma}\left(x.re + x.im, x.re - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re \]

   4. Applied rewrites 97.9%

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

   if 2e229 < x.re

   1. Initial program 51.2%

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

   2. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{3}} \]
   3. Step-by-step derivation

      1. unpow3 N/A

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

      2. pow2 N/A

         \[\leadsto {x.re}^{2} \cdot x.re \]

      3. lower-*.f64 N/A

         \[\leadsto {x.re}^{2} \cdot \color{blue}{x.re} \]

      4. pow2 N/A

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

      5. lift-*.f64 93.6

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

   4. Applied rewrites 93.6%

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

Alternative 2: 98.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ \begin{array}{l} t_0 := \left(x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;x.re\_m \leq 2 \cdot 10^{+45}:\\ \;\;\;\;\mathsf{fma}\left(x.im \cdot \left(x.re\_m \cdot x.im\right), -3, t\_0\right)\\ \mathbf{elif}\;x.re\_m \leq 2 \cdot 10^{+229}:\\ \;\;\;\;\mathsf{fma}\left(x.re\_m + x.im, x.re\_m - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re\_m\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \end{array} \]

FPCore

x.re\_m = (fabs.f64 x.re)
x.re\_s = (copysign.f64 #s(literal 1 binary64) x.re)
(FPCore (x.re_s x.re_m x.im)
 :precision binary64
 (let* ((t_0 (* (* x.re_m x.re_m) x.re_m)))
   (*
    x.re_s
    (if (<= x.re_m 2e+45)
      (fma (* x.im (* x.re_m x.im)) -3.0 t_0)
      (if (<= x.re_m 2e+229)
        (* (fma (+ x.re_m x.im) (- x.re_m x.im) (* -2.0 (* x.im x.im))) x.re_m)
        t_0)))))

C

x.re\_m = fabs(x_46_re);
x.re\_s = copysign(1.0, x_46_re);
double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double t_0 = (x_46_re_m * x_46_re_m) * x_46_re_m;
	double tmp;
	if (x_46_re_m <= 2e+45) {
		tmp = fma((x_46_im * (x_46_re_m * x_46_im)), -3.0, t_0);
	} else if (x_46_re_m <= 2e+229) {
		tmp = fma((x_46_re_m + x_46_im), (x_46_re_m - x_46_im), (-2.0 * (x_46_im * x_46_im))) * x_46_re_m;
	} else {
		tmp = t_0;
	}
	return x_46_re_s * tmp;
}

Julia

x.re\_m = abs(x_46_re)
x.re\_s = copysign(1.0, x_46_re)
function code(x_46_re_s, x_46_re_m, x_46_im)
	t_0 = Float64(Float64(x_46_re_m * x_46_re_m) * x_46_re_m)
	tmp = 0.0
	if (x_46_re_m <= 2e+45)
		tmp = fma(Float64(x_46_im * Float64(x_46_re_m * x_46_im)), -3.0, t_0);
	elseif (x_46_re_m <= 2e+229)
		tmp = Float64(fma(Float64(x_46_re_m + x_46_im), Float64(x_46_re_m - x_46_im), Float64(-2.0 * Float64(x_46_im * x_46_im))) * x_46_re_m);
	else
		tmp = t_0;
	end
	return Float64(x_46_re_s * tmp)
end

Wolfram

x.re\_m = N[Abs[x$46$re], $MachinePrecision]
x.re\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x$46$re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$46$re$95$s_, x$46$re$95$m_, x$46$im_] := Block[{t$95$0 = N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]}, N[(x$46$re$95$s * If[LessEqual[x$46$re$95$m, 2e+45], N[(N[(x$46$im * N[(x$46$re$95$m * x$46$im), $MachinePrecision]), $MachinePrecision] * -3.0 + t$95$0), $MachinePrecision], If[LessEqual[x$46$re$95$m, 2e+229], N[(N[(N[(x$46$re$95$m + x$46$im), $MachinePrecision] * N[(x$46$re$95$m - x$46$im), $MachinePrecision] + N[(-2.0 * N[(x$46$im * x$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x$46$re$95$m), $MachinePrecision], t$95$0]]), $MachinePrecision]]

Derivation

1. Split input into 3 regimes

2. if x.re < 1.9999999999999999e45

   1. Initial program 89.7%

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

   2. Taylor expanded in x.re around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. fp-cancel-sub-sign-inv N/A

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

      4. +-commutative N/A

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

      5. mul-1-neg N/A

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

      6. negate-sub N/A

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

      7. pow2 N/A

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

      8. pow2 N/A

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

      9. difference-of-squares N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-+.f64 N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval N/A

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

      14. lower-*.f64 N/A

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

      15. pow2 N/A

          \[\leadsto \mathsf{fma}\left(x.re + x.im, x.re - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re \]

      16. lift-*.f64 89.6

          \[\leadsto \mathsf{fma}\left(x.re + x.im, x.re - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re \]

   4. Applied rewrites 89.6%

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

   5. Taylor expanded in x.im around 0

      \[\leadsto \color{blue}{{x.im}^{2} \cdot \left(-1 \cdot x.re - 2 \cdot x.re\right) + {x.re}^{3}} \]
   6. Step-by-step derivation

      1. difference-of-squares-rev N/A

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

      2. *-commutative N/A

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

      3. count-2-rev N/A

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

      4. fp-cancel-sub-sign N/A

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

      5. distribute-rgt-out-- N/A

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

      6. metadata-eval N/A

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

      7. *-commutative N/A

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

      8. associate-*l* N/A

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

      9. *-commutative N/A

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

      10. associate-*r* N/A

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

      11. *-commutative N/A

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

      12. lower-fma.f64 N/A

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

      13. lower-*.f64 N/A

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

      14. pow2 N/A

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

      15. lift-*.f64 N/A

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

      16. pow3 N/A

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

      17. lift-*.f64 N/A

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

      18. lift-*.f64 89.6

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

   7. Applied rewrites 89.6%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. lower-*.f64 N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 99.7

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

   9. Applied rewrites 99.7%

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

   if 1.9999999999999999e45 < x.re < 2e229

   1. Initial program 80.5%

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

   2. Taylor expanded in x.re around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. fp-cancel-sub-sign-inv N/A

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

      4. +-commutative N/A

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

      5. mul-1-neg N/A

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

      6. negate-sub N/A

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

      7. pow2 N/A

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

      8. pow2 N/A

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

      9. difference-of-squares N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-+.f64 N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval N/A

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

      14. lower-*.f64 N/A

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

      15. pow2 N/A

          \[\leadsto \mathsf{fma}\left(x.re + x.im, x.re - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re \]

      16. lift-*.f64 97.2

          \[\leadsto \mathsf{fma}\left(x.re + x.im, x.re - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re \]

   4. Applied rewrites 97.2%

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

   if 2e229 < x.re

   1. Initial program 51.2%

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

   2. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{3}} \]
   3. Step-by-step derivation

      1. unpow3 N/A

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

      2. pow2 N/A

         \[\leadsto {x.re}^{2} \cdot x.re \]

      3. lower-*.f64 N/A

         \[\leadsto {x.re}^{2} \cdot \color{blue}{x.re} \]

      4. pow2 N/A

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

      5. lift-*.f64 93.6

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

   4. Applied rewrites 93.6%

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

Alternative 3: 92.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;x.im \leq 1.2 \cdot 10^{+154}:\\ \;\;\;\;\mathsf{fma}\left(x.re\_m \cdot -3, x.im \cdot x.im, \left(x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-3 \cdot x.re\_m\right) \cdot x.im\right) \cdot x.im\\ \end{array} \end{array} \]

FPCore

x.re\_m = (fabs.f64 x.re)
x.re\_s = (copysign.f64 #s(literal 1 binary64) x.re)
(FPCore (x.re_s x.re_m x.im)
 :precision binary64
 (*
  x.re_s
  (if (<= x.im 1.2e+154)
    (fma (* x.re_m -3.0) (* x.im x.im) (* (* x.re_m x.re_m) x.re_m))
    (* (* (* -3.0 x.re_m) x.im) x.im))))

C

x.re\_m = fabs(x_46_re);
x.re\_s = copysign(1.0, x_46_re);
double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (x_46_im <= 1.2e+154) {
		tmp = fma((x_46_re_m * -3.0), (x_46_im * x_46_im), ((x_46_re_m * x_46_re_m) * x_46_re_m));
	} else {
		tmp = ((-3.0 * x_46_re_m) * x_46_im) * x_46_im;
	}
	return x_46_re_s * tmp;
}

Julia

x.re\_m = abs(x_46_re)
x.re\_s = copysign(1.0, x_46_re)
function code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0
	if (x_46_im <= 1.2e+154)
		tmp = fma(Float64(x_46_re_m * -3.0), Float64(x_46_im * x_46_im), Float64(Float64(x_46_re_m * x_46_re_m) * x_46_re_m));
	else
		tmp = Float64(Float64(Float64(-3.0 * x_46_re_m) * x_46_im) * x_46_im);
	end
	return Float64(x_46_re_s * tmp)
end

Wolfram

x.re\_m = N[Abs[x$46$re], $MachinePrecision]
x.re\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x$46$re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$46$re$95$s_, x$46$re$95$m_, x$46$im_] := N[(x$46$re$95$s * If[LessEqual[x$46$im, 1.2e+154], N[(N[(x$46$re$95$m * -3.0), $MachinePrecision] * N[(x$46$im * x$46$im), $MachinePrecision] + N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $MachinePrecision], N[(N[(N[(-3.0 * x$46$re$95$m), $MachinePrecision] * x$46$im), $MachinePrecision] * x$46$im), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x.im < 1.20000000000000007e154

   1. Initial program 86.2%

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

   2. Taylor expanded in x.im around 0

      \[\leadsto \color{blue}{{x.im}^{2} \cdot \left(-1 \cdot x.re - 2 \cdot x.re\right) + {x.re}^{3}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

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

      2. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(-1 \cdot x.re - 2 \cdot x.re, \color{blue}{{x.im}^{2}}, {x.re}^{3}\right) \]

      3. distribute-rgt-out-- N/A

         \[\leadsto \mathsf{fma}\left(x.re \cdot \left(-1 - 2\right), {\color{blue}{x.im}}^{2}, {x.re}^{3}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(x.re \cdot \left(-1 - 2\right), {\color{blue}{x.im}}^{2}, {x.re}^{3}\right) \]

      5. metadata-eval N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 N/A

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

      8. unpow3 N/A

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

      9. pow2 N/A

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

      10. lower-*.f64 N/A

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

      11. pow2 N/A

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

      12. lift-*.f64 91.0

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

   4. Applied rewrites 91.0%

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

   if 1.20000000000000007e154 < x.im

   1. Initial program 50.3%

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

   2. Taylor expanded in x.im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt-out-- N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 62.9

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

   4. Applied rewrites 62.9%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. metadata-eval N/A

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

      7. distribute-rgt-out N/A

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

      8. lower-*.f64 N/A

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

      9. distribute-rgt-out N/A

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

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 85.9

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

   6. Applied rewrites 85.9%

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

Alternative 4: 92.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;x.im \leq 9.5 \cdot 10^{+153}:\\ \;\;\;\;\mathsf{fma}\left(x.im \cdot x.im, -3, x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\\ \mathbf{else}:\\ \;\;\;\;\left(\left(-3 \cdot x.re\_m\right) \cdot x.im\right) \cdot x.im\\ \end{array} \end{array} \]

FPCore

x.re\_m = (fabs.f64 x.re)
x.re\_s = (copysign.f64 #s(literal 1 binary64) x.re)
(FPCore (x.re_s x.re_m x.im)
 :precision binary64
 (*
  x.re_s
  (if (<= x.im 9.5e+153)
    (* (fma (* x.im x.im) -3.0 (* x.re_m x.re_m)) x.re_m)
    (* (* (* -3.0 x.re_m) x.im) x.im))))

C

x.re\_m = fabs(x_46_re);
x.re\_s = copysign(1.0, x_46_re);
double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (x_46_im <= 9.5e+153) {
		tmp = fma((x_46_im * x_46_im), -3.0, (x_46_re_m * x_46_re_m)) * x_46_re_m;
	} else {
		tmp = ((-3.0 * x_46_re_m) * x_46_im) * x_46_im;
	}
	return x_46_re_s * tmp;
}

Julia

x.re\_m = abs(x_46_re)
x.re\_s = copysign(1.0, x_46_re)
function code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0
	if (x_46_im <= 9.5e+153)
		tmp = Float64(fma(Float64(x_46_im * x_46_im), -3.0, Float64(x_46_re_m * x_46_re_m)) * x_46_re_m);
	else
		tmp = Float64(Float64(Float64(-3.0 * x_46_re_m) * x_46_im) * x_46_im);
	end
	return Float64(x_46_re_s * tmp)
end

Wolfram

x.re\_m = N[Abs[x$46$re], $MachinePrecision]
x.re\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x$46$re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$46$re$95$s_, x$46$re$95$m_, x$46$im_] := N[(x$46$re$95$s * If[LessEqual[x$46$im, 9.5e+153], N[(N[(N[(x$46$im * x$46$im), $MachinePrecision] * -3.0 + N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision]), $MachinePrecision] * x$46$re$95$m), $MachinePrecision], N[(N[(N[(-3.0 * x$46$re$95$m), $MachinePrecision] * x$46$im), $MachinePrecision] * x$46$im), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x.im < 9.4999999999999995e153

   1. Initial program 86.2%

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

   2. Taylor expanded in x.re around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. fp-cancel-sub-sign-inv N/A

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

      4. +-commutative N/A

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

      5. mul-1-neg N/A

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

      6. negate-sub N/A

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

      7. pow2 N/A

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

      8. pow2 N/A

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

      9. difference-of-squares N/A

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

      10. lower-fma.f64 N/A

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

      11. lower-+.f64 N/A

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

      12. lower--.f64 N/A

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

      13. metadata-eval N/A

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

      14. lower-*.f64 N/A

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

      15. pow2 N/A

          \[\leadsto \mathsf{fma}\left(x.re + x.im, x.re - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re \]

      16. lift-*.f64 94.6

          \[\leadsto \mathsf{fma}\left(x.re + x.im, x.re - x.im, -2 \cdot \left(x.im \cdot x.im\right)\right) \cdot x.re \]

   4. Applied rewrites 94.6%

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

   5. Taylor expanded in x.im around 0

      \[\leadsto \color{blue}{{x.im}^{2} \cdot \left(-1 \cdot x.re - 2 \cdot x.re\right) + {x.re}^{3}} \]
   6. Step-by-step derivation

      1. difference-of-squares-rev N/A

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

      2. *-commutative N/A

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

      3. count-2-rev N/A

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

      4. fp-cancel-sub-sign N/A

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

      5. distribute-rgt-out-- N/A

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

      6. metadata-eval N/A

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

      7. *-commutative N/A

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

      8. associate-*l* N/A

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

      9. *-commutative N/A

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

      10. associate-*r* N/A

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

      11. *-commutative N/A

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

      12. lower-fma.f64 N/A

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

      13. lower-*.f64 N/A

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

      14. pow2 N/A

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

      15. lift-*.f64 N/A

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

      16. pow3 N/A

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

      17. lift-*.f64 N/A

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

      18. lift-*.f64 84.8

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

   7. Applied rewrites 84.8%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. fp-cancel-sign-sub-inv N/A

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

      7. +-commutative N/A

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

      8. associate-*r* N/A

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

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

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

      10. metadata-eval N/A

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

      11. fp-cancel-sign-sub-inv N/A

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

   9. Applied rewrites 92.4%

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

   if 9.4999999999999995e153 < x.im

   1. Initial program 50.3%

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

   2. Taylor expanded in x.im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt-out-- N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 62.9

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

   4. Applied rewrites 62.9%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. metadata-eval N/A

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

      7. distribute-rgt-out N/A

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

      8. lower-*.f64 N/A

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

      9. distribute-rgt-out N/A

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

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 85.9

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

   6. Applied rewrites 85.9%

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

Alternative 5: 91.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;\left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.re\_m - \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.im \leq -2 \cdot 10^{-202}:\\ \;\;\;\;\left(\left(-3 \cdot x.re\_m\right) \cdot x.im\right) \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;\left(x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\\ \end{array} \end{array} \]

FPCore

x.re\_m = (fabs.f64 x.re)
x.re\_s = (copysign.f64 #s(literal 1 binary64) x.re)
(FPCore (x.re_s x.re_m x.im)
 :precision binary64
 (*
  x.re_s
  (if (<=
       (-
        (* (- (* x.re_m x.re_m) (* x.im x.im)) x.re_m)
        (* (+ (* x.re_m x.im) (* x.im x.re_m)) x.im))
       -2e-202)
    (* (* (* -3.0 x.re_m) x.im) x.im)
    (* (* x.re_m x.re_m) x.re_m))))

C

x.re\_m = fabs(x_46_re);
x.re\_s = copysign(1.0, x_46_re);
double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202) {
		tmp = ((-3.0 * x_46_re_m) * x_46_im) * x_46_im;
	} else {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	}
	return x_46_re_s * tmp;
}

Fortran

x.re\_m =     private
x.re\_s =     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_s, x_46re_m, x_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re_s
    real(8), intent (in) :: x_46re_m
    real(8), intent (in) :: x_46im
    real(8) :: tmp
    if (((((x_46re_m * x_46re_m) - (x_46im * x_46im)) * x_46re_m) - (((x_46re_m * x_46im) + (x_46im * x_46re_m)) * x_46im)) <= (-2d-202)) then
        tmp = (((-3.0d0) * x_46re_m) * x_46im) * x_46im
    else
        tmp = (x_46re_m * x_46re_m) * x_46re_m
    end if
    code = x_46re_s * tmp
end function

Java

x.re\_m = Math.abs(x_46_re);
x.re\_s = Math.copySign(1.0, x_46_re);
public static double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202) {
		tmp = ((-3.0 * x_46_re_m) * x_46_im) * x_46_im;
	} else {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	}
	return x_46_re_s * tmp;
}

Python

x.re\_m = math.fabs(x_46_re)
x.re\_s = math.copysign(1.0, x_46_re)
def code(x_46_re_s, x_46_re_m, x_46_im):
	tmp = 0
	if ((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202:
		tmp = ((-3.0 * x_46_re_m) * x_46_im) * x_46_im
	else:
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m
	return x_46_re_s * tmp

Julia

x.re\_m = abs(x_46_re)
x.re\_s = copysign(1.0, x_46_re)
function code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x_46_re_m * x_46_re_m) - Float64(x_46_im * x_46_im)) * x_46_re_m) - Float64(Float64(Float64(x_46_re_m * x_46_im) + Float64(x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202)
		tmp = Float64(Float64(Float64(-3.0 * x_46_re_m) * x_46_im) * x_46_im);
	else
		tmp = Float64(Float64(x_46_re_m * x_46_re_m) * x_46_re_m);
	end
	return Float64(x_46_re_s * tmp)
end

MATLAB

x.re\_m = abs(x_46_re);
x.re\_s = sign(x_46_re) * abs(1.0);
function tmp_2 = code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202)
		tmp = ((-3.0 * x_46_re_m) * x_46_im) * x_46_im;
	else
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	end
	tmp_2 = x_46_re_s * tmp;
end

Wolfram

x.re\_m = N[Abs[x$46$re], $MachinePrecision]
x.re\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x$46$re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$46$re$95$s_, x$46$re$95$m_, x$46$im_] := N[(x$46$re$95$s * If[LessEqual[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$46$re$95$m), $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$46$im), $MachinePrecision]), $MachinePrecision], -2e-202], N[(N[(N[(-3.0 * x$46$re$95$m), $MachinePrecision] * x$46$im), $MachinePrecision] * x$46$im), $MachinePrecision], N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * 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.re) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.im)) < -2.0000000000000001e-202

   1. Initial program 82.7%

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

   2. Taylor expanded in x.im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt-out-- N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 82.2

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

   4. Applied rewrites 82.2%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. metadata-eval N/A

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

      7. distribute-rgt-out N/A

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

      8. lower-*.f64 N/A

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

      9. distribute-rgt-out N/A

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

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 99.1

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

   6. Applied rewrites 99.1%

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

   if -2.0000000000000001e-202 < (-.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.re) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.im))

   1. Initial program 81.2%

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

   2. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{3}} \]
   3. Step-by-step derivation

      1. unpow3 N/A

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

      2. pow2 N/A

         \[\leadsto {x.re}^{2} \cdot x.re \]

      3. lower-*.f64 N/A

         \[\leadsto {x.re}^{2} \cdot \color{blue}{x.re} \]

      4. pow2 N/A

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

      5. lift-*.f64 89.7

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

   4. Applied rewrites 89.7%

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

Alternative 6: 90.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;\left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.re\_m - \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.im \leq -2 \cdot 10^{-202}:\\ \;\;\;\;\left(\left(-3 \cdot x.im\right) \cdot x.re\_m\right) \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;\left(x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\\ \end{array} \end{array} \]

FPCore

x.re\_m = (fabs.f64 x.re)
x.re\_s = (copysign.f64 #s(literal 1 binary64) x.re)
(FPCore (x.re_s x.re_m x.im)
 :precision binary64
 (*
  x.re_s
  (if (<=
       (-
        (* (- (* x.re_m x.re_m) (* x.im x.im)) x.re_m)
        (* (+ (* x.re_m x.im) (* x.im x.re_m)) x.im))
       -2e-202)
    (* (* (* -3.0 x.im) x.re_m) x.im)
    (* (* x.re_m x.re_m) x.re_m))))

C

x.re\_m = fabs(x_46_re);
x.re\_s = copysign(1.0, x_46_re);
double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202) {
		tmp = ((-3.0 * x_46_im) * x_46_re_m) * x_46_im;
	} else {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	}
	return x_46_re_s * tmp;
}

Fortran

x.re\_m =     private
x.re\_s =     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_s, x_46re_m, x_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re_s
    real(8), intent (in) :: x_46re_m
    real(8), intent (in) :: x_46im
    real(8) :: tmp
    if (((((x_46re_m * x_46re_m) - (x_46im * x_46im)) * x_46re_m) - (((x_46re_m * x_46im) + (x_46im * x_46re_m)) * x_46im)) <= (-2d-202)) then
        tmp = (((-3.0d0) * x_46im) * x_46re_m) * x_46im
    else
        tmp = (x_46re_m * x_46re_m) * x_46re_m
    end if
    code = x_46re_s * tmp
end function

Java

x.re\_m = Math.abs(x_46_re);
x.re\_s = Math.copySign(1.0, x_46_re);
public static double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202) {
		tmp = ((-3.0 * x_46_im) * x_46_re_m) * x_46_im;
	} else {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	}
	return x_46_re_s * tmp;
}

Python

x.re\_m = math.fabs(x_46_re)
x.re\_s = math.copysign(1.0, x_46_re)
def code(x_46_re_s, x_46_re_m, x_46_im):
	tmp = 0
	if ((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202:
		tmp = ((-3.0 * x_46_im) * x_46_re_m) * x_46_im
	else:
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m
	return x_46_re_s * tmp

Julia

x.re\_m = abs(x_46_re)
x.re\_s = copysign(1.0, x_46_re)
function code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x_46_re_m * x_46_re_m) - Float64(x_46_im * x_46_im)) * x_46_re_m) - Float64(Float64(Float64(x_46_re_m * x_46_im) + Float64(x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202)
		tmp = Float64(Float64(Float64(-3.0 * x_46_im) * x_46_re_m) * x_46_im);
	else
		tmp = Float64(Float64(x_46_re_m * x_46_re_m) * x_46_re_m);
	end
	return Float64(x_46_re_s * tmp)
end

MATLAB

x.re\_m = abs(x_46_re);
x.re\_s = sign(x_46_re) * abs(1.0);
function tmp_2 = code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202)
		tmp = ((-3.0 * x_46_im) * x_46_re_m) * x_46_im;
	else
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	end
	tmp_2 = x_46_re_s * tmp;
end

Wolfram

x.re\_m = N[Abs[x$46$re], $MachinePrecision]
x.re\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x$46$re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$46$re$95$s_, x$46$re$95$m_, x$46$im_] := N[(x$46$re$95$s * If[LessEqual[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$46$re$95$m), $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$46$im), $MachinePrecision]), $MachinePrecision], -2e-202], N[(N[(N[(-3.0 * x$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision] * x$46$im), $MachinePrecision], N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * 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.re) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.im)) < -2.0000000000000001e-202

   1. Initial program 82.7%

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

   2. Taylor expanded in x.im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt-out-- N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 82.2

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

   4. Applied rewrites 82.2%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. metadata-eval N/A

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

      7. distribute-rgt-out N/A

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

      8. lower-*.f64 N/A

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

      9. distribute-rgt-out N/A

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

      10. metadata-eval N/A

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

      11. *-commutative N/A

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

      12. lower-*.f64 N/A

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

      13. *-commutative N/A

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

      14. lower-*.f64 99.1

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

   6. Applied rewrites 99.1%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. *-commutative N/A

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

      5. associate-*r* N/A

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

      6. lower-*.f64 N/A

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

      7. lower-*.f64 99.1

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

   8. Applied rewrites 99.1%

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

   if -2.0000000000000001e-202 < (-.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.re) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.im))

   1. Initial program 81.2%

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

   2. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{3}} \]
   3. Step-by-step derivation

      1. unpow3 N/A

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

      2. pow2 N/A

         \[\leadsto {x.re}^{2} \cdot x.re \]

      3. lower-*.f64 N/A

         \[\leadsto {x.re}^{2} \cdot \color{blue}{x.re} \]

      4. pow2 N/A

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

      5. lift-*.f64 89.7

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

   4. Applied rewrites 89.7%

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

Alternative 7: 87.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;\left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.re\_m - \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.im \leq -2 \cdot 10^{-202}:\\ \;\;\;\;\left(x.re\_m \cdot -3\right) \cdot \left(x.im \cdot x.im\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\\ \end{array} \end{array} \]

FPCore

x.re\_m = (fabs.f64 x.re)
x.re\_s = (copysign.f64 #s(literal 1 binary64) x.re)
(FPCore (x.re_s x.re_m x.im)
 :precision binary64
 (*
  x.re_s
  (if (<=
       (-
        (* (- (* x.re_m x.re_m) (* x.im x.im)) x.re_m)
        (* (+ (* x.re_m x.im) (* x.im x.re_m)) x.im))
       -2e-202)
    (* (* x.re_m -3.0) (* x.im x.im))
    (* (* x.re_m x.re_m) x.re_m))))

C

x.re\_m = fabs(x_46_re);
x.re\_s = copysign(1.0, x_46_re);
double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202) {
		tmp = (x_46_re_m * -3.0) * (x_46_im * x_46_im);
	} else {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	}
	return x_46_re_s * tmp;
}

Fortran

x.re\_m =     private
x.re\_s =     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_s, x_46re_m, x_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re_s
    real(8), intent (in) :: x_46re_m
    real(8), intent (in) :: x_46im
    real(8) :: tmp
    if (((((x_46re_m * x_46re_m) - (x_46im * x_46im)) * x_46re_m) - (((x_46re_m * x_46im) + (x_46im * x_46re_m)) * x_46im)) <= (-2d-202)) then
        tmp = (x_46re_m * (-3.0d0)) * (x_46im * x_46im)
    else
        tmp = (x_46re_m * x_46re_m) * x_46re_m
    end if
    code = x_46re_s * tmp
end function

Java

x.re\_m = Math.abs(x_46_re);
x.re\_s = Math.copySign(1.0, x_46_re);
public static double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	double tmp;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202) {
		tmp = (x_46_re_m * -3.0) * (x_46_im * x_46_im);
	} else {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	}
	return x_46_re_s * tmp;
}

Python

x.re\_m = math.fabs(x_46_re)
x.re\_s = math.copysign(1.0, x_46_re)
def code(x_46_re_s, x_46_re_m, x_46_im):
	tmp = 0
	if ((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202:
		tmp = (x_46_re_m * -3.0) * (x_46_im * x_46_im)
	else:
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m
	return x_46_re_s * tmp

Julia

x.re\_m = abs(x_46_re)
x.re\_s = copysign(1.0, x_46_re)
function code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x_46_re_m * x_46_re_m) - Float64(x_46_im * x_46_im)) * x_46_re_m) - Float64(Float64(Float64(x_46_re_m * x_46_im) + Float64(x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202)
		tmp = Float64(Float64(x_46_re_m * -3.0) * Float64(x_46_im * x_46_im));
	else
		tmp = Float64(Float64(x_46_re_m * x_46_re_m) * x_46_re_m);
	end
	return Float64(x_46_re_s * tmp)
end

MATLAB

x.re\_m = abs(x_46_re);
x.re\_s = sign(x_46_re) * abs(1.0);
function tmp_2 = code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = 0.0;
	if (((((x_46_re_m * x_46_re_m) - (x_46_im * x_46_im)) * x_46_re_m) - (((x_46_re_m * x_46_im) + (x_46_im * x_46_re_m)) * x_46_im)) <= -2e-202)
		tmp = (x_46_re_m * -3.0) * (x_46_im * x_46_im);
	else
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	end
	tmp_2 = x_46_re_s * tmp;
end

Wolfram

x.re\_m = N[Abs[x$46$re], $MachinePrecision]
x.re\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x$46$re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$46$re$95$s_, x$46$re$95$m_, x$46$im_] := N[(x$46$re$95$s * If[LessEqual[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$46$re$95$m), $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$46$im), $MachinePrecision]), $MachinePrecision], -2e-202], N[(N[(x$46$re$95$m * -3.0), $MachinePrecision] * N[(x$46$im * x$46$im), $MachinePrecision]), $MachinePrecision], N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * 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.re) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.im)) < -2.0000000000000001e-202

   1. Initial program 82.7%

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

   2. Taylor expanded in x.im around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. distribute-rgt-out-- N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. pow2 N/A

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

      7. lift-*.f64 82.2

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

   4. Applied rewrites 82.2%

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

   if -2.0000000000000001e-202 < (-.f64 (*.f64 (-.f64 (*.f64 x.re x.re) (*.f64 x.im x.im)) x.re) (*.f64 (+.f64 (*.f64 x.re x.im) (*.f64 x.im x.re)) x.im))

   1. Initial program 81.2%

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

   2. Taylor expanded in x.re around inf

      \[\leadsto \color{blue}{{x.re}^{3}} \]
   3. Step-by-step derivation

      1. unpow3 N/A

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

      2. pow2 N/A

         \[\leadsto {x.re}^{2} \cdot x.re \]

      3. lower-*.f64 N/A

         \[\leadsto {x.re}^{2} \cdot \color{blue}{x.re} \]

      4. pow2 N/A

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

      5. lift-*.f64 89.7

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

   4. Applied rewrites 89.7%

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

Alternative 8: 60.0% accurate, 3.9× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \left(\left(x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\right) \end{array} \]

FPCore

x.re\_m = (fabs.f64 x.re)
x.re\_s = (copysign.f64 #s(literal 1 binary64) x.re)
(FPCore (x.re_s x.re_m x.im)
 :precision binary64
 (* x.re_s (* (* x.re_m x.re_m) x.re_m)))

C

x.re\_m = fabs(x_46_re);
x.re\_s = copysign(1.0, x_46_re);
double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	return x_46_re_s * ((x_46_re_m * x_46_re_m) * x_46_re_m);
}

Fortran

x.re\_m =     private
x.re\_s =     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_s, x_46re_m, x_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re_s
    real(8), intent (in) :: x_46re_m
    real(8), intent (in) :: x_46im
    code = x_46re_s * ((x_46re_m * x_46re_m) * x_46re_m)
end function

Java

x.re\_m = Math.abs(x_46_re);
x.re\_s = Math.copySign(1.0, x_46_re);
public static double code(double x_46_re_s, double x_46_re_m, double x_46_im) {
	return x_46_re_s * ((x_46_re_m * x_46_re_m) * x_46_re_m);
}

Python

x.re\_m = math.fabs(x_46_re)
x.re\_s = math.copysign(1.0, x_46_re)
def code(x_46_re_s, x_46_re_m, x_46_im):
	return x_46_re_s * ((x_46_re_m * x_46_re_m) * x_46_re_m)

Julia

x.re\_m = abs(x_46_re)
x.re\_s = copysign(1.0, x_46_re)
function code(x_46_re_s, x_46_re_m, x_46_im)
	return Float64(x_46_re_s * Float64(Float64(x_46_re_m * x_46_re_m) * x_46_re_m))
end

MATLAB

x.re\_m = abs(x_46_re);
x.re\_s = sign(x_46_re) * abs(1.0);
function tmp = code(x_46_re_s, x_46_re_m, x_46_im)
	tmp = x_46_re_s * ((x_46_re_m * x_46_re_m) * x_46_re_m);
end

Wolfram

x.re\_m = N[Abs[x$46$re], $MachinePrecision]
x.re\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x$46$re]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$46$re$95$s_, x$46$re$95$m_, x$46$im_] := N[(x$46$re$95$s * N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 81.7%

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

2. Taylor expanded in x.re around inf

   \[\leadsto \color{blue}{{x.re}^{3}} \]
3. Step-by-step derivation

   1. unpow3 N/A

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

   2. pow2 N/A

      \[\leadsto {x.re}^{2} \cdot x.re \]

   3. lower-*.f64 N/A

      \[\leadsto {x.re}^{2} \cdot \color{blue}{x.re} \]

   4. pow2 N/A

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

   5. lift-*.f64 60.0

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

4. Applied rewrites 60.0%

   \[\leadsto \color{blue}{\left(x.re \cdot x.re\right) \cdot x.re} \]
5. Add Preprocessing

Developer Target 1: 87.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \left(x.re \cdot x.re\right) \cdot \left(x.re - x.im\right) + \left(x.re \cdot x.im\right) \cdot \left(x.re - 3 \cdot x.im\right) \end{array} \]

FPCore

(FPCore (x.re x.im)
 :precision binary64
 (+ (* (* x.re x.re) (- x.re x.im)) (* (* x.re x.im) (- x.re (* 3.0 x.im)))))

C

double code(double x_46_re, double x_46_im) {
	return ((x_46_re * x_46_re) * (x_46_re - x_46_im)) + ((x_46_re * x_46_im) * (x_46_re - (3.0 * x_46_im)));
}

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_46re - x_46im)) + ((x_46re * x_46im) * (x_46re - (3.0d0 * 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_re - x_46_im)) + ((x_46_re * x_46_im) * (x_46_re - (3.0 * x_46_im)));
}

Python

def code(x_46_re, x_46_im):
	return ((x_46_re * x_46_re) * (x_46_re - x_46_im)) + ((x_46_re * x_46_im) * (x_46_re - (3.0 * x_46_im)))

Julia

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

MATLAB

function tmp = code(x_46_re, x_46_im)
	tmp = ((x_46_re * x_46_re) * (x_46_re - x_46_im)) + ((x_46_re * x_46_im) * (x_46_re - (3.0 * x_46_im)));
end

Wolfram

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

Reproduce

herbie shell --seed 2025110 
(FPCore (x.re x.im)
  :name "math.cube on complex, real part"
  :precision binary64

  :alt
  (! :herbie-platform c (+ (* (* x.re x.re) (- x.re x.im)) (* (* x.re x.im) (- x.re (* 3 x.im)))))

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