exp neg sub

Average Accuracy: 100.0% → 100.0%

Time: 1.8s

Precision: binary64

Cost: 12992

Math

\[e^{-\left(1 - x \cdot x\right)} \]

↓

\[e^{\mathsf{fma}\left(x, x, -1\right)} \]

FPCore

(FPCore (x) :precision binary64 (exp (- (- 1.0 (* x x)))))

↓

(FPCore (x) :precision binary64 (exp (fma x x -1.0)))

C

double code(double x) {
	return exp(-(1.0 - (x * x)));
}

↓

double code(double x) {
	return exp(fma(x, x, -1.0));
}

Julia

function code(x)
	return exp(Float64(-Float64(1.0 - Float64(x * x))))
end

↓

function code(x)
	return exp(fma(x, x, -1.0))
end

Wolfram

code[x_] := N[Exp[(-N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision])], $MachinePrecision]

↓

code[x_] := N[Exp[N[(x * x + -1.0), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[e^{-\left(1 - x \cdot x\right)} \]

2. Simplified 100.0%

   \[\leadsto \color{blue}{e^{\mathsf{fma}\left(x, x, -1\right)}} \]
   Proof

   [Start] 100.0	\[ e^{-\left(1 - x \cdot x\right)} \]
   neg-sub0 [=>] 100.0	\[ e^{\color{blue}{0 - \left(1 - x \cdot x\right)}} \]
   associate--r- [=>] 100.0	\[ e^{\color{blue}{\left(0 - 1\right) + x \cdot x}} \]
   metadata-eval [=>] 100.0	\[ e^{\color{blue}{-1} + x \cdot x} \]
   +-commutative [=>] 100.0	\[ e^{\color{blue}{x \cdot x + -1}} \]
   fma-def [=>] 100.0	\[ e^{\color{blue}{\mathsf{fma}\left(x, x, -1\right)}} \]

3. Final simplification 100.0%

   \[\leadsto e^{\mathsf{fma}\left(x, x, -1\right)} \]

Alternatives

Alternative 1
Accuracy	100.0%
Cost	6720

\[e^{-1 + x \cdot x} \]

Alternative 2
Accuracy	98.5%
Cost	6464

\[e^{-1} \]

Reproduce

herbie shell --seed 2023137 
(FPCore (x)
  :name "exp neg sub"
  :precision binary64
  (exp (- (- 1.0 (* x x)))))