Average Error: 11.9 → 0.4
Time: 1.7m
Precision: 64
Internal Precision: 576
\[\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5\]
\[(\left((e^{\log_* (1 + \frac{3 - v \cdot 2}{\frac{1 - v}{0.125}})} - 1)^*\right) \cdot \left(-\left(r \cdot w\right) \cdot \left(r \cdot w\right)\right) + \left(\left(\left(-4.5\right) + 3\right) + \frac{2}{r \cdot r}\right))_* + 0\]

Error

Bits error versus v

Bits error versus w

Bits error versus r

Derivation

  1. Initial program 11.9

    \[\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5\]
  2. Applied simplify0.4

    \[\leadsto \color{blue}{\left(\frac{\frac{2}{r}}{r} + 3\right) - (\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\]
  3. Using strategy rm
  4. Applied add-sqr-sqrt1.0

    \[\leadsto \left(\frac{\frac{2}{r}}{r} + 3\right) - \color{blue}{\sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*} \cdot \sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}}\]
  5. Applied add-sqr-sqrt1.0

    \[\leadsto \color{blue}{\sqrt{\frac{\frac{2}{r}}{r} + 3} \cdot \sqrt{\frac{\frac{2}{r}}{r} + 3}} - \sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*} \cdot \sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\]
  6. Applied prod-diff1.0

    \[\leadsto \color{blue}{(\left(\sqrt{\frac{\frac{2}{r}}{r} + 3}\right) \cdot \left(\sqrt{\frac{\frac{2}{r}}{r} + 3}\right) + \left(-\sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*} \cdot \sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\right))_* + (\left(-\sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\right) \cdot \left(\sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\right) + \left(\sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*} \cdot \sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\right))_*}\]
  7. Applied simplify0.4

    \[\leadsto \color{blue}{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(-\left(r \cdot w\right) \cdot \left(r \cdot w\right)\right) + \left(\left(\left(-4.5\right) + 3\right) + \frac{2}{r \cdot r}\right))_*} + (\left(-\sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\right) \cdot \left(\sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\right) + \left(\sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*} \cdot \sqrt{(\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(\left(w \cdot r\right) \cdot \left(w \cdot r\right)\right) + 4.5)_*}\right))_*\]
  8. Applied simplify0.4

    \[\leadsto (\left(\frac{3 - v \cdot 2}{\frac{1 - v}{0.125}}\right) \cdot \left(-\left(r \cdot w\right) \cdot \left(r \cdot w\right)\right) + \left(\left(\left(-4.5\right) + 3\right) + \frac{2}{r \cdot r}\right))_* + \color{blue}{0}\]
  9. Using strategy rm
  10. Applied expm1-log1p-u0.4

    \[\leadsto (\color{blue}{\left((e^{\log_* (1 + \frac{3 - v \cdot 2}{\frac{1 - v}{0.125}})} - 1)^*\right)} \cdot \left(-\left(r \cdot w\right) \cdot \left(r \cdot w\right)\right) + \left(\left(\left(-4.5\right) + 3\right) + \frac{2}{r \cdot r}\right))_* + 0\]

Runtime

Time bar (total: 1.7m)Debug logProfile

herbie shell --seed 2018206 +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))