| Alternative 1 | |
|---|---|
| Error | 0.06% |
| Cost | 7104 |
\[\frac{\frac{x}{e^{wj}} + wj \cdot wj}{wj + 1}
\]
(FPCore (wj x) :precision binary64 (- wj (/ (- (* wj (exp wj)) x) (+ (exp wj) (* wj (exp wj))))))
(FPCore (wj x) :precision binary64 (/ (fma wj wj (/ x (exp wj))) (+ wj 1.0)))
double code(double wj, double x) {
return wj - (((wj * exp(wj)) - x) / (exp(wj) + (wj * exp(wj))));
}
double code(double wj, double x) {
return fma(wj, wj, (x / exp(wj))) / (wj + 1.0);
}
function code(wj, x) return Float64(wj - Float64(Float64(Float64(wj * exp(wj)) - x) / Float64(exp(wj) + Float64(wj * exp(wj))))) end
function code(wj, x) return Float64(fma(wj, wj, Float64(x / exp(wj))) / Float64(wj + 1.0)) end
code[wj_, x_] := N[(wj - N[(N[(N[(wj * N[Exp[wj], $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision] / N[(N[Exp[wj], $MachinePrecision] + N[(wj * N[Exp[wj], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
code[wj_, x_] := N[(N[(wj * wj + N[(x / N[Exp[wj], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(wj + 1.0), $MachinePrecision]), $MachinePrecision]
wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}}
\frac{\mathsf{fma}\left(wj, wj, \frac{x}{e^{wj}}\right)}{wj + 1}
| Original | 21.9% |
|---|---|
| Target | 20.98% |
| Herbie | 0.06% |
Initial program 21.9
Simplified20.98
[Start]21.9 | \[ wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}}
\] |
|---|---|
sub-neg [=>]21.9 | \[ \color{blue}{wj + \left(-\frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}}\right)}
\] |
neg-mul-1 [=>]21.9 | \[ wj + \color{blue}{-1 \cdot \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}}}
\] |
*-commutative [=>]21.9 | \[ wj + \color{blue}{\frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}} \cdot -1}
\] |
*-commutative [<=]21.9 | \[ wj + \color{blue}{-1 \cdot \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}}}
\] |
neg-mul-1 [<=]21.9 | \[ wj + \color{blue}{\left(-\frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}}\right)}
\] |
neg-sub0 [=>]21.9 | \[ wj + \color{blue}{\left(0 - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}}\right)}
\] |
div-sub [=>]21.9 | \[ wj + \left(0 - \color{blue}{\left(\frac{wj \cdot e^{wj}}{e^{wj} + wj \cdot e^{wj}} - \frac{x}{e^{wj} + wj \cdot e^{wj}}\right)}\right)
\] |
associate--r- [=>]21.9 | \[ wj + \color{blue}{\left(\left(0 - \frac{wj \cdot e^{wj}}{e^{wj} + wj \cdot e^{wj}}\right) + \frac{x}{e^{wj} + wj \cdot e^{wj}}\right)}
\] |
+-commutative [=>]21.9 | \[ wj + \color{blue}{\left(\frac{x}{e^{wj} + wj \cdot e^{wj}} + \left(0 - \frac{wj \cdot e^{wj}}{e^{wj} + wj \cdot e^{wj}}\right)\right)}
\] |
sub0-neg [=>]21.9 | \[ wj + \left(\frac{x}{e^{wj} + wj \cdot e^{wj}} + \color{blue}{\left(-\frac{wj \cdot e^{wj}}{e^{wj} + wj \cdot e^{wj}}\right)}\right)
\] |
sub-neg [<=]21.9 | \[ wj + \color{blue}{\left(\frac{x}{e^{wj} + wj \cdot e^{wj}} - \frac{wj \cdot e^{wj}}{e^{wj} + wj \cdot e^{wj}}\right)}
\] |
Applied egg-rr44.81
Simplified23.89
[Start]44.81 | \[ \left(wj + 1\right) - \left(1 - \frac{\frac{x}{e^{wj}} - wj}{wj + 1}\right)
\] |
|---|---|
associate--r- [=>]23.89 | \[ \color{blue}{\left(\left(wj + 1\right) - 1\right) + \frac{\frac{x}{e^{wj}} - wj}{wj + 1}}
\] |
Applied egg-rr22.1
Applied egg-rr20.97
Simplified0.06
[Start]20.97 | \[ \frac{\left(\frac{x}{e^{wj}} - wj\right) + \left(wj + wj \cdot wj\right)}{wj + 1}
\] |
|---|---|
sub-neg [=>]20.97 | \[ \frac{\color{blue}{\left(\frac{x}{e^{wj}} + \left(-wj\right)\right)} + \left(wj + wj \cdot wj\right)}{wj + 1}
\] |
associate-+l+ [=>]11.12 | \[ \frac{\color{blue}{\frac{x}{e^{wj}} + \left(\left(-wj\right) + \left(wj + wj \cdot wj\right)\right)}}{wj + 1}
\] |
+-commutative [=>]11.12 | \[ \frac{\frac{x}{e^{wj}} + \color{blue}{\left(\left(wj + wj \cdot wj\right) + \left(-wj\right)\right)}}{wj + 1}
\] |
distribute-rgt1-in [=>]11.14 | \[ \frac{\frac{x}{e^{wj}} + \left(\color{blue}{\left(wj + 1\right) \cdot wj} + \left(-wj\right)\right)}{wj + 1}
\] |
neg-mul-1 [=>]11.14 | \[ \frac{\frac{x}{e^{wj}} + \left(\left(wj + 1\right) \cdot wj + \color{blue}{-1 \cdot wj}\right)}{wj + 1}
\] |
distribute-rgt-in [<=]11.12 | \[ \frac{\frac{x}{e^{wj}} + \color{blue}{wj \cdot \left(\left(wj + 1\right) + -1\right)}}{wj + 1}
\] |
associate-+l+ [=>]0.06 | \[ \frac{\frac{x}{e^{wj}} + wj \cdot \color{blue}{\left(wj + \left(1 + -1\right)\right)}}{wj + 1}
\] |
metadata-eval [=>]0.06 | \[ \frac{\frac{x}{e^{wj}} + wj \cdot \left(wj + \color{blue}{0}\right)}{wj + 1}
\] |
+-commutative [<=]0.06 | \[ \frac{\frac{x}{e^{wj}} + wj \cdot \color{blue}{\left(0 + wj\right)}}{wj + 1}
\] |
+-lft-identity [=>]0.06 | \[ \frac{\frac{x}{e^{wj}} + wj \cdot \color{blue}{wj}}{wj + 1}
\] |
Taylor expanded in x around 0 0.06
Simplified0.06
[Start]0.06 | \[ \frac{{wj}^{2} + \frac{x}{e^{wj}}}{wj + 1}
\] |
|---|---|
unpow2 [=>]0.06 | \[ \frac{\color{blue}{wj \cdot wj} + \frac{x}{e^{wj}}}{wj + 1}
\] |
fma-udef [<=]0.06 | \[ \frac{\color{blue}{\mathsf{fma}\left(wj, wj, \frac{x}{e^{wj}}\right)}}{wj + 1}
\] |
Final simplification0.06
| Alternative 1 | |
|---|---|
| Error | 0.06% |
| Cost | 7104 |
| Alternative 2 | |
|---|---|
| Error | 1.19% |
| Cost | 1344 |
| Alternative 3 | |
|---|---|
| Error | 14.68% |
| Cost | 1096 |
| Alternative 4 | |
|---|---|
| Error | 14.79% |
| Cost | 841 |
| Alternative 5 | |
|---|---|
| Error | 1.4% |
| Cost | 832 |
| Alternative 6 | |
|---|---|
| Error | 14.9% |
| Cost | 713 |
| Alternative 7 | |
|---|---|
| Error | 14.81% |
| Cost | 712 |
| Alternative 8 | |
|---|---|
| Error | 14.75% |
| Cost | 712 |
| Alternative 9 | |
|---|---|
| Error | 15.35% |
| Cost | 456 |
| Alternative 10 | |
|---|---|
| Error | 95.6% |
| Cost | 64 |
| Alternative 11 | |
|---|---|
| Error | 15.34% |
| Cost | 64 |
herbie shell --seed 2023115
(FPCore (wj x)
:name "Jmat.Real.lambertw, newton loop step"
:precision binary64
:herbie-target
(- wj (- (/ wj (+ wj 1.0)) (/ x (+ (exp wj) (* wj (exp wj))))))
(- wj (/ (- (* wj (exp wj)) x) (+ (exp wj) (* wj (exp wj))))))