Average Error: 0.1 → 0.1
Time: 1.2s
Precision: binary64
\[0.954929658551372 \cdot x - 0.12900613773279798 \cdot \left(\left(x \cdot x\right) \cdot x\right)\]
↓
\[0.954929658551372 \cdot x - \left(x \cdot 0.12900613773279798\right) \cdot \left(x \cdot x\right)\]
0.954929658551372 \cdot x - 0.12900613773279798 \cdot \left(\left(x \cdot x\right) \cdot x\right)
↓
0.954929658551372 \cdot x - \left(x \cdot 0.12900613773279798\right) \cdot \left(x \cdot x\right)
(FPCore (x)
 :precision binary64
 (- (* 0.954929658551372 x) (* 0.12900613773279798 (* (* x x) x))))
↓
(FPCore (x)
 :precision binary64
 (- (* 0.954929658551372 x) (* (* x 0.12900613773279798) (* x x))))
double code(double x) {
	return ((double) (((double) (0.954929658551372 * x)) - ((double) (0.12900613773279798 * ((double) (((double) (x * x)) * x))))));
}
↓
double code(double x) {
	return ((double) (((double) (0.954929658551372 * x)) - ((double) (((double) (x * 0.12900613773279798)) * ((double) (x * x))))));
}

Error

Bits error versus x

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.1

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

  2. Simplified0.1

    \[\leadsto \color{blue}{0.954929658551372 \cdot x - 0.12900613773279798 \cdot {x}^{3}}\]
  3. Using strategy rm

  4. Applied cube-mult0.1

    \[\leadsto 0.954929658551372 \cdot x - 0.12900613773279798 \cdot \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)}\]

  5. Applied associate-*r*0.1

    \[\leadsto 0.954929658551372 \cdot x - \color{blue}{\left(0.12900613773279798 \cdot x\right) \cdot \left(x \cdot x\right)}\]

  6. Final simplification0.1

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

Reproduce

herbie shell --seed 2020198 
(FPCore (x)
  :name "Rosa's Benchmark"
  :precision binary64
  (- (* 0.954929658551372 x) (* 0.12900613773279798 (* (* x x) x))))