Average Error: 0.0 → 0.0
Time: 1.6s
Precision: binary64
\[e^{-\left(1 - x \cdot x\right)} \]
↓
\[{e}^{\left(\mathsf{fma}\left(x, x, -1\right)\right)} \]
(FPCore (x) :precision binary64 (exp (- (- 1.0 (* x x)))))
↓
(FPCore (x) :precision binary64 (pow E (fma x x -1.0)))
double code(double x) {
	return exp(-(1.0 - (x * x)));
}
↓
double code(double x) {
	return pow(((double) M_E), fma(x, x, -1.0));
}

function code(x)
	return exp(Float64(-Float64(1.0 - Float64(x * x))))
end
↓
function code(x)
	return exp(1) ^ fma(x, x, -1.0)
end

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

e^{-\left(1 - x \cdot x\right)}
↓
{e}^{\left(\mathsf{fma}\left(x, x, -1\right)\right)}

Error

Bits error versus x

Derivation

  1. Initial program 0.0

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

  2. Simplified0.0

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

  3. Applied egg-rr0.0

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

  4. Final simplification0.0

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

Reproduce

herbie shell --seed 2022131 
(FPCore (x)
  :name "exp neg sub"
  :precision binary64
  (exp (- (- 1.0 (* x x)))))