_multiplyComplex, imaginary part

Percentage Accurate: 99.2% → 99.6%

Time: 2.8s

Alternatives: 5

Speedup: 1.0×

• 24.1% 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: 99.6% accurate, 0.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.2%

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

   1. +-commutative 99.2%

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

   2. *-commutative 99.2%

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

   3. fma-def 99.6%

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

3. Applied egg-rr 99.6%

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

4. Final simplification 99.6%

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

Alternative 2: 99.6% accurate, 0.1× speedup

Math

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

FPCore

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

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, (y_46_re * x_46_im));
}

Julia

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

Wolfram

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

Derivation

1. Initial program 99.2%

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

   1. fma-def 99.6%

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

3. Simplified 99.6%

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

4. Final simplification 99.6%

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

Alternative 3: 72.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y.re \cdot x.im \leq -3.6 \cdot 10^{-43}:\\ \;\;\;\;y.re \cdot x.im\\ \mathbf{elif}\;y.re \cdot x.im \leq 7.2 \cdot 10^{-16} \lor \neg \left(y.re \cdot x.im \leq 3.5 \cdot 10^{+108}\right) \land y.re \cdot x.im \leq 3.85 \cdot 10^{+242}:\\ \;\;\;\;x.re \cdot y.im\\ \mathbf{else}:\\ \;\;\;\;y.re \cdot x.im\\ \end{array} \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (if (<= (* y.re x.im) -3.6e-43)
   (* y.re x.im)
   (if (or (<= (* y.re x.im) 7.2e-16)
           (and (not (<= (* y.re x.im) 3.5e+108))
                (<= (* y.re x.im) 3.85e+242)))
     (* x.re y.im)
     (* y.re x.im))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double tmp;
	if ((y_46_re * x_46_im) <= -3.6e-43) {
		tmp = y_46_re * x_46_im;
	} else if (((y_46_re * x_46_im) <= 7.2e-16) || (!((y_46_re * x_46_im) <= 3.5e+108) && ((y_46_re * x_46_im) <= 3.85e+242))) {
		tmp = x_46_re * y_46_im;
	} else {
		tmp = y_46_re * x_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 ((y_46re * x_46im) <= (-3.6d-43)) then
        tmp = y_46re * x_46im
    else if (((y_46re * x_46im) <= 7.2d-16) .or. (.not. ((y_46re * x_46im) <= 3.5d+108)) .and. ((y_46re * x_46im) <= 3.85d+242)) then
        tmp = x_46re * y_46im
    else
        tmp = y_46re * x_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 ((y_46_re * x_46_im) <= -3.6e-43) {
		tmp = y_46_re * x_46_im;
	} else if (((y_46_re * x_46_im) <= 7.2e-16) || (!((y_46_re * x_46_im) <= 3.5e+108) && ((y_46_re * x_46_im) <= 3.85e+242))) {
		tmp = x_46_re * y_46_im;
	} else {
		tmp = y_46_re * x_46_im;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	tmp = 0
	if (y_46_re * x_46_im) <= -3.6e-43:
		tmp = y_46_re * x_46_im
	elif ((y_46_re * x_46_im) <= 7.2e-16) or (not ((y_46_re * x_46_im) <= 3.5e+108) and ((y_46_re * x_46_im) <= 3.85e+242)):
		tmp = x_46_re * y_46_im
	else:
		tmp = y_46_re * x_46_im
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = 0.0
	if (Float64(y_46_re * x_46_im) <= -3.6e-43)
		tmp = Float64(y_46_re * x_46_im);
	elseif ((Float64(y_46_re * x_46_im) <= 7.2e-16) || (!(Float64(y_46_re * x_46_im) <= 3.5e+108) && (Float64(y_46_re * x_46_im) <= 3.85e+242)))
		tmp = Float64(x_46_re * y_46_im);
	else
		tmp = Float64(y_46_re * x_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 ((y_46_re * x_46_im) <= -3.6e-43)
		tmp = y_46_re * x_46_im;
	elseif (((y_46_re * x_46_im) <= 7.2e-16) || (~(((y_46_re * x_46_im) <= 3.5e+108)) && ((y_46_re * x_46_im) <= 3.85e+242)))
		tmp = x_46_re * y_46_im;
	else
		tmp = y_46_re * x_46_im;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := If[LessEqual[N[(y$46$re * x$46$im), $MachinePrecision], -3.6e-43], N[(y$46$re * x$46$im), $MachinePrecision], If[Or[LessEqual[N[(y$46$re * x$46$im), $MachinePrecision], 7.2e-16], And[N[Not[LessEqual[N[(y$46$re * x$46$im), $MachinePrecision], 3.5e+108]], $MachinePrecision], LessEqual[N[(y$46$re * x$46$im), $MachinePrecision], 3.85e+242]]], N[(x$46$re * y$46$im), $MachinePrecision], N[(y$46$re * x$46$im), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x.im y.re) < -3.5999999999999999e-43 or 7.19999999999999965e-16 < (*.f64 x.im y.re) < 3.5000000000000002e108 or 3.8499999999999999e242 < (*.f64 x.im y.re)

   1. Initial program 98.3%

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

   2. Taylor expanded in x.re around 0 82.9%

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

   if -3.5999999999999999e-43 < (*.f64 x.im y.re) < 7.19999999999999965e-16 or 3.5000000000000002e108 < (*.f64 x.im y.re) < 3.8499999999999999e242

   1. Initial program 100.0%

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

   2. Taylor expanded in x.re around inf 81.7%

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

4. Final simplification 82.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y.re \cdot x.im \leq -3.6 \cdot 10^{-43}:\\ \;\;\;\;y.re \cdot x.im\\ \mathbf{elif}\;y.re \cdot x.im \leq 7.2 \cdot 10^{-16} \lor \neg \left(y.re \cdot x.im \leq 3.5 \cdot 10^{+108}\right) \land y.re \cdot x.im \leq 3.85 \cdot 10^{+242}:\\ \;\;\;\;x.re \cdot y.im\\ \mathbf{else}:\\ \;\;\;\;y.re \cdot x.im\\ \end{array} \]

Alternative 4: 99.2% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (+ (* x.re y.im) (* y.re x.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) + (y_46_re * x_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) + (y_46re * x_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) + (y_46_re * x_46_im);
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return (x_46_re * y_46_im) + (y_46_re * x_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_im) + Float64(y_46_re * x_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) + (y_46_re * x_46_im);
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[(y$46$re * x$46$im), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.2%

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

2. Final simplification 99.2%

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

Alternative 5: 53.1% accurate, 2.3× speedup

Math

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

FPCore

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

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return y_46_re * x_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 = y_46re * x_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 y_46_re * x_46_im;
}

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.2%

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

2. Taylor expanded in x.re around 0 51.1%

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

3. Final simplification 51.1%

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

Reproduce

herbie shell --seed 2023297 
(FPCore (x.re x.im y.re y.im)
  :name "_multiplyComplex, imaginary part"
  :precision binary64
  (+ (* x.re y.im) (* x.im y.re)))