_multiplyComplex, imaginary part

Percentage Accurate: 99.2% → 99.7%

Time: 1.7s

Alternatives: 4

Speedup: 1.0×

• 23.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x.re \cdot y.im + x.im \cdot y.re \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (+ (* x.re y.im) (* x.im 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_im) + (x_46_im * y_46_re);
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    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_46im) + (x_46im * 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_im) + (x_46_im * y_46_re);
}

Python

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

Julia

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

Wolfram

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

Initial Program: 99.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x.re \cdot y.im + x.im \cdot y.re \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (+ (* x.re y.im) (* x.im 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_im) + (x_46_im * y_46_re);
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    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_46im) + (x_46im * 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_im) + (x_46_im * y_46_re);
}

Python

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

Julia

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

Wolfram

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

Alternative 1: 99.7% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x.re, y.im, x.im \cdot y.re\right) \end{array} \]

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.6%

   \[x.re \cdot y.im + x.im \cdot y.re \]
2. Step-by-step derivation

   1. fma-def 100.0%

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

3. Simplified 100.0%

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

4. Final simplification 100.0%

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

Alternative 2: 73.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x.re \cdot y.im \leq -4 \cdot 10^{+212}:\\ \;\;\;\;x.re \cdot y.im\\ \mathbf{elif}\;x.re \cdot y.im \leq -1.32 \cdot 10^{+151} \lor \neg \left(x.re \cdot y.im \leq -3.3 \cdot 10^{+69}\right) \land x.re \cdot y.im \leq 1.9 \cdot 10^{-66}:\\ \;\;\;\;x.im \cdot y.re\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot y.im\\ \end{array} \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (if (<= (* x.re y.im) -4e+212)
   (* x.re y.im)
   (if (or (<= (* x.re y.im) -1.32e+151)
           (and (not (<= (* x.re y.im) -3.3e+69)) (<= (* x.re y.im) 1.9e-66)))
     (* x.im y.re)
     (* x.re y.im))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double tmp;
	if ((x_46_re * y_46_im) <= -4e+212) {
		tmp = x_46_re * y_46_im;
	} else if (((x_46_re * y_46_im) <= -1.32e+151) || (!((x_46_re * y_46_im) <= -3.3e+69) && ((x_46_re * y_46_im) <= 1.9e-66))) {
		tmp = x_46_im * y_46_re;
	} else {
		tmp = x_46_re * y_46_im;
	}
	return tmp;
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    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) :: tmp
    if ((x_46re * y_46im) <= (-4d+212)) then
        tmp = x_46re * y_46im
    else if (((x_46re * y_46im) <= (-1.32d+151)) .or. (.not. ((x_46re * y_46im) <= (-3.3d+69))) .and. ((x_46re * y_46im) <= 1.9d-66)) then
        tmp = x_46im * y_46re
    else
        tmp = x_46re * y_46im
    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 tmp;
	if ((x_46_re * y_46_im) <= -4e+212) {
		tmp = x_46_re * y_46_im;
	} else if (((x_46_re * y_46_im) <= -1.32e+151) || (!((x_46_re * y_46_im) <= -3.3e+69) && ((x_46_re * y_46_im) <= 1.9e-66))) {
		tmp = x_46_im * y_46_re;
	} else {
		tmp = x_46_re * y_46_im;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	tmp = 0
	if (x_46_re * y_46_im) <= -4e+212:
		tmp = x_46_re * y_46_im
	elif ((x_46_re * y_46_im) <= -1.32e+151) or (not ((x_46_re * y_46_im) <= -3.3e+69) and ((x_46_re * y_46_im) <= 1.9e-66)):
		tmp = x_46_im * y_46_re
	else:
		tmp = x_46_re * y_46_im
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = 0.0
	if (Float64(x_46_re * y_46_im) <= -4e+212)
		tmp = Float64(x_46_re * y_46_im);
	elseif ((Float64(x_46_re * y_46_im) <= -1.32e+151) || (!(Float64(x_46_re * y_46_im) <= -3.3e+69) && (Float64(x_46_re * y_46_im) <= 1.9e-66)))
		tmp = Float64(x_46_im * y_46_re);
	else
		tmp = Float64(x_46_re * y_46_im);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = 0.0;
	if ((x_46_re * y_46_im) <= -4e+212)
		tmp = x_46_re * y_46_im;
	elseif (((x_46_re * y_46_im) <= -1.32e+151) || (~(((x_46_re * y_46_im) <= -3.3e+69)) && ((x_46_re * y_46_im) <= 1.9e-66)))
		tmp = x_46_im * y_46_re;
	else
		tmp = x_46_re * y_46_im;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := If[LessEqual[N[(x$46$re * y$46$im), $MachinePrecision], -4e+212], N[(x$46$re * y$46$im), $MachinePrecision], If[Or[LessEqual[N[(x$46$re * y$46$im), $MachinePrecision], -1.32e+151], And[N[Not[LessEqual[N[(x$46$re * y$46$im), $MachinePrecision], -3.3e+69]], $MachinePrecision], LessEqual[N[(x$46$re * y$46$im), $MachinePrecision], 1.9e-66]]], N[(x$46$im * y$46$re), $MachinePrecision], N[(x$46$re * y$46$im), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x.re y.im) < -3.9999999999999996e212 or -1.31999999999999992e151 < (*.f64 x.re y.im) < -3.2999999999999999e69 or 1.8999999999999999e-66 < (*.f64 x.re y.im)

   1. Initial program 99.1%

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

   2. Taylor expanded in x.re around inf 80.5%

      \[\leadsto \color{blue}{x.re \cdot y.im} \]

   if -3.9999999999999996e212 < (*.f64 x.re y.im) < -1.31999999999999992e151 or -3.2999999999999999e69 < (*.f64 x.re y.im) < 1.8999999999999999e-66

   1. Initial program 100.0%

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

   2. Taylor expanded in x.re around 0 79.2%

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

4. Final simplification 79.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x.re \cdot y.im \leq -4 \cdot 10^{+212}:\\ \;\;\;\;x.re \cdot y.im\\ \mathbf{elif}\;x.re \cdot y.im \leq -1.32 \cdot 10^{+151} \lor \neg \left(x.re \cdot y.im \leq -3.3 \cdot 10^{+69}\right) \land x.re \cdot y.im \leq 1.9 \cdot 10^{-66}:\\ \;\;\;\;x.im \cdot y.re\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot y.im\\ \end{array} \]

Alternative 3: 99.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x.re \cdot y.im + x.im \cdot y.re \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (+ (* x.re y.im) (* x.im 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_im) + (x_46_im * y_46_re);
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    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_46im) + (x_46im * 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_im) + (x_46_im * y_46_re);
}

Python

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.6%

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

2. Final simplification 99.6%

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

Alternative 4: 51.7% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ x.re \cdot y.im \end{array} \]

FPCore

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

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    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_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_im;
}

Python

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.6%

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

2. Taylor expanded in x.re around inf 49.0%

   \[\leadsto \color{blue}{x.re \cdot y.im} \]

3. Final simplification 49.0%

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

Reproduce

herbie shell --seed 2023228 
(FPCore (x.re x.im y.re y.im)
  :name "_multiplyComplex, imaginary part"
  :precision binary64
  (+ (* x.re y.im) (* x.im y.re)))