Average Error: 0.0 → 0.0
Time: 815.0ms
Precision: binary64
\[x \cdot \left(1 - x \cdot 0.5\right)\]
↓
\[x \cdot 1 - x \cdot \left(x \cdot 0.5\right)\]
x \cdot \left(1 - x \cdot 0.5\right)
↓
x \cdot 1 - x \cdot \left(x \cdot 0.5\right)
double code(double x) {
	return ((double) (x * ((double) (1.0 - ((double) (x * 0.5))))));
}
↓
double code(double x) {
	return ((double) (((double) (x * 1.0)) - ((double) (x * ((double) (x * 0.5))))));
}

Error

Bits error versus x

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.0

    \[x \cdot \left(1 - x \cdot 0.5\right)\]
  2. Using strategy rm

  3. Applied sub-neg0.0

    \[\leadsto x \cdot \color{blue}{\left(1 + \left(-x \cdot 0.5\right)\right)}\]

  4. Applied distribute-lft-in0.0

    \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(-x \cdot 0.5\right)}\]

  5. Simplified0.0

    \[\leadsto x \cdot 1 + \color{blue}{x \cdot \left(x \cdot \left(-0.5\right)\right)}\]

  6. Final simplification0.0

    \[\leadsto x \cdot 1 - x \cdot \left(x \cdot 0.5\right)\]

Reproduce

herbie shell --seed 2020196 
(FPCore (x)
  :name "Numeric.SpecFunctions:log1p from math-functions-0.1.5.2, B"
  :precision binary64
  (* x (- 1.0 (* x 0.5))))