math.cube on complex, real part

Percentage Accurate: 82.4% → 99.8%

Time: 2.0s

Alternatives: 8

Speedup: 1.0×

• 44.5% 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: 82.4% 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: 99.8% accurate, 0.6× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;x.re\_m \leq 2 \cdot 10^{+88}:\\ \;\;\;\;\mathsf{fma}\left(x.im, \mathsf{fma}\left(-3, x.im \cdot x.re\_m, x.re\_m \cdot \left(x.re\_m + -1 \cdot x.re\_m\right)\right), {x.re\_m}^{3}\right)\\ \mathbf{else}:\\ \;\;\;\;1 \cdot \left(\left(\left(x.re\_m + x.im\right) \cdot \left(x.re\_m - x.im\right)\right) \cdot x.re\_m\right)\\ \end{array} \end{array} \]

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 2e+88)
    (fma
     x.im
     (fma -3.0 (* x.im x.re_m) (* x.re_m (+ x.re_m (* -1.0 x.re_m))))
     (pow x.re_m 3.0))
    (* 1.0 (* (* (+ x.re_m x.im) (- x.re_m x.im)) 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 <= 2e+88) {
		tmp = fma(x_46_im, fma(-3.0, (x_46_im * x_46_re_m), (x_46_re_m * (x_46_re_m + (-1.0 * x_46_re_m)))), pow(x_46_re_m, 3.0));
	} else {
		tmp = 1.0 * (((x_46_re_m + x_46_im) * (x_46_re_m - x_46_im)) * 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 <= 2e+88)
		tmp = fma(x_46_im, fma(-3.0, Float64(x_46_im * x_46_re_m), Float64(x_46_re_m * Float64(x_46_re_m + Float64(-1.0 * x_46_re_m)))), (x_46_re_m ^ 3.0));
	else
		tmp = Float64(1.0 * Float64(Float64(Float64(x_46_re_m + x_46_im) * Float64(x_46_re_m - x_46_im)) * 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, 2e+88], N[(x$46$im * N[(-3.0 * N[(x$46$im * x$46$re$95$m), $MachinePrecision] + N[(x$46$re$95$m * N[(x$46$re$95$m + N[(-1.0 * x$46$re$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[x$46$re$95$m, 3.0], $MachinePrecision]), $MachinePrecision], N[(1.0 * N[(N[(N[(x$46$re$95$m + x$46$im), $MachinePrecision] * N[(x$46$re$95$m - x$46$im), $MachinePrecision]), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x.re < 1.99999999999999992e88

   1. Initial program 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.im around 0

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

      1. lower-fma.f64 N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-pow.f64 87.9

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

   6. Applied rewrites 87.9%

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

   if 1.99999999999999992e88 < x.re

   1. Initial program 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around inf

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

   6. Applied rewrites 78.5%

      \[\leadsto \color{blue}{1} \cdot \left(\left(\left(x.re + x.im\right) \cdot \left(x.re - x.im\right)\right) \cdot x.re\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;x.re\_m \leq 6.5 \cdot 10^{+64}:\\ \;\;\;\;\mathsf{fma}\left(x.im + x.re\_m, \left(x.re\_m - x.im\right) \cdot x.re\_m, \left(-x.im \cdot \left(x.re\_m + x.re\_m\right)\right) \cdot x.im\right)\\ \mathbf{else}:\\ \;\;\;\;1 \cdot \left(\left(\left(x.re\_m + x.im\right) \cdot \left(x.re\_m - x.im\right)\right) \cdot x.re\_m\right)\\ \end{array} \end{array} \]

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 6.5e+64)
    (fma
     (+ x.im x.re_m)
     (* (- x.re_m x.im) x.re_m)
     (* (- (* x.im (+ x.re_m x.re_m))) x.im))
    (* 1.0 (* (* (+ x.re_m x.im) (- x.re_m x.im)) 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 <= 6.5e+64) {
		tmp = fma((x_46_im + x_46_re_m), ((x_46_re_m - x_46_im) * x_46_re_m), (-(x_46_im * (x_46_re_m + x_46_re_m)) * x_46_im));
	} else {
		tmp = 1.0 * (((x_46_re_m + x_46_im) * (x_46_re_m - x_46_im)) * 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 <= 6.5e+64)
		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(-Float64(x_46_im * Float64(x_46_re_m + x_46_re_m))) * x_46_im));
	else
		tmp = Float64(1.0 * Float64(Float64(Float64(x_46_re_m + x_46_im) * Float64(x_46_re_m - x_46_im)) * 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, 6.5e+64], 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[(x$46$im * N[(x$46$re$95$m + x$46$re$95$m), $MachinePrecision]), $MachinePrecision]) * x$46$im), $MachinePrecision]), $MachinePrecision], N[(1.0 * N[(N[(N[(x$46$re$95$m + x$46$im), $MachinePrecision] * N[(x$46$re$95$m - x$46$im), $MachinePrecision]), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x.re < 6.50000000000000007e64

   1. Initial program 82.4%

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

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

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

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

      4. lift-*.f64 N/A

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

      5. lower-fma.f64 N/A

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

      6. lift--.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. difference-of-squares N/A

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

      10. lower-*.f64 N/A

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

      11. lower-+.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lower-*.f64 N/A

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

   3. Applied rewrites 85.5%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lower-+.f64 N/A

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

      8. lower-*.f64 91.4

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

      9. lift-fma.f64 N/A

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

      10. lift-*.f64 N/A

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

      11. distribute-lft-out N/A

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

      12. lower-*.f64 N/A

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

      13. lower-+.f64 91.3

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

   5. Applied rewrites 91.3%

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

   if 6.50000000000000007e64 < x.re

   1. Initial program 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around inf

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

   6. Applied rewrites 78.5%

      \[\leadsto \color{blue}{1} \cdot \left(\left(\left(x.re + x.im\right) \cdot \left(x.re - x.im\right)\right) \cdot x.re\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 98.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ \begin{array}{l} t_0 := \left(x.re\_m - x.im\right) \cdot x.re\_m\\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;\left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.re\_m - \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.im \leq -2 \cdot 10^{-288}:\\ \;\;\;\;\mathsf{fma}\left(x.im, t\_0, \left(-x.im \cdot \left(x.re\_m + x.re\_m\right)\right) \cdot x.im\right)\\ \mathbf{else}:\\ \;\;\;\;\left(t\_0 \cdot \left(x.re\_m + x.im\right)\right) \cdot 1\\ \end{array} \end{array} \end{array} \]

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.im) x.re_m)))
   (*
    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-288)
      (fma x.im t_0 (* (- (* x.im (+ x.re_m x.re_m))) x.im))
      (* (* t_0 (+ x.re_m x.im)) 1.0)))))

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_im) * x_46_re_m;
	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-288) {
		tmp = fma(x_46_im, t_0, (-(x_46_im * (x_46_re_m + x_46_re_m)) * x_46_im));
	} else {
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0;
	}
	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_im) * x_46_re_m)
	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-288)
		tmp = fma(x_46_im, t_0, Float64(Float64(-Float64(x_46_im * Float64(x_46_re_m + x_46_re_m))) * x_46_im));
	else
		tmp = Float64(Float64(t_0 * Float64(x_46_re_m + x_46_im)) * 1.0);
	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$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]}, 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-288], N[(x$46$im * t$95$0 + N[((-N[(x$46$im * N[(x$46$re$95$m + x$46$re$95$m), $MachinePrecision]), $MachinePrecision]) * x$46$im), $MachinePrecision]), $MachinePrecision], N[(N[(t$95$0 * N[(x$46$re$95$m + x$46$im), $MachinePrecision]), $MachinePrecision] * 1.0), $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)) < -2.00000000000000012e-288

   1. Initial program 82.4%

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

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

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

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

      4. lift-*.f64 N/A

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

      5. lower-fma.f64 N/A

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

      6. lift--.f64 N/A

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

      7. lift-*.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. difference-of-squares N/A

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

      10. lower-*.f64 N/A

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

      11. lower-+.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lower-*.f64 N/A

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

   3. Applied rewrites 85.5%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. lower-fma.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lower-+.f64 N/A

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

      8. lower-*.f64 91.4

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

      9. lift-fma.f64 N/A

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

      10. lift-*.f64 N/A

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

      11. distribute-lft-out N/A

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

      12. lower-*.f64 N/A

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

      13. lower-+.f64 91.3

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

   5. Applied rewrites 91.3%

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

   6. Taylor expanded in x.re around 0

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

   8. Applied rewrites 62.5%

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

   if -2.00000000000000012e-288 < (-.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 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around inf

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

   6. Applied rewrites 78.5%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 78.5

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. associate-*l* N/A

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

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 79.2

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

   8. Applied rewrites 79.2%

      \[\leadsto \color{blue}{\left(\left(\left(x.re - x.im\right) \cdot x.re\right) \cdot \left(x.re + x.im\right)\right) \cdot 1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 98.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ \begin{array}{l} t_0 := \left(x.re\_m - x.im\right) \cdot x.re\_m\\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;\left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.re\_m - \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.im \leq -2 \cdot 10^{-288}:\\ \;\;\;\;\left(x.re\_m + x.im\right) \cdot \left(t\_0 \cdot 3\right)\\ \mathbf{else}:\\ \;\;\;\;\left(t\_0 \cdot \left(x.re\_m + x.im\right)\right) \cdot 1\\ \end{array} \end{array} \end{array} \]

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.im) x.re_m)))
   (*
    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-288)
      (* (+ x.re_m x.im) (* t_0 3.0))
      (* (* t_0 (+ x.re_m x.im)) 1.0)))))

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_im) * x_46_re_m;
	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-288) {
		tmp = (x_46_re_m + x_46_im) * (t_0 * 3.0);
	} else {
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0;
	}
	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) :: t_0
    real(8) :: tmp
    t_0 = (x_46re_m - x_46im) * x_46re_m
    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-288)) then
        tmp = (x_46re_m + x_46im) * (t_0 * 3.0d0)
    else
        tmp = (t_0 * (x_46re_m + x_46im)) * 1.0d0
    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 t_0 = (x_46_re_m - x_46_im) * x_46_re_m;
	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-288) {
		tmp = (x_46_re_m + x_46_im) * (t_0 * 3.0);
	} else {
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0;
	}
	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):
	t_0 = (x_46_re_m - x_46_im) * x_46_re_m
	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-288:
		tmp = (x_46_re_m + x_46_im) * (t_0 * 3.0)
	else:
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0
	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_im) * x_46_re_m)
	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-288)
		tmp = Float64(Float64(x_46_re_m + x_46_im) * Float64(t_0 * 3.0));
	else
		tmp = Float64(Float64(t_0 * Float64(x_46_re_m + x_46_im)) * 1.0);
	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)
	t_0 = (x_46_re_m - x_46_im) * x_46_re_m;
	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-288)
		tmp = (x_46_re_m + x_46_im) * (t_0 * 3.0);
	else
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0;
	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_] := Block[{t$95$0 = N[(N[(x$46$re$95$m - x$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]}, 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-288], N[(N[(x$46$re$95$m + x$46$im), $MachinePrecision] * N[(t$95$0 * 3.0), $MachinePrecision]), $MachinePrecision], N[(N[(t$95$0 * N[(x$46$re$95$m + x$46$im), $MachinePrecision]), $MachinePrecision] * 1.0), $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)) < -2.00000000000000012e-288

   1. Initial program 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around 0

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

   6. Applied rewrites 79.3%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 79.3

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. associate-*l* N/A

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

      7. lift-*.f64 N/A

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

      8. lower-*.f64 85.2

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

   8. Applied rewrites 85.2%

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

      1. lift-*.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-*l* N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 85.2

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

   10. Applied rewrites 85.2%

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

   if -2.00000000000000012e-288 < (-.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 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around inf

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

   6. Applied rewrites 78.5%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 78.5

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. associate-*l* N/A

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

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 79.2

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

   8. Applied rewrites 79.2%

      \[\leadsto \color{blue}{\left(\left(\left(x.re - x.im\right) \cdot x.re\right) \cdot \left(x.re + x.im\right)\right) \cdot 1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 98.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ \begin{array}{l} t_0 := \left(x.re\_m - x.im\right) \cdot x.re\_m\\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;\left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.re\_m - \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.im \leq -2 \cdot 10^{-288}:\\ \;\;\;\;\left(x.im \cdot t\_0\right) \cdot 3\\ \mathbf{else}:\\ \;\;\;\;\left(t\_0 \cdot \left(x.re\_m + x.im\right)\right) \cdot 1\\ \end{array} \end{array} \end{array} \]

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.im) x.re_m)))
   (*
    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-288)
      (* (* x.im t_0) 3.0)
      (* (* t_0 (+ x.re_m x.im)) 1.0)))))

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_im) * x_46_re_m;
	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-288) {
		tmp = (x_46_im * t_0) * 3.0;
	} else {
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0;
	}
	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) :: t_0
    real(8) :: tmp
    t_0 = (x_46re_m - x_46im) * x_46re_m
    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-288)) then
        tmp = (x_46im * t_0) * 3.0d0
    else
        tmp = (t_0 * (x_46re_m + x_46im)) * 1.0d0
    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 t_0 = (x_46_re_m - x_46_im) * x_46_re_m;
	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-288) {
		tmp = (x_46_im * t_0) * 3.0;
	} else {
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0;
	}
	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):
	t_0 = (x_46_re_m - x_46_im) * x_46_re_m
	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-288:
		tmp = (x_46_im * t_0) * 3.0
	else:
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0
	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_im) * x_46_re_m)
	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-288)
		tmp = Float64(Float64(x_46_im * t_0) * 3.0);
	else
		tmp = Float64(Float64(t_0 * Float64(x_46_re_m + x_46_im)) * 1.0);
	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)
	t_0 = (x_46_re_m - x_46_im) * x_46_re_m;
	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-288)
		tmp = (x_46_im * t_0) * 3.0;
	else
		tmp = (t_0 * (x_46_re_m + x_46_im)) * 1.0;
	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_] := Block[{t$95$0 = N[(N[(x$46$re$95$m - x$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]}, 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-288], N[(N[(x$46$im * t$95$0), $MachinePrecision] * 3.0), $MachinePrecision], N[(N[(t$95$0 * N[(x$46$re$95$m + x$46$im), $MachinePrecision]), $MachinePrecision] * 1.0), $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)) < -2.00000000000000012e-288

   1. Initial program 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around 0

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

   6. Applied rewrites 79.3%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 79.3

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. associate-*l* N/A

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

      7. lift-*.f64 N/A

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

      8. lower-*.f64 85.2

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

   8. Applied rewrites 85.2%

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

   9. Taylor expanded in x.re around 0

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

   11. Applied rewrites 66.6%

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

   if -2.00000000000000012e-288 < (-.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 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around inf

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

   6. Applied rewrites 78.5%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 78.5

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. associate-*l* N/A

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

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-*.f64 79.2

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

   8. Applied rewrites 79.2%

      \[\leadsto \color{blue}{\left(\left(\left(x.re - x.im\right) \cdot x.re\right) \cdot \left(x.re + x.im\right)\right) \cdot 1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 98.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;\left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.re\_m - \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.im \leq -2 \cdot 10^{-288}:\\ \;\;\;\;\left(x.im \cdot \left(\left(x.re\_m - x.im\right) \cdot x.re\_m\right)\right) \cdot 3\\ \mathbf{else}:\\ \;\;\;\;1 \cdot \left(\left(\left(x.re\_m + x.im\right) \cdot \left(x.re\_m - x.im\right)\right) \cdot x.re\_m\right)\\ \end{array} \end{array} \]

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-288)
    (* (* x.im (* (- x.re_m x.im) x.re_m)) 3.0)
    (* 1.0 (* (* (+ x.re_m x.im) (- x.re_m x.im)) 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-288) {
		tmp = (x_46_im * ((x_46_re_m - x_46_im) * x_46_re_m)) * 3.0;
	} else {
		tmp = 1.0 * (((x_46_re_m + x_46_im) * (x_46_re_m - x_46_im)) * 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-288)) then
        tmp = (x_46im * ((x_46re_m - x_46im) * x_46re_m)) * 3.0d0
    else
        tmp = 1.0d0 * (((x_46re_m + x_46im) * (x_46re_m - x_46im)) * 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-288) {
		tmp = (x_46_im * ((x_46_re_m - x_46_im) * x_46_re_m)) * 3.0;
	} else {
		tmp = 1.0 * (((x_46_re_m + x_46_im) * (x_46_re_m - x_46_im)) * 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-288:
		tmp = (x_46_im * ((x_46_re_m - x_46_im) * x_46_re_m)) * 3.0
	else:
		tmp = 1.0 * (((x_46_re_m + x_46_im) * (x_46_re_m - x_46_im)) * 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-288)
		tmp = Float64(Float64(x_46_im * Float64(Float64(x_46_re_m - x_46_im) * x_46_re_m)) * 3.0);
	else
		tmp = Float64(1.0 * Float64(Float64(Float64(x_46_re_m + x_46_im) * Float64(x_46_re_m - x_46_im)) * 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-288)
		tmp = (x_46_im * ((x_46_re_m - x_46_im) * x_46_re_m)) * 3.0;
	else
		tmp = 1.0 * (((x_46_re_m + x_46_im) * (x_46_re_m - x_46_im)) * 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-288], N[(N[(x$46$im * N[(N[(x$46$re$95$m - x$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $MachinePrecision] * 3.0), $MachinePrecision], N[(1.0 * N[(N[(N[(x$46$re$95$m + x$46$im), $MachinePrecision] * N[(x$46$re$95$m - x$46$im), $MachinePrecision]), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $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)) < -2.00000000000000012e-288

   1. Initial program 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around 0

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

   6. Applied rewrites 79.3%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 79.3

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. associate-*l* N/A

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

      7. lift-*.f64 N/A

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

      8. lower-*.f64 85.2

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

   8. Applied rewrites 85.2%

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

   9. Taylor expanded in x.re around 0

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

   11. Applied rewrites 66.6%

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

   if -2.00000000000000012e-288 < (-.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 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around inf

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

   6. Applied rewrites 78.5%

      \[\leadsto \color{blue}{1} \cdot \left(\left(\left(x.re + x.im\right) \cdot \left(x.re - x.im\right)\right) \cdot x.re\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 94.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} x.re\_m = \left|x.re\right| \\ x.re\_s = \mathsf{copysign}\left(1, x.re\right) \\ \begin{array}{l} t_0 := \left(x.im \cdot \left(\left(x.re\_m - x.im\right) \cdot x.re\_m\right)\right) \cdot 3\\ t_1 := \left(x.re\_m \cdot x.re\_m - x.im \cdot x.im\right) \cdot x.re\_m - \left(x.re\_m \cdot x.im + x.im \cdot x.re\_m\right) \cdot x.im\\ x.re\_s \cdot \begin{array}{l} \mathbf{if}\;t\_1 \leq -2 \cdot 10^{-288}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;\left(x.re\_m \cdot x.re\_m\right) \cdot x.re\_m\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \end{array} \]

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.im (* (- x.re_m x.im) x.re_m)) 3.0))
        (t_1
         (-
          (* (- (* 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))))
   (*
    x.re_s
    (if (<= t_1 -2e-288)
      t_0
      (if (<= t_1 INFINITY) (* (* x.re_m x.re_m) 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_im * ((x_46_re_m - x_46_im) * x_46_re_m)) * 3.0;
	double t_1 = (((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);
	double tmp;
	if (t_1 <= -2e-288) {
		tmp = t_0;
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	} else {
		tmp = t_0;
	}
	return x_46_re_s * tmp;
}

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 t_0 = (x_46_im * ((x_46_re_m - x_46_im) * x_46_re_m)) * 3.0;
	double t_1 = (((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);
	double tmp;
	if (t_1 <= -2e-288) {
		tmp = t_0;
	} else if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	} else {
		tmp = t_0;
	}
	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):
	t_0 = (x_46_im * ((x_46_re_m - x_46_im) * x_46_re_m)) * 3.0
	t_1 = (((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)
	tmp = 0
	if t_1 <= -2e-288:
		tmp = t_0
	elif t_1 <= math.inf:
		tmp = (x_46_re_m * x_46_re_m) * 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_im * Float64(Float64(x_46_re_m - x_46_im) * x_46_re_m)) * 3.0)
	t_1 = 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))
	tmp = 0.0
	if (t_1 <= -2e-288)
		tmp = t_0;
	elseif (t_1 <= Inf)
		tmp = Float64(Float64(x_46_re_m * x_46_re_m) * x_46_re_m);
	else
		tmp = t_0;
	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)
	t_0 = (x_46_im * ((x_46_re_m - x_46_im) * x_46_re_m)) * 3.0;
	t_1 = (((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);
	tmp = 0.0;
	if (t_1 <= -2e-288)
		tmp = t_0;
	elseif (t_1 <= Inf)
		tmp = (x_46_re_m * x_46_re_m) * x_46_re_m;
	else
		tmp = t_0;
	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_] := Block[{t$95$0 = N[(N[(x$46$im * N[(N[(x$46$re$95$m - x$46$im), $MachinePrecision] * x$46$re$95$m), $MachinePrecision]), $MachinePrecision] * 3.0), $MachinePrecision]}, Block[{t$95$1 = 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]}, N[(x$46$re$95$s * If[LessEqual[t$95$1, -2e-288], t$95$0, If[LessEqual[t$95$1, Infinity], N[(N[(x$46$re$95$m * x$46$re$95$m), $MachinePrecision] * x$46$re$95$m), $MachinePrecision], t$95$0]]), $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)) < -2.00000000000000012e-288 or +inf.0 < (-.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 82.4%

      \[\left(x.re \cdot x.re - x.im \cdot x.im\right) \cdot x.re - \left(x.re \cdot x.im + x.im \cdot x.re\right) \cdot x.im \]
   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. sub-to-mult N/A

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

      3. lower-*.f64 N/A

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

   3. Applied rewrites 56.2%

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

   4. Taylor expanded in x.re around 0

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

   6. Applied rewrites 79.3%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 79.3

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

      4. lift-*.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. associate-*l* N/A

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

      7. lift-*.f64 N/A

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

      8. lower-*.f64 85.2

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

   8. Applied rewrites 85.2%

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

   9. Taylor expanded in x.re around 0

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

   11. Applied rewrites 66.6%

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

   if -2.00000000000000012e-288 < (-.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)) < +inf.0

   1. Initial program 82.4%

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

   2. Taylor expanded in x.re around inf

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

      1. lower-pow.f64 59.5

         \[\leadsto {x.re}^{\color{blue}{3}} \]

   4. Applied rewrites 59.5%

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

      1. lift-pow.f64 N/A

         \[\leadsto {x.re}^{\color{blue}{3}} \]

      2. unpow3 N/A

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

      3. pow2 N/A

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

      4. lift-pow.f64 N/A

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

      5. lower-*.f64 59.4

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

      6. lift-pow.f64 N/A

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

      7. pow2 N/A

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

      8. lower-*.f64 59.4

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

   6. Applied rewrites 59.4%

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

Alternative 8: 59.4% 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 82.4%

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

2. Taylor expanded in x.re around inf

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

   1. lower-pow.f64 59.5

      \[\leadsto {x.re}^{\color{blue}{3}} \]

4. Applied rewrites 59.5%

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

   1. lift-pow.f64 N/A

      \[\leadsto {x.re}^{\color{blue}{3}} \]

   2. unpow3 N/A

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

   3. pow2 N/A

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

   4. lift-pow.f64 N/A

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

   5. lower-*.f64 59.4

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

   6. lift-pow.f64 N/A

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

   7. pow2 N/A

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

   8. lower-*.f64 59.4

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

6. Applied rewrites 59.4%

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

Developer Target 1: 86.9% 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 2025150 
(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)))