Average Error: 13.1 → 0.2
Time: 6.6s
Precision: binary64
\[\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5 \]
↓
\[\left(\left(3 + 2 \cdot {r}^{-2}\right) - \frac{\mathsf{fma}\left(v, -0.25, 0.375\right)}{\left(1 - v\right) \cdot {\left(r \cdot w\right)}^{-2}}\right) + -4.5 \]
(FPCore (v w r)
 :precision binary64
 (-
  (-
   (+ 3.0 (/ 2.0 (* r r)))
   (/ (* (* 0.125 (- 3.0 (* 2.0 v))) (* (* (* w w) r) r)) (- 1.0 v)))
  4.5))
↓
(FPCore (v w r)
 :precision binary64
 (+
  (-
   (+ 3.0 (* 2.0 (pow r -2.0)))
   (/ (fma v -0.25 0.375) (* (- 1.0 v) (pow (* r w) -2.0))))
  -4.5))
double code(double v, double w, double r) {
	return ((3.0 + (2.0 / (r * r))) - (((0.125 * (3.0 - (2.0 * v))) * (((w * w) * r) * r)) / (1.0 - v))) - 4.5;
}
↓
double code(double v, double w, double r) {
	return ((3.0 + (2.0 * pow(r, -2.0))) - (fma(v, -0.25, 0.375) / ((1.0 - v) * pow((r * w), -2.0)))) + -4.5;
}

function code(v, w, r)
	return Float64(Float64(Float64(3.0 + Float64(2.0 / Float64(r * r))) - Float64(Float64(Float64(0.125 * Float64(3.0 - Float64(2.0 * v))) * Float64(Float64(Float64(w * w) * r) * r)) / Float64(1.0 - v))) - 4.5)
end
↓
function code(v, w, r)
	return Float64(Float64(Float64(3.0 + Float64(2.0 * (r ^ -2.0))) - Float64(fma(v, -0.25, 0.375) / Float64(Float64(1.0 - v) * (Float64(r * w) ^ -2.0)))) + -4.5)
end

code[v_, w_, r_] := N[(N[(N[(3.0 + N[(2.0 / N[(r * r), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(0.125 * N[(3.0 - N[(2.0 * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(N[(N[(w * w), $MachinePrecision] * r), $MachinePrecision] * r), $MachinePrecision]), $MachinePrecision] / N[(1.0 - v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 4.5), $MachinePrecision]
↓
code[v_, w_, r_] := N[(N[(N[(3.0 + N[(2.0 * N[Power[r, -2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(v * -0.25 + 0.375), $MachinePrecision] / N[(N[(1.0 - v), $MachinePrecision] * N[Power[N[(r * w), $MachinePrecision], -2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -4.5), $MachinePrecision]

\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(\left(3 + 2 \cdot {r}^{-2}\right) - \frac{\mathsf{fma}\left(v, -0.25, 0.375\right)}{\left(1 - v\right) \cdot {\left(r \cdot w\right)}^{-2}}\right) + -4.5

Error

Derivation

  1. Initial program 13.1

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

  2. Applied egg-rr0.4

    \[\leadsto \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\left(0.375 + \left(v \cdot -2\right) \cdot 0.125\right) \cdot \frac{1}{\frac{1 - v}{{\left(w \cdot r\right)}^{2}}}}\right) - 4.5 \]

  3. Applied egg-rr0.3

    \[\leadsto \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\frac{\mathsf{fma}\left(v, -0.25, 0.375\right)}{\left(1 - v\right) \cdot {\left(w \cdot r\right)}^{-2}}}\right) - 4.5 \]

  4. Applied egg-rr0.2

    \[\leadsto \left(\left(3 + \color{blue}{2 \cdot {r}^{-2}}\right) - \frac{\mathsf{fma}\left(v, -0.25, 0.375\right)}{\left(1 - v\right) \cdot {\left(w \cdot r\right)}^{-2}}\right) - 4.5 \]

  5. Final simplification0.2

    \[\leadsto \left(\left(3 + 2 \cdot {r}^{-2}\right) - \frac{\mathsf{fma}\left(v, -0.25, 0.375\right)}{\left(1 - v\right) \cdot {\left(r \cdot w\right)}^{-2}}\right) + -4.5 \]

Reproduce

herbie shell --seed 2022204 
(FPCore (v w r)
  :name "Rosa's TurbineBenchmark"
  :precision binary64
  (- (- (+ 3.0 (/ 2.0 (* r r))) (/ (* (* 0.125 (- 3.0 (* 2.0 v))) (* (* (* w w) r) r)) (- 1.0 v))) 4.5))