Average Error: 12.1 → 0.3
Time: 35.1s
Precision: 64
Internal Precision: 128
\[\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5\]
\[\frac{\frac{2}{r}}{r} - \left(\left(4.5 - 3\right) + \left(\frac{(v \cdot -2 + 3)_* \cdot 0.125}{1 - v} \cdot \left(r \cdot w\right)\right) \cdot \left(r \cdot w\right)\right)\]

Error

Bits error versus v

Bits error versus w

Bits error versus r

Derivation

  1. Initial program 12.1

    \[\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5\]
  2. Simplified0.4

    \[\leadsto \color{blue}{\left(\frac{2}{r \cdot r} - (\left(\frac{(v \cdot -2 + 3)_* \cdot 0.125}{1 - v}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*\right) + 3}\]
  3. Using strategy rm
  4. Applied associate-/r*0.4

    \[\leadsto \left(\color{blue}{\frac{\frac{2}{r}}{r}} - (\left(\frac{(v \cdot -2 + 3)_* \cdot 0.125}{1 - v}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*\right) + 3\]
  5. Using strategy rm
  6. Applied associate-+l-0.4

    \[\leadsto \color{blue}{\frac{\frac{2}{r}}{r} - \left((\left(\frac{(v \cdot -2 + 3)_* \cdot 0.125}{1 - v}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_* - 3\right)}\]
  7. Using strategy rm
  8. Applied fma-udef0.4

    \[\leadsto \frac{\frac{2}{r}}{r} - \left(\color{blue}{\left(\frac{(v \cdot -2 + 3)_* \cdot 0.125}{1 - v} \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5\right)} - 3\right)\]
  9. Applied associate--l+0.4

    \[\leadsto \frac{\frac{2}{r}}{r} - \color{blue}{\left(\frac{(v \cdot -2 + 3)_* \cdot 0.125}{1 - v} \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + \left(4.5 - 3\right)\right)}\]
  10. Using strategy rm
  11. Applied associate-*r*0.3

    \[\leadsto \frac{\frac{2}{r}}{r} - \left(\color{blue}{\left(\frac{(v \cdot -2 + 3)_* \cdot 0.125}{1 - v} \cdot \left(w \cdot r\right)\right) \cdot \left(w \cdot r\right)} + \left(4.5 - 3\right)\right)\]
  12. Final simplification0.3

    \[\leadsto \frac{\frac{2}{r}}{r} - \left(\left(4.5 - 3\right) + \left(\frac{(v \cdot -2 + 3)_* \cdot 0.125}{1 - v} \cdot \left(r \cdot w\right)\right) \cdot \left(r \cdot w\right)\right)\]

Reproduce

herbie shell --seed 2019089 +o rules:numerics
(FPCore (v w r)
  :name "Rosa's TurbineBenchmark"
  (- (- (+ 3 (/ 2 (* r r))) (/ (* (* 0.125 (- 3 (* 2 v))) (* (* (* w w) r) r)) (- 1 v))) 4.5))