Average Error: 0.0 → 0.0
Time: 2.1s
Precision: binary64
\[e^{-\left(1 - x \cdot x\right)}\]
\[{\left(e^{x + 1}\right)}^{\left(x - 1\right)}\]
e^{-\left(1 - x \cdot x\right)}
{\left(e^{x + 1}\right)}^{\left(x - 1\right)}
(FPCore (x) :precision binary64 (exp (- (- 1.0 (* x x)))))
(FPCore (x) :precision binary64 (pow (exp (+ x 1.0)) (- x 1.0)))
double code(double x) {
	return exp(-(1.0 - (x * x)));
}
double code(double x) {
	return pow(exp(x + 1.0), (x - 1.0));
}

Error

Bits error versus x

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.0

    \[e^{-\left(1 - x \cdot x\right)}\]
  2. Simplified0.0

    \[\leadsto \color{blue}{e^{x \cdot x - 1}}\]
  3. Using strategy rm
  4. Applied difference-of-sqr-1_binary64_3890.0

    \[\leadsto e^{\color{blue}{\left(x + 1\right) \cdot \left(x - 1\right)}}\]
  5. Applied exp-prod_binary64_4710.0

    \[\leadsto \color{blue}{{\left(e^{x + 1}\right)}^{\left(x - 1\right)}}\]
  6. Final simplification0.0

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

Reproduce

herbie shell --seed 2021022 
(FPCore (x)
  :name "exp neg sub"
  :precision binary64
  (exp (- (- 1.0 (* x x)))))