Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2

Average Accuracy: 99.8% → 99.9%

Time: 12.0s

Precision: binary64

Cost: 13376

Math

\[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

↓

\[\mathsf{fma}\left(\log y, -0.5 - y, y + \left(x - z\right)\right) \]

FPCore

(FPCore (x y z) :precision binary64 (- (+ (- x (* (+ y 0.5) (log y))) y) z))

↓

(FPCore (x y z) :precision binary64 (fma (log y) (- -0.5 y) (+ y (- x z))))

C

double code(double x, double y, double z) {
	return ((x - ((y + 0.5) * log(y))) + y) - z;
}

↓

double code(double x, double y, double z) {
	return fma(log(y), (-0.5 - y), (y + (x - z)));
}

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x - Float64(Float64(y + 0.5) * log(y))) + y) - z)
end

↓

function code(x, y, z)
	return fma(log(y), Float64(-0.5 - y), Float64(y + Float64(x - z)))
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x - N[(N[(y + 0.5), $MachinePrecision] * N[Log[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + y), $MachinePrecision] - z), $MachinePrecision]

↓

code[x_, y_, z_] := N[(N[Log[y], $MachinePrecision] * N[(-0.5 - y), $MachinePrecision] + N[(y + N[(x - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	99.8%
Target	99.8%
Herbie	99.9%

\[\left(\left(y + x\right) - z\right) - \left(y + 0.5\right) \cdot \log y \]

Derivation

1. Initial program 99.8%

   \[\left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]

2. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\log y, -0.5 - y, y + \left(x - z\right)\right)} \]
   Proof

   [Start] 99.8	\[ \left(\left(x - \left(y + 0.5\right) \cdot \log y\right) + y\right) - z \]
   associate--l+ [=>] 99.8	\[ \color{blue}{\left(x - \left(y + 0.5\right) \cdot \log y\right) + \left(y - z\right)} \]
   sub-neg [=>] 99.8	\[ \color{blue}{\left(x + \left(-\left(y + 0.5\right) \cdot \log y\right)\right)} + \left(y - z\right) \]
   +-commutative [=>] 99.8	\[ \color{blue}{\left(\left(-\left(y + 0.5\right) \cdot \log y\right) + x\right)} + \left(y - z\right) \]
   associate-+l+ [=>] 99.8	\[ \color{blue}{\left(-\left(y + 0.5\right) \cdot \log y\right) + \left(x + \left(y - z\right)\right)} \]
   *-commutative [=>] 99.8	\[ \left(-\color{blue}{\log y \cdot \left(y + 0.5\right)}\right) + \left(x + \left(y - z\right)\right) \]
   distribute-rgt-neg-in [=>] 99.8	\[ \color{blue}{\log y \cdot \left(-\left(y + 0.5\right)\right)} + \left(x + \left(y - z\right)\right) \]
   fma-def [=>] 99.9	\[ \color{blue}{\mathsf{fma}\left(\log y, -\left(y + 0.5\right), x + \left(y - z\right)\right)} \]
   neg-sub0 [=>] 99.9	\[ \mathsf{fma}\left(\log y, \color{blue}{0 - \left(y + 0.5\right)}, x + \left(y - z\right)\right) \]
   +-commutative [=>] 99.9	\[ \mathsf{fma}\left(\log y, 0 - \color{blue}{\left(0.5 + y\right)}, x + \left(y - z\right)\right) \]
   associate--r+ [=>] 99.9	\[ \mathsf{fma}\left(\log y, \color{blue}{\left(0 - 0.5\right) - y}, x + \left(y - z\right)\right) \]
   metadata-eval [=>] 99.9	\[ \mathsf{fma}\left(\log y, \color{blue}{-0.5} - y, x + \left(y - z\right)\right) \]
   associate-+r- [=>] 99.9	\[ \mathsf{fma}\left(\log y, -0.5 - y, \color{blue}{\left(x + y\right) - z}\right) \]
   +-commutative [<=] 99.9	\[ \mathsf{fma}\left(\log y, -0.5 - y, \color{blue}{\left(y + x\right)} - z\right) \]
   associate-+r- [<=] 99.9	\[ \mathsf{fma}\left(\log y, -0.5 - y, \color{blue}{y + \left(x - z\right)}\right) \]

3. Final simplification 99.9%

   \[\leadsto \mathsf{fma}\left(\log y, -0.5 - y, y + \left(x - z\right)\right) \]

Alternatives

Alternative 1
Accuracy	62.5%
Cost	7644

\[\begin{array}{l} t_0 := y \cdot \left(1 - \log y\right)\\ t_1 := x - \log y \cdot 0.5\\ \mathbf{if}\;z \leq -3.8 \cdot 10^{+14}:\\ \;\;\;\;x - z\\ \mathbf{elif}\;z \leq -3.4 \cdot 10^{-127}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -4.8 \cdot 10^{-182}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.25 \cdot 10^{-305}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 2.4 \cdot 10^{-95}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 2.05 \cdot 10^{-10}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{+106}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \]

Alternative 2
Accuracy	62.5%
Cost	7644

\[\begin{array}{l} t_0 := x - \log y \cdot 0.5\\ t_1 := y \cdot \left(1 - \log y\right)\\ \mathbf{if}\;z \leq -3.8 \cdot 10^{+14}:\\ \;\;\;\;x - z\\ \mathbf{elif}\;z \leq -1 \cdot 10^{-122}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -3.6 \cdot 10^{-181}:\\ \;\;\;\;y - y \cdot \log y\\ \mathbf{elif}\;z \leq 4.7 \cdot 10^{-306}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.45 \cdot 10^{-95}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{-8}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 2.8 \cdot 10^{+106}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \]

Alternative 3
Accuracy	88.6%
Cost	7245

\[\begin{array}{l} \mathbf{if}\;y \leq 12200 \lor \neg \left(y \leq 2.25 \cdot 10^{+46}\right) \land y \leq 3.7 \cdot 10^{+95}:\\ \;\;\;\;\left(x - \log y \cdot 0.5\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(y - y \cdot \log y\right) - z\\ \end{array} \]

Alternative 4
Accuracy	99.2%
Cost	7241

\[\begin{array}{l} \mathbf{if}\;x \leq -22.5 \lor \neg \left(x \leq 0.072\right):\\ \;\;\;\;\left(y + \left(x - y \cdot \log y\right)\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(y - \log y \cdot \left(y + 0.5\right)\right) - z\\ \end{array} \]

Alternative 5
Accuracy	99.2%
Cost	7108

\[\begin{array}{l} \mathbf{if}\;y \leq 2.5 \cdot 10^{-10}:\\ \;\;\;\;\left(x - \log y \cdot 0.5\right) - z\\ \mathbf{else}:\\ \;\;\;\;\left(y + \left(x - y \cdot \log y\right)\right) - z\\ \end{array} \]

Alternative 6
Accuracy	99.8%
Cost	7104

\[\left(y + \left(x - \log y \cdot \left(y + 0.5\right)\right)\right) - z \]

Alternative 7
Accuracy	84.7%
Cost	6980

\[\begin{array}{l} \mathbf{if}\;y \leq 2.6 \cdot 10^{+135}:\\ \;\;\;\;\left(x - \log y \cdot 0.5\right) - z\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(1 - \log y\right)\\ \end{array} \]

Alternative 8
Accuracy	57.4%
Cost	6856

\[\begin{array}{l} \mathbf{if}\;z \leq 5.6 \cdot 10^{-188}:\\ \;\;\;\;x - z\\ \mathbf{elif}\;z \leq 6.6 \cdot 10^{-85}:\\ \;\;\;\;\log y \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;x - z\\ \end{array} \]

Alternative 9
Accuracy	72.7%
Cost	6852

\[\begin{array}{l} \mathbf{if}\;y \leq 2.45 \cdot 10^{+134}:\\ \;\;\;\;x - z\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(1 - \log y\right)\\ \end{array} \]

Alternative 10
Accuracy	48.5%
Cost	392

\[\begin{array}{l} \mathbf{if}\;z \leq -5.2 \cdot 10^{+91}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z \leq 7 \cdot 10^{+95}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \]

Alternative 11
Accuracy	58.7%
Cost	192

\[x - z \]

Alternative 12
Accuracy	31.1%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023150 
(FPCore (x y z)
  :name "Numeric.SpecFunctions:stirlingError from math-functions-0.1.5.2"
  :precision binary64

  :herbie-target
  (- (- (+ y x) z) (* (+ y 0.5) (log y)))

  (- (+ (- x (* (+ y 0.5) (log y))) y) z))