Average Error: 0.0 → 0.0
Time: 662.0ms
Precision: 64
\[\left(x \cdot x + y\right) + y\]
↓
\[\mathsf{fma}\left(y, 2, {x}^{2}\right)\]
\left(x \cdot x + y\right) + y
↓
\mathsf{fma}\left(y, 2, {x}^{2}\right)
double f(double x, double y) {
        double r915171 = x;
        double r915172 = r915171 * r915171;
        double r915173 = y;
        double r915174 = r915172 + r915173;
        double r915175 = r915174 + r915173;
        return r915175;
}
↓
double f(double x, double y) {
        double r915176 = y;
        double r915177 = 2.0;
        double r915178 = x;
        double r915179 = pow(r915178, r915177);
        double r915180 = fma(r915176, r915177, r915179);
        return r915180;
}

Error

Bits error versus x

Bits error versus y

Target

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

Derivation

  1. Initial program 0.0

    \[\left(x \cdot x + y\right) + y\]

  2. Simplified0.0

    \[\leadsto \color{blue}{y + \mathsf{fma}\left(x, x, y\right)}\]
  3. Using strategy rm

  4. Applied *-un-lft-identity0.0

    \[\leadsto y + \color{blue}{1 \cdot \mathsf{fma}\left(x, x, y\right)}\]

  5. Applied *-un-lft-identity0.0

    \[\leadsto \color{blue}{1 \cdot y} + 1 \cdot \mathsf{fma}\left(x, x, y\right)\]

  6. Applied distribute-lft-out0.0

    \[\leadsto \color{blue}{1 \cdot \left(y + \mathsf{fma}\left(x, x, y\right)\right)}\]

  7. Simplified0.0

    \[\leadsto 1 \cdot \color{blue}{\mathsf{fma}\left(y, 2, {x}^{2}\right)}\]

  8. Final simplification0.0

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

Reproduce

herbie shell --seed 2020065 +o rules:numerics
(FPCore (x y)
  :name "Data.Random.Distribution.Normal:normalTail from random-fu-0.2.6.2"
  :precision binary64

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

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