exp neg sub

Average Error: 0.0 → 0.0

Time: 1.1s

Precision: binary64

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 0.0

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

2. Simplified 0.0

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

3. Final simplification 0.0

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

Reproduce

herbie shell --seed 2022203 
(FPCore (x)
  :name "exp neg sub"
  :precision binary64
  (exp (- (- 1.0 (* x x)))))