Average Error: 0.0 → 0.0
Time: 5.5s
Precision: 64
\[x \cdot \left(1.0 - x \cdot 0.5\right)\]
↓
\[x \cdot 1.0 + \left(-0.5 \cdot x\right) \cdot x\]
x \cdot \left(1.0 - x \cdot 0.5\right)
↓
x \cdot 1.0 + \left(-0.5 \cdot x\right) \cdot x
double f(double x) {
        double r4312784 = x;
        double r4312785 = 1.0;
        double r4312786 = 0.5;
        double r4312787 = r4312784 * r4312786;
        double r4312788 = r4312785 - r4312787;
        double r4312789 = r4312784 * r4312788;
        return r4312789;
}
↓
double f(double x) {
        double r4312790 = x;
        double r4312791 = 1.0;
        double r4312792 = r4312790 * r4312791;
        double r4312793 = 0.5;
        double r4312794 = r4312793 * r4312790;
        double r4312795 = -r4312794;
        double r4312796 = r4312795 * r4312790;
        double r4312797 = r4312792 + r4312796;
        return r4312797;
}

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

    \[x \cdot \left(1.0 - x \cdot 0.5\right)\]
  2. Using strategy rm

  3. Applied sub-neg0.0

    \[\leadsto x \cdot \color{blue}{\left(1.0 + \left(-x \cdot 0.5\right)\right)}\]

  4. Applied distribute-lft-in0.0

    \[\leadsto \color{blue}{x \cdot 1.0 + x \cdot \left(-x \cdot 0.5\right)}\]

  5. Final simplification0.0

    \[\leadsto x \cdot 1.0 + \left(-0.5 \cdot x\right) \cdot x\]

Reproduce

herbie shell --seed 2019158 
(FPCore (x)
  :name "Numeric.SpecFunctions:log1p from math-functions-0.1.5.2, B"
  (* x (- 1.0 (* x 0.5))))