\[\left(\alpha > -1 \land \beta > -1\right) \land i > 1\]
\[ \begin{array}{c}[alpha, beta] = \mathsf{sort}([alpha, beta])\\ \end{array} \]
\[\frac{\frac{\left(i \cdot \left(\left(\alpha + \beta\right) + i\right)\right) \cdot \left(\beta \cdot \alpha + i \cdot \left(\left(\alpha + \beta\right) + i\right)\right)}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right)}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right) - 1}
\]
↓
\[\begin{array}{l}
t_0 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
\mathbf{if}\;\beta \leq 2.5 \cdot 10^{+157}:\\
\;\;\;\;\left(0.0625 + 0.125 \cdot \frac{\beta}{i}\right) + \frac{\beta}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\frac{-1 + t_0}{i + \alpha}}}{1 + t_0}\\
\end{array}
\]
(FPCore (alpha beta i)
:precision binary64
(/
(/
(* (* i (+ (+ alpha beta) i)) (+ (* beta alpha) (* i (+ (+ alpha beta) i))))
(* (+ (+ alpha beta) (* 2.0 i)) (+ (+ alpha beta) (* 2.0 i))))
(- (* (+ (+ alpha beta) (* 2.0 i)) (+ (+ alpha beta) (* 2.0 i))) 1.0)))
↓
(FPCore (alpha beta i)
:precision binary64
(let* ((t_0 (fma i 2.0 (+ beta alpha))))
(if (<= beta 2.5e+157)
(+ (+ 0.0625 (* 0.125 (/ beta i))) (* (/ beta i) -0.125))
(/ (/ i (/ (+ -1.0 t_0) (+ i alpha))) (+ 1.0 t_0)))))double code(double alpha, double beta, double i) {
return (((i * ((alpha + beta) + i)) * ((beta * alpha) + (i * ((alpha + beta) + i)))) / (((alpha + beta) + (2.0 * i)) * ((alpha + beta) + (2.0 * i)))) / ((((alpha + beta) + (2.0 * i)) * ((alpha + beta) + (2.0 * i))) - 1.0);
}
↓
double code(double alpha, double beta, double i) {
double t_0 = fma(i, 2.0, (beta + alpha));
double tmp;
if (beta <= 2.5e+157) {
tmp = (0.0625 + (0.125 * (beta / i))) + ((beta / i) * -0.125);
} else {
tmp = (i / ((-1.0 + t_0) / (i + alpha))) / (1.0 + t_0);
}
return tmp;
}
function code(alpha, beta, i)
return Float64(Float64(Float64(Float64(i * Float64(Float64(alpha + beta) + i)) * Float64(Float64(beta * alpha) + Float64(i * Float64(Float64(alpha + beta) + i)))) / Float64(Float64(Float64(alpha + beta) + Float64(2.0 * i)) * Float64(Float64(alpha + beta) + Float64(2.0 * i)))) / Float64(Float64(Float64(Float64(alpha + beta) + Float64(2.0 * i)) * Float64(Float64(alpha + beta) + Float64(2.0 * i))) - 1.0))
end
↓
function code(alpha, beta, i)
t_0 = fma(i, 2.0, Float64(beta + alpha))
tmp = 0.0
if (beta <= 2.5e+157)
tmp = Float64(Float64(0.0625 + Float64(0.125 * Float64(beta / i))) + Float64(Float64(beta / i) * -0.125));
else
tmp = Float64(Float64(i / Float64(Float64(-1.0 + t_0) / Float64(i + alpha))) / Float64(1.0 + t_0));
end
return tmp
end
code[alpha_, beta_, i_] := N[(N[(N[(N[(i * N[(N[(alpha + beta), $MachinePrecision] + i), $MachinePrecision]), $MachinePrecision] * N[(N[(beta * alpha), $MachinePrecision] + N[(i * N[(N[(alpha + beta), $MachinePrecision] + i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision] * N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision] * N[(N[(alpha + beta), $MachinePrecision] + N[(2.0 * i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]
↓
code[alpha_, beta_, i_] := Block[{t$95$0 = N[(i * 2.0 + N[(beta + alpha), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[beta, 2.5e+157], N[(N[(0.0625 + N[(0.125 * N[(beta / i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(beta / i), $MachinePrecision] * -0.125), $MachinePrecision]), $MachinePrecision], N[(N[(i / N[(N[(-1.0 + t$95$0), $MachinePrecision] / N[(i + alpha), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(1.0 + t$95$0), $MachinePrecision]), $MachinePrecision]]]
\frac{\frac{\left(i \cdot \left(\left(\alpha + \beta\right) + i\right)\right) \cdot \left(\beta \cdot \alpha + i \cdot \left(\left(\alpha + \beta\right) + i\right)\right)}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right)}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right) - 1}
↓
\begin{array}{l}
t_0 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
\mathbf{if}\;\beta \leq 2.5 \cdot 10^{+157}:\\
\;\;\;\;\left(0.0625 + 0.125 \cdot \frac{\beta}{i}\right) + \frac{\beta}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\frac{-1 + t_0}{i + \alpha}}}{1 + t_0}\\
\end{array}
Alternatives
| Alternative 1 |
|---|
| Error | 86.4% |
|---|
| Cost | 14276.00 |
|---|
\[\begin{array}{l}
t_0 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
\mathbf{if}\;\beta \leq 2.45 \cdot 10^{+157}:\\
\;\;\;\;\left(0.0625 + 0.125 \cdot \frac{\beta}{i}\right) + \frac{\beta}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\frac{i + \alpha}{1 + t_0} \cdot \frac{i}{-1 + t_0}\\
\end{array}
\]
| Alternative 2 |
|---|
| Error | 85.7% |
|---|
| Cost | 7364.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3.2 \cdot 10^{+157}:\\
\;\;\;\;\left(0.0625 + 0.125 \cdot \frac{\beta}{i}\right) + \frac{\beta}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 3 |
|---|
| Error | 85.6% |
|---|
| Cost | 964.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 7.8 \cdot 10^{+157}:\\
\;\;\;\;\left(0.0625 + 0.125 \cdot \frac{\beta}{i}\right) + \frac{\beta}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta + \alpha}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 4 |
|---|
| Error | 85.6% |
|---|
| Cost | 836.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 7.8 \cdot 10^{+158}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta + \alpha}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 5 |
|---|
| Error | 85.6% |
|---|
| Cost | 708.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 1.35 \cdot 10^{+158}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i + \alpha}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 6 |
|---|
| Error | 83.1% |
|---|
| Cost | 580.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 1.4 \cdot 10^{+194}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 7 |
|---|
| Error | 74.3% |
|---|
| Cost | 196.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 1.12 \cdot 10^{+245}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;0\\
\end{array}
\]
| Alternative 8 |
|---|
| Error | 10.0% |
|---|
| Cost | 64.00 |
|---|
\[0
\]