Average Error: 0.0 → 0.0
Time: 1.9s
Precision: 64
\[\left(x + y\right) \cdot \left(x + y\right)\]
↓
\[\mathsf{fma}\left(x, x, \mathsf{fma}\left(2 \cdot x, y, {y}^{2}\right)\right)\]
\left(x + y\right) \cdot \left(x + y\right)
↓
\mathsf{fma}\left(x, x, \mathsf{fma}\left(2 \cdot x, y, {y}^{2}\right)\right)
double code(double x, double y) {
	return ((double) (((double) (x + y)) * ((double) (x + y))));
}
↓
double code(double x, double y) {
	return ((double) fma(x, x, ((double) fma(((double) (2.0 * x)), y, ((double) pow(y, 2.0))))));
}

Error

Bits error versus x

Bits error versus y

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original0.0
Target0.0
Herbie0.0
\[x \cdot x + \left(y \cdot y + 2 \cdot \left(y \cdot x\right)\right)\]

Derivation

  1. Initial program 0.0

    \[\left(x + y\right) \cdot \left(x + y\right)\]
  2. Using strategy rm

  3. Applied +-commutative0.0

    \[\leadsto \left(x + y\right) \cdot \color{blue}{\left(y + x\right)}\]

  4. Applied distribute-lft-in0.0

    \[\leadsto \color{blue}{\left(x + y\right) \cdot y + \left(x + y\right) \cdot x}\]

  5. Simplified0.0

    \[\leadsto \color{blue}{y \cdot \left(x + y\right)} + \left(x + y\right) \cdot x\]

  6. Simplified0.0

    \[\leadsto y \cdot \left(x + y\right) + \color{blue}{x \cdot \left(x + y\right)}\]

  7. Taylor expanded around 0 0.0

    \[\leadsto \color{blue}{{x}^{2} + \left({y}^{2} + 2 \cdot \left(x \cdot y\right)\right)}\]

  8. Simplified0.0

    \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, \mathsf{fma}\left(2 \cdot x, y, {y}^{2}\right)\right)}\]

  9. Final simplification0.0

    \[\leadsto \mathsf{fma}\left(x, x, \mathsf{fma}\left(2 \cdot x, y, {y}^{2}\right)\right)\]

Reproduce

herbie shell --seed 2020113 +o rules:numerics
(FPCore (x y)
  :name "Examples.Basics.BasicTests:f3 from sbv-4.4"
  :precision binary64

  :herbie-target
  (+ (* x x) (+ (* y y) (* 2 (* y x))))

  (* (+ x y) (+ x y)))