Average Error: 0.2 → 0.2
Time: 5.1s
Precision: binary64
\[0.954929658551372 \cdot x - 0.12900613773279798 \cdot \left(\left(x \cdot x\right) \cdot x\right) \]
↓
\[\mathsf{fma}\left(-{x}^{3}, 0.12900613773279798, x \cdot 0.954929658551372\right) \]
0.954929658551372 \cdot x - 0.12900613773279798 \cdot \left(\left(x \cdot x\right) \cdot x\right)
↓
\mathsf{fma}\left(-{x}^{3}, 0.12900613773279798, x \cdot 0.954929658551372\right)

(FPCore (x)
 :precision binary64
 (- (* 0.954929658551372 x) (* 0.12900613773279798 (* (* x x) x))))
↓
(FPCore (x)
 :precision binary64
 (fma (- (pow x 3.0)) 0.12900613773279798 (* x 0.954929658551372)))
double code(double x) {
	return (0.954929658551372 * x) - (0.12900613773279798 * ((x * x) * x));
}
↓
double code(double x) {
	return fma(-pow(x, 3.0), 0.12900613773279798, (x * 0.954929658551372));
}

Error

Bits error versus x

Derivation

  1. Initial program 0.2

    \[0.954929658551372 \cdot x - 0.12900613773279798 \cdot \left(\left(x \cdot x\right) \cdot x\right) \]

  2. Applied egg-rr0.2

    \[\leadsto \color{blue}{\mathsf{fma}\left(-{x}^{3}, 0.12900613773279798, x \cdot 0.954929658551372\right)} \]

  3. Final simplification0.2

    \[\leadsto \mathsf{fma}\left(-{x}^{3}, 0.12900613773279798, x \cdot 0.954929658551372\right) \]

Reproduce

herbie shell --seed 2022130 
(FPCore (x)
  :name "Rosa's Benchmark"
  :precision binary64
  (- (* 0.954929658551372 x) (* 0.12900613773279798 (* (* x x) x))))