_multiplyComplex, real part

Percentage Accurate: 99.4% → 99.4%

Time: 1.2s

Alternatives: 3

Speedup: 1.0×

• 77.0% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[x.re \cdot y.re - x.im \cdot y.im \]

FPCore

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

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (x_46_re * y_46_re) - (x_46_im * y_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, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = (x_46re * y_46re) - (x_46im * y_46im)
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (x_46_re * y_46_re) - (x_46_im * y_46_im);
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return (x_46_re * y_46_re) - (x_46_im * y_46_im)

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(Float64(x_46_re * y_46_re) - Float64(x_46_im * y_46_im))
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = (x_46_re * y_46_re) - (x_46_im * y_46_im);
end

Wolfram

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

Initial Program: 99.4% accurate, 1.0× speedup

Math

\[x.re \cdot y.re - x.im \cdot y.im \]

FPCore

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

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (x_46_re * y_46_re) - (x_46_im * y_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, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = (x_46re * y_46re) - (x_46im * y_46im)
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (x_46_re * y_46_re) - (x_46_im * y_46_im);
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return (x_46_re * y_46_re) - (x_46_im * y_46_im)

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(Float64(x_46_re * y_46_re) - Float64(x_46_im * y_46_im))
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = (x_46_re * y_46_re) - (x_46_im * y_46_im);
end

Wolfram

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

Alternative 1: 75.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} t_0 := \left(-y.im\right) \cdot x.im\\ \mathbf{if}\;x.im \cdot y.im \leq \frac{-6805647338418769}{680564733841876926926749214863536422912}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x.im \cdot y.im \leq 99999999999999997168788049560464200849936328366177157906432:\\ \;\;\;\;x.re \cdot y.re\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (let* ((t_0 (* (- y.im) x.im)))
  (if (<=
       (* x.im y.im)
       -6805647338418769/680564733841876926926749214863536422912)
    t_0
    (if (<=
         (* x.im y.im)
         99999999999999997168788049560464200849936328366177157906432)
      (* x.re y.re)
      t_0))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = -y_46_im * x_46_im;
	double tmp;
	if ((x_46_im * y_46_im) <= -1e-23) {
		tmp = t_0;
	} else if ((x_46_im * y_46_im) <= 1e+59) {
		tmp = x_46_re * y_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

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, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = -y_46im * x_46im
    if ((x_46im * y_46im) <= (-1d-23)) then
        tmp = t_0
    else if ((x_46im * y_46im) <= 1d+59) then
        tmp = x_46re * y_46re
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = -y_46_im * x_46_im;
	double tmp;
	if ((x_46_im * y_46_im) <= -1e-23) {
		tmp = t_0;
	} else if ((x_46_im * y_46_im) <= 1e+59) {
		tmp = x_46_re * y_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = -y_46_im * x_46_im
	tmp = 0
	if (x_46_im * y_46_im) <= -1e-23:
		tmp = t_0
	elif (x_46_im * y_46_im) <= 1e+59:
		tmp = x_46_re * y_46_re
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(Float64(-y_46_im) * x_46_im)
	tmp = 0.0
	if (Float64(x_46_im * y_46_im) <= -1e-23)
		tmp = t_0;
	elseif (Float64(x_46_im * y_46_im) <= 1e+59)
		tmp = Float64(x_46_re * y_46_re);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = -y_46_im * x_46_im;
	tmp = 0.0;
	if ((x_46_im * y_46_im) <= -1e-23)
		tmp = t_0;
	elseif ((x_46_im * y_46_im) <= 1e+59)
		tmp = x_46_re * y_46_re;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[((-y$46$im) * x$46$im), $MachinePrecision]}, If[LessEqual[N[(x$46$im * y$46$im), $MachinePrecision], -6805647338418769/680564733841876926926749214863536422912], t$95$0, If[LessEqual[N[(x$46$im * y$46$im), $MachinePrecision], 99999999999999997168788049560464200849936328366177157906432], N[(x$46$re * y$46$re), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x.im y.im) < -9.9999999999999996e-24 or 9.9999999999999997e58 < (*.f64 x.im y.im)

   1. Initial program 99.4%

      \[x.re \cdot y.re - x.im \cdot y.im \]

   2. Taylor expanded in x.re around 0

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 51.7%

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

   4. Applied rewrites 51.7%

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

      1. lift-*.f64 N/A

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

      2. mul-1-neg N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

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

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

      6. lower-*.f64 N/A

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

      7. lower-neg.f64 51.7%

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

   6. Applied rewrites 51.7%

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

   if -9.9999999999999996e-24 < (*.f64 x.im y.im) < 9.9999999999999997e58

   1. Initial program 99.4%

      \[x.re \cdot y.re - x.im \cdot y.im \]

   2. Taylor expanded in y.re around inf

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

      1. lower-*.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lower-*.f64 91.7%

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

   4. Applied rewrites 91.7%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 91.7%

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

      4. lift-+.f64 N/A

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

      5. add-flip N/A

         \[\leadsto \left(x.re - \left(\mathsf{neg}\left(-1 \cdot \frac{x.im \cdot y.im}{y.re}\right)\right)\right) \cdot y.re \]

      6. lower--.f64 N/A

         \[\leadsto \left(x.re - \left(\mathsf{neg}\left(-1 \cdot \frac{x.im \cdot y.im}{y.re}\right)\right)\right) \cdot y.re \]

      7. lift-*.f64 N/A

         \[\leadsto \left(x.re - \left(\mathsf{neg}\left(-1 \cdot \frac{x.im \cdot y.im}{y.re}\right)\right)\right) \cdot y.re \]

      8. lift-/.f64 N/A

         \[\leadsto \left(x.re - \left(\mathsf{neg}\left(-1 \cdot \frac{x.im \cdot y.im}{y.re}\right)\right)\right) \cdot y.re \]

      9. associate-*r/ N/A

         \[\leadsto \left(x.re - \left(\mathsf{neg}\left(\frac{-1 \cdot \left(x.im \cdot y.im\right)}{y.re}\right)\right)\right) \cdot y.re \]

      10. mul-1-neg N/A

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

      11. distribute-neg-frac2 N/A

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

      12. frac-2neg N/A

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

      13. mult-flip N/A

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

      14. lift-*.f64 N/A

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

      15. *-commutative N/A

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

      16. associate-*l* N/A

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

      17. *-commutative N/A

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

      18. lower-*.f64 N/A

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

      19. mult-flip-rev N/A

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

      20. lower-/.f64 87.6%

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

   6. Applied rewrites 87.6%

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

   7. Taylor expanded in x.re around inf

      \[\leadsto x.re \cdot \color{blue}{y.re} \]
   8. Step-by-step derivation

      1. lower-*.f64 52.5%

         \[\leadsto x.re \cdot y.re \]

   9. Applied rewrites 52.5%

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

Alternative 2: 52.5% accurate, 2.3× speedup

Math

\[x.re \cdot y.re \]

FPCore

(FPCore (x.re x.im y.re y.im)
  :precision binary64
  (* x.re y.re))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return x_46_re * y_46_re;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x_46re, x_46im, y_46re, y_46im)
use fmin_fmax_functions
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = x_46re * y_46re
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return x_46_re * y_46_re;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return x_46_re * y_46_re

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(x_46_re * y_46_re)
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = x_46_re * y_46_re;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(x$46$re * y$46$re), $MachinePrecision]

Derivation

1. Initial program 99.4%

   \[x.re \cdot y.re - x.im \cdot y.im \]

2. Taylor expanded in y.re around inf

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

   1. lower-*.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-*.f64 N/A

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

   4. lower-/.f64 N/A

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

   5. lower-*.f64 91.7%

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

4. Applied rewrites 91.7%

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

   1. lift-*.f64 N/A

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

   2. *-commutative N/A

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

   3. lower-*.f64 91.7%

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

   4. lift-+.f64 N/A

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

   5. add-flip N/A

      \[\leadsto \left(x.re - \left(\mathsf{neg}\left(-1 \cdot \frac{x.im \cdot y.im}{y.re}\right)\right)\right) \cdot y.re \]

   6. lower--.f64 N/A

      \[\leadsto \left(x.re - \left(\mathsf{neg}\left(-1 \cdot \frac{x.im \cdot y.im}{y.re}\right)\right)\right) \cdot y.re \]

   7. lift-*.f64 N/A

      \[\leadsto \left(x.re - \left(\mathsf{neg}\left(-1 \cdot \frac{x.im \cdot y.im}{y.re}\right)\right)\right) \cdot y.re \]

   8. lift-/.f64 N/A

      \[\leadsto \left(x.re - \left(\mathsf{neg}\left(-1 \cdot \frac{x.im \cdot y.im}{y.re}\right)\right)\right) \cdot y.re \]

   9. associate-*r/ N/A

      \[\leadsto \left(x.re - \left(\mathsf{neg}\left(\frac{-1 \cdot \left(x.im \cdot y.im\right)}{y.re}\right)\right)\right) \cdot y.re \]

   10. mul-1-neg N/A

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

   11. distribute-neg-frac2 N/A

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

   12. frac-2neg N/A

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

   13. mult-flip N/A

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

   14. lift-*.f64 N/A

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

   15. *-commutative N/A

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

   16. associate-*l* N/A

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

   17. *-commutative N/A

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

   18. lower-*.f64 N/A

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

   19. mult-flip-rev N/A

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

   20. lower-/.f64 87.6%

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

6. Applied rewrites 87.6%

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

7. Taylor expanded in x.re around inf

   \[\leadsto x.re \cdot \color{blue}{y.re} \]
8. Step-by-step derivation

   1. lower-*.f64 52.5%

      \[\leadsto x.re \cdot y.re \]

9. Applied rewrites 52.5%

   \[\leadsto x.re \cdot \color{blue}{y.re} \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2025285 -o generate:evaluate
(FPCore (x.re x.im y.re y.im)
  :name "_multiplyComplex, real part"
  :precision binary64
  (- (* x.re y.re) (* x.im y.im)))