Numeric.Log:$cexpm1 from log-domain-0.10.2.1, B

Average Accuracy: 100.0% → 100.0%

Time: 3.8s

Precision: binary64

Cost: 6720

Math

\[\left(x \cdot y + x\right) + y \]

↓

\[y + \mathsf{fma}\left(x, y, x\right) \]

FPCore

(FPCore (x y) :precision binary64 (+ (+ (* x y) x) y))

↓

(FPCore (x y) :precision binary64 (+ y (fma x y x)))

C

double code(double x, double y) {
	return ((x * y) + x) + y;
}

↓

double code(double x, double y) {
	return y + fma(x, y, x);
}

Julia

function code(x, y)
	return Float64(Float64(Float64(x * y) + x) + y)
end

↓

function code(x, y)
	return Float64(y + fma(x, y, x))
end

Wolfram

code[x_, y_] := N[(N[(N[(x * y), $MachinePrecision] + x), $MachinePrecision] + y), $MachinePrecision]

↓

code[x_, y_] := N[(y + N[(x * y + x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\left(x \cdot y + x\right) + y \]

2. Simplified 100.0%

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

   [Start] 100.0	\[ \left(x \cdot y + x\right) + y \]
   +-commutative [=>] 100.0	\[ \color{blue}{y + \left(x \cdot y + x\right)} \]
   fma-def [=>] 100.0	\[ y + \color{blue}{\mathsf{fma}\left(x, y, x\right)} \]

3. Final simplification 100.0%

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

Alternatives

Alternative 1
Accuracy	61.8%
Cost	984

\[\begin{array}{l} \mathbf{if}\;y \leq -1:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{-197}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 6.1 \cdot 10^{-149}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 8 \cdot 10^{-75}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.75 \cdot 10^{+72}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 2.15 \cdot 10^{+102}:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]

Alternative 2
Accuracy	78.4%
Cost	589

\[\begin{array}{l} \mathbf{if}\;y \leq -1.46 \cdot 10^{+16} \lor \neg \left(y \leq 2.4 \cdot 10^{+72}\right) \land y \leq 1.15 \cdot 10^{+102}:\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \]

Alternative 3
Accuracy	80.5%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x \leq -32:\\ \;\;\;\;x \cdot \left(y + 1\right)\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{-138}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \end{array} \]

Alternative 4
Accuracy	80.5%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x \leq -32:\\ \;\;\;\;x + y \cdot x\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{-138}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(x + 1\right)\\ \end{array} \]

Alternative 5
Accuracy	80.5%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x \leq -32:\\ \;\;\;\;x + y \cdot x\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{-138}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;y + y \cdot x\\ \end{array} \]

Alternative 6
Accuracy	55.0%
Cost	460

\[\begin{array}{l} \mathbf{if}\;y \leq 6.2 \cdot 10^{-197}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 6.1 \cdot 10^{-149}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 1.16 \cdot 10^{-75}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]

Alternative 7
Accuracy	85.5%
Cost	456

\[\begin{array}{l} \mathbf{if}\;x \leq -32:\\ \;\;\;\;x \cdot \left(y + 1\right)\\ \mathbf{elif}\;x \leq 6.2 \cdot 10^{+23}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 8
Accuracy	100.0%
Cost	448

\[y + \left(x + y \cdot x\right) \]

Alternative 9
Accuracy	44.4%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023137 
(FPCore (x y)
  :name "Numeric.Log:$cexpm1 from log-domain-0.10.2.1, B"
  :precision binary64
  (+ (+ (* x y) x) y))