Average Error: 0.0 → 0.0
Time: 29.5s
Precision: binary64
\[x + \left(y - z\right) \cdot \left(t - x\right) \]
↓
\[\mathsf{fma}\left(y, t, \mathsf{fma}\left(z, x, x\right) - \mathsf{fma}\left(y, x, t \cdot z\right)\right) \]
x + \left(y - z\right) \cdot \left(t - x\right)
↓
\mathsf{fma}\left(y, t, \mathsf{fma}\left(z, x, x\right) - \mathsf{fma}\left(y, x, t \cdot z\right)\right)

(FPCore (x y z t) :precision binary64 (+ x (* (- y z) (- t x))))
↓
(FPCore (x y z t)
 :precision binary64
 (fma y t (- (fma z x x) (fma y x (* t z)))))
double code(double x, double y, double z, double t) {
	return x + ((y - z) * (t - x));
}
↓
double code(double x, double y, double z, double t) {
	return fma(y, t, (fma(z, x, x) - fma(y, x, (t * z))));
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Target

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

Derivation

  1. Initial program 0.0

    \[x + \left(y - z\right) \cdot \left(t - x\right) \]

  2. Simplified0.0

    \[\leadsto \color{blue}{\mathsf{fma}\left(y - z, t - x, x\right)} \]

  3. Taylor expanded in y around 0 0.0

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

  4. Applied egg-rr0.0

    \[\leadsto \color{blue}{\mathsf{fma}\left(y, t, \mathsf{fma}\left(z, x, x\right) - \mathsf{fma}\left(y, x, t \cdot z\right)\right)} \]

  5. Final simplification0.0

    \[\leadsto \mathsf{fma}\left(y, t, \mathsf{fma}\left(z, x, x\right) - \mathsf{fma}\left(y, x, t \cdot z\right)\right) \]

Reproduce

herbie shell --seed 2022130 
(FPCore (x y z t)
  :name "Data.Metrics.Snapshot:quantile from metrics-0.3.0.2"
  :precision binary64

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

  (+ x (* (- y z) (- t x))))