Rosa's TurbineBenchmark

Percentage Accurate: 84.2% → 99.8%

Time: 16.6s

Precision: binary64

Cost: 1728

• 53.0% of points produce a very large (infinite) output. You may want to add a precondition.

Math

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

↓

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

FPCore

(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 (* r r)))
   (* (* r w) (/ (+ 0.375 (* v -0.25)) (/ (- 1.0 v) (* r w)))))
  -4.5))

C

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 / (r * r))) - ((r * w) * ((0.375 + (v * -0.25)) / ((1.0 - v) / (r * w))))) + -4.5;
}

Fortran

real(8) function code(v, w, r)
    real(8), intent (in) :: v
    real(8), intent (in) :: w
    real(8), intent (in) :: r
    code = ((3.0d0 + (2.0d0 / (r * r))) - (((0.125d0 * (3.0d0 - (2.0d0 * v))) * (((w * w) * r) * r)) / (1.0d0 - v))) - 4.5d0
end function

↓

real(8) function code(v, w, r)
    real(8), intent (in) :: v
    real(8), intent (in) :: w
    real(8), intent (in) :: r
    code = ((3.0d0 + (2.0d0 / (r * r))) - ((r * w) * ((0.375d0 + (v * (-0.25d0))) / ((1.0d0 - v) / (r * w))))) + (-4.5d0)
end function

Java

public static 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;
}

↓

public static double code(double v, double w, double r) {
	return ((3.0 + (2.0 / (r * r))) - ((r * w) * ((0.375 + (v * -0.25)) / ((1.0 - v) / (r * w))))) + -4.5;
}

Python

def code(v, w, r):
	return ((3.0 + (2.0 / (r * r))) - (((0.125 * (3.0 - (2.0 * v))) * (((w * w) * r) * r)) / (1.0 - v))) - 4.5

↓

def code(v, w, r):
	return ((3.0 + (2.0 / (r * r))) - ((r * w) * ((0.375 + (v * -0.25)) / ((1.0 - v) / (r * w))))) + -4.5

Julia

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 / Float64(r * r))) - Float64(Float64(r * w) * Float64(Float64(0.375 + Float64(v * -0.25)) / Float64(Float64(1.0 - v) / Float64(r * w))))) + -4.5)
end

MATLAB

function tmp = code(v, w, r)
	tmp = ((3.0 + (2.0 / (r * r))) - (((0.125 * (3.0 - (2.0 * v))) * (((w * w) * r) * r)) / (1.0 - v))) - 4.5;
end

↓

function tmp = code(v, w, r)
	tmp = ((3.0 + (2.0 / (r * r))) - ((r * w) * ((0.375 + (v * -0.25)) / ((1.0 - v) / (r * w))))) + -4.5;
end

Wolfram

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[(r * r), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(r * w), $MachinePrecision] * N[(N[(0.375 + N[(v * -0.25), $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 - v), $MachinePrecision] / N[(r * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -4.5), $MachinePrecision]

Derivation

1. Initial program 86.3%

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

2. Simplified 88.5%

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

   [Start] 86.3	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5 \]
   sub-neg [=>] 86.3	\[ \color{blue}{\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) + \left(-4.5\right)} \]
   associate-/l* [=>] 88.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\frac{0.125 \cdot \left(3 - 2 \cdot v\right)}{\frac{1 - v}{\left(\left(w \cdot w\right) \cdot r\right) \cdot r}}}\right) + \left(-4.5\right) \]
   cancel-sign-sub-inv [=>] 88.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \color{blue}{\left(3 + \left(-2\right) \cdot v\right)}}{\frac{1 - v}{\left(\left(w \cdot w\right) \cdot r\right) \cdot r}}\right) + \left(-4.5\right) \]
   metadata-eval [=>] 88.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + \color{blue}{-2} \cdot v\right)}{\frac{1 - v}{\left(\left(w \cdot w\right) \cdot r\right) \cdot r}}\right) + \left(-4.5\right) \]
   *-commutative [=>] 88.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{\color{blue}{r \cdot \left(\left(w \cdot w\right) \cdot r\right)}}}\right) + \left(-4.5\right) \]
   *-commutative [=>] 88.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{r \cdot \color{blue}{\left(r \cdot \left(w \cdot w\right)\right)}}}\right) + \left(-4.5\right) \]
   metadata-eval [=>] 88.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}\right) + \color{blue}{-4.5} \]

3. Applied egg-rr 99.7%

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

   [Start] 88.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}\right) + -4.5 \]
   *-un-lft-identity [=>] 88.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{\color{blue}{1 \cdot \left(1 - v\right)}}{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}\right) + -4.5 \]
   add-sqr-sqrt [=>] 88.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 \cdot \left(1 - v\right)}{\color{blue}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)} \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}}}\right) + -4.5 \]
   times-frac [=>] 88.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\color{blue}{\frac{1}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}} \cdot \frac{1 - v}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}}}\right) + -4.5 \]
   sqrt-prod [=>] 47.7	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{\color{blue}{\sqrt{r} \cdot \sqrt{r \cdot \left(w \cdot w\right)}}} \cdot \frac{1 - v}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}}\right) + -4.5 \]
   sqrt-prod [=>] 47.7	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{\sqrt{r} \cdot \color{blue}{\left(\sqrt{r} \cdot \sqrt{w \cdot w}\right)}} \cdot \frac{1 - v}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}}\right) + -4.5 \]
   sqrt-prod [=>] 21.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{\sqrt{r} \cdot \left(\sqrt{r} \cdot \color{blue}{\left(\sqrt{w} \cdot \sqrt{w}\right)}\right)} \cdot \frac{1 - v}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}}\right) + -4.5 \]
   add-sqr-sqrt [<=] 36.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{\sqrt{r} \cdot \left(\sqrt{r} \cdot \color{blue}{w}\right)} \cdot \frac{1 - v}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}}\right) + -4.5 \]
   associate-*l* [<=] 36.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{\color{blue}{\left(\sqrt{r} \cdot \sqrt{r}\right) \cdot w}} \cdot \frac{1 - v}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}}\right) + -4.5 \]
   add-sqr-sqrt [<=] 73.6	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{\color{blue}{r} \cdot w} \cdot \frac{1 - v}{\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}}\right) + -4.5 \]
   sqrt-prod [=>] 36.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{r \cdot w} \cdot \frac{1 - v}{\color{blue}{\sqrt{r} \cdot \sqrt{r \cdot \left(w \cdot w\right)}}}}\right) + -4.5 \]
   sqrt-prod [=>] 36.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{r \cdot w} \cdot \frac{1 - v}{\sqrt{r} \cdot \color{blue}{\left(\sqrt{r} \cdot \sqrt{w \cdot w}\right)}}}\right) + -4.5 \]
   sqrt-prod [=>] 22.6	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{r \cdot w} \cdot \frac{1 - v}{\sqrt{r} \cdot \left(\sqrt{r} \cdot \color{blue}{\left(\sqrt{w} \cdot \sqrt{w}\right)}\right)}}\right) + -4.5 \]
   add-sqr-sqrt [<=] 52.1	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{r \cdot w} \cdot \frac{1 - v}{\sqrt{r} \cdot \left(\sqrt{r} \cdot \color{blue}{w}\right)}}\right) + -4.5 \]
   associate-*l* [<=] 52.1	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{r \cdot w} \cdot \frac{1 - v}{\color{blue}{\left(\sqrt{r} \cdot \sqrt{r}\right) \cdot w}}}\right) + -4.5 \]
   add-sqr-sqrt [<=] 99.7	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{r \cdot w} \cdot \frac{1 - v}{\color{blue}{r} \cdot w}}\right) + -4.5 \]

4. Applied egg-rr 99.8%

   \[\leadsto \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\frac{0.375 + v \cdot -0.25}{\frac{1 - v}{r \cdot w}} \cdot \left(r \cdot w\right)}\right) + -4.5 \]
   Step-by-step derivation

   [Start] 99.7	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1}{r \cdot w} \cdot \frac{1 - v}{r \cdot w}}\right) + -4.5 \]
   associate-*l/ [=>] 99.7	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\color{blue}{\frac{1 \cdot \frac{1 - v}{r \cdot w}}{r \cdot w}}}\right) + -4.5 \]
   *-un-lft-identity [<=] 99.7	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{\color{blue}{\frac{1 - v}{r \cdot w}}}{r \cdot w}}\right) + -4.5 \]
   associate-/r/ [=>] 99.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{r \cdot w}} \cdot \left(r \cdot w\right)}\right) + -4.5 \]
   distribute-rgt-in [=>] 99.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\color{blue}{3 \cdot 0.125 + \left(-2 \cdot v\right) \cdot 0.125}}{\frac{1 - v}{r \cdot w}} \cdot \left(r \cdot w\right)\right) + -4.5 \]
   metadata-eval [=>] 99.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\color{blue}{0.375} + \left(-2 \cdot v\right) \cdot 0.125}{\frac{1 - v}{r \cdot w}} \cdot \left(r \cdot w\right)\right) + -4.5 \]
   *-commutative [=>] 99.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.375 + \color{blue}{\left(v \cdot -2\right)} \cdot 0.125}{\frac{1 - v}{r \cdot w}} \cdot \left(r \cdot w\right)\right) + -4.5 \]
   associate-*l* [=>] 99.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.375 + \color{blue}{v \cdot \left(-2 \cdot 0.125\right)}}{\frac{1 - v}{r \cdot w}} \cdot \left(r \cdot w\right)\right) + -4.5 \]
   metadata-eval [=>] 99.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.375 + v \cdot \color{blue}{-0.25}}{\frac{1 - v}{r \cdot w}} \cdot \left(r \cdot w\right)\right) + -4.5 \]

5. Final simplification 99.8%

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

Alternatives

Alternative 1
Accuracy	99.3%
Cost	1737

\[\begin{array}{l} t_0 := 3 + \frac{2}{r \cdot r}\\ \mathbf{if}\;v \leq -57000 \lor \neg \left(v \leq 1.2 \cdot 10^{-20}\right):\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot 0.25\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot \left(0.375 + v \cdot -0.25\right)\right)\right)\right)\\ \end{array} \]

Alternative 2
Accuracy	99.7%
Cost	1728

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

Alternative 3
Accuracy	91.7%
Cost	1616

\[\begin{array}{l} t_0 := \frac{2}{r \cdot r} + \left(\left(r \cdot r\right) \cdot \left(\left(w \cdot w\right) \cdot -0.375\right) - 1.5\right)\\ t_1 := -4.5 + \left(3 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot 0.25\right)\right)\right)\\ \mathbf{if}\;r \leq -1650000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;r \leq -3.6 \cdot 10^{-120}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;r \leq 2.8 \cdot 10^{-71}:\\ \;\;\;\;\frac{\frac{2}{r}}{r}\\ \mathbf{elif}\;r \leq 5.9 \cdot 10^{+25}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 4
Accuracy	93.2%
Cost	1613

\[\begin{array}{l} t_0 := \frac{2}{r \cdot r}\\ \mathbf{if}\;r \leq -28500000000000:\\ \;\;\;\;-4.5 + \left(3 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot 0.25\right)\right)\right)\\ \mathbf{elif}\;r \leq 4 \cdot 10^{+25} \lor \neg \left(r \leq 3.6 \cdot 10^{+145}\right):\\ \;\;\;\;-4.5 + \left(\left(3 + t_0\right) - \left(r \cdot w\right) \cdot \left(0.375 \cdot \left(r \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_0 + \left(-1.5 - 0.25 \cdot \left(\left(r \cdot r\right) \cdot \left(w \cdot w\right)\right)\right)\\ \end{array} \]

Alternative 5
Accuracy	99.3%
Cost	1481

\[\begin{array}{l} t_0 := 3 + \frac{2}{r \cdot r}\\ \mathbf{if}\;v \leq -57000 \lor \neg \left(v \leq 1.2 \cdot 10^{-20}\right):\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot 0.25\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(0.375 \cdot \left(r \cdot w\right)\right)\right)\\ \end{array} \]

Alternative 6
Accuracy	89.7%
Cost	1352

\[\begin{array}{l} t_0 := \frac{2}{r \cdot r}\\ t_1 := -4.5 + \left(3 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot 0.25\right)\right)\right)\\ \mathbf{if}\;r \leq -5.8 \cdot 10^{+159}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;r \leq -1.2 \cdot 10^{-119}:\\ \;\;\;\;t_0 + \left(-1.5 - 0.25 \cdot \left(\left(r \cdot r\right) \cdot \left(w \cdot w\right)\right)\right)\\ \mathbf{elif}\;r \leq 40000:\\ \;\;\;\;t_0 + -1.5\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 7
Accuracy	87.9%
Cost	1097

\[\begin{array}{l} \mathbf{if}\;r \leq -75000 \lor \neg \left(r \leq 8500\right):\\ \;\;\;\;-4.5 + \left(3 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot 0.25\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{r \cdot r} + -1.5\\ \end{array} \]

Alternative 8
Accuracy	73.0%
Cost	841

\[\begin{array}{l} \mathbf{if}\;r \leq -1.12 \cdot 10^{+86} \lor \neg \left(r \leq 4150000000000\right):\\ \;\;\;\;\left(r \cdot r\right) \cdot \left(\left(w \cdot w\right) \cdot -0.375\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{r \cdot r} + -1.5\\ \end{array} \]

Alternative 9
Accuracy	72.7%
Cost	841

\[\begin{array}{l} \mathbf{if}\;r \leq -3.6 \cdot 10^{+85} \lor \neg \left(r \leq 11000000000000\right):\\ \;\;\;\;\left(r \cdot r\right) \cdot \left(-0.25 \cdot \left(w \cdot w\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{r \cdot r} + -1.5\\ \end{array} \]

Alternative 10
Accuracy	56.0%
Cost	584

\[\begin{array}{l} \mathbf{if}\;r \leq -1.16:\\ \;\;\;\;-1.5\\ \mathbf{elif}\;r \leq 1.56 \cdot 10^{-6}:\\ \;\;\;\;\frac{2}{r \cdot r}\\ \mathbf{else}:\\ \;\;\;\;-1.5\\ \end{array} \]

Alternative 11
Accuracy	56.0%
Cost	584

\[\begin{array}{l} \mathbf{if}\;r \leq -1.16:\\ \;\;\;\;-1.5\\ \mathbf{elif}\;r \leq 1.56 \cdot 10^{-6}:\\ \;\;\;\;\frac{\frac{2}{r}}{r}\\ \mathbf{else}:\\ \;\;\;\;-1.5\\ \end{array} \]

Alternative 12
Accuracy	56.8%
Cost	448

\[\frac{2}{r \cdot r} + -1.5 \]

Alternative 13
Accuracy	13.9%
Cost	64

\[-1.5 \]

Reproduce

herbie shell --seed 2023160 
(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))