_multiplyComplex, real part

?

Percentage Accurate: 99.1% → 99.6%
Time: 4.7s
Precision: binary64
Cost: 6784

?

\[x.re \cdot y.re - x.im \cdot y.im \]
↓
\[\mathsf{fma}\left(-y.im, x.im, x.re \cdot y.re\right) \]
(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (- (* x.re y.re) (* x.im y.im)))
↓
(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (fma (- y.im) x.im (* x.re y.re)))
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);
}
↓
double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return fma(-y_46_im, x_46_im, (x_46_re * y_46_re));
}

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

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

x.re \cdot y.re - x.im \cdot y.im
↓
\mathsf{fma}\left(-y.im, x.im, x.re \cdot y.re\right)

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Herbie found 4 alternatives:

AlternativeAccuracySpeedup

Accuracy vs Speed

The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Bogosity?

Bogosity

Derivation?

  1. Initial program 100.0%

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

  2. Applied egg-rr100.0%

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

    [Start]100.0%

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

    sub-neg [=>]100.0%

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

    +-commutative [=>]100.0%

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

    *-commutative [=>]100.0%

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

    distribute-lft-neg-in [=>]100.0%

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

    fma-def [=>]100.0%

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

  3. Final simplification100.0%

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

Alternatives

Alternative 1
Accuracy99.6%
Cost6784
\[\mathsf{fma}\left(-y.im, x.im, x.re \cdot y.re\right) \]
Alternative 2
Accuracy76.4%
Cost1297
\[\begin{array}{l} \mathbf{if}\;x.re \cdot y.re \leq -9.5 \cdot 10^{+59}:\\ \;\;\;\;x.re \cdot y.re\\ \mathbf{elif}\;x.re \cdot y.re \leq -6.8 \cdot 10^{+17} \lor \neg \left(x.re \cdot y.re \leq -0.0064\right) \land x.re \cdot y.re \leq 1.6 \cdot 10^{-15}:\\ \;\;\;\;y.im \cdot \left(-x.im\right)\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot y.re\\ \end{array} \]
Alternative 3
Accuracy99.1%
Cost448
\[x.re \cdot y.re - y.im \cdot x.im \]
Alternative 4
Accuracy52.1%
Cost192
\[x.re \cdot y.re \]

Reproduce?

herbie shell --seed 2023277 
(FPCore (x.re x.im y.re y.im)
  :name "_multiplyComplex, real part"
  :precision binary64
  (- (* x.re y.re) (* x.im y.im)))