\[\left(\alpha > -1 \land \beta > -1\right) \land i > 1\]
\[ \begin{array}{c}[alpha, beta] = \mathsf{sort}([alpha, beta])\\ \end{array} \]
\[\frac{\frac{\left(i \cdot \left(\left(\alpha + \beta\right) + i\right)\right) \cdot \left(\beta \cdot \alpha + i \cdot \left(\left(\alpha + \beta\right) + i\right)\right)}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right)}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right) - 1}
\]
↓
\[\begin{array}{l}
t_0 := \frac{i}{\mathsf{fma}\left(i, 2, \beta\right)}\\
\left(t_0 \cdot \frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right) + 1}\right) \cdot \left(t_0 \cdot \frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right) + \left(\alpha + -1\right)}\right)
\end{array}
\]
(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 (/ i (fma i 2.0 beta))))
(*
(* t_0 (/ (+ i beta) (+ (fma i 2.0 beta) 1.0)))
(* t_0 (/ (+ i beta) (+ (fma i 2.0 beta) (+ alpha -1.0)))))))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 = i / fma(i, 2.0, beta);
return (t_0 * ((i + beta) / (fma(i, 2.0, beta) + 1.0))) * (t_0 * ((i + beta) / (fma(i, 2.0, beta) + (alpha + -1.0))));
}
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 = Float64(i / fma(i, 2.0, beta))
return Float64(Float64(t_0 * Float64(Float64(i + beta) / Float64(fma(i, 2.0, beta) + 1.0))) * Float64(t_0 * Float64(Float64(i + beta) / Float64(fma(i, 2.0, beta) + Float64(alpha + -1.0)))))
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 / N[(i * 2.0 + beta), $MachinePrecision]), $MachinePrecision]}, N[(N[(t$95$0 * N[(N[(i + beta), $MachinePrecision] / N[(N[(i * 2.0 + beta), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * N[(N[(i + beta), $MachinePrecision] / N[(N[(i * 2.0 + beta), $MachinePrecision] + N[(alpha + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $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 := \frac{i}{\mathsf{fma}\left(i, 2, \beta\right)}\\
\left(t_0 \cdot \frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right) + 1}\right) \cdot \left(t_0 \cdot \frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right) + \left(\alpha + -1\right)}\right)
\end{array}
Alternatives
| Alternative 1 |
|---|
| Error | 2.1 |
|---|
| Cost | 27712 |
|---|
\[\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right) + 1}\right) \cdot \left(i \cdot \frac{\frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right)}}{\mathsf{fma}\left(i, 2, \beta\right) + \left(\alpha + -1\right)}\right)
\]
| Alternative 2 |
|---|
| Error | 9.9 |
|---|
| Cost | 14532 |
|---|
\[\begin{array}{l}
t_0 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
\mathbf{if}\;\beta \leq 3.7 \cdot 10^{+185}:\\
\;\;\;\;\left(0.0625 + 0.0625 \cdot \frac{2 \cdot \beta + 2 \cdot \alpha}{i}\right) + \frac{\beta + \alpha}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\left(\frac{i}{t_0} \cdot \frac{i + \left(\beta + \alpha\right)}{t_0}\right) \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 3 |
|---|
| Error | 9.8 |
|---|
| Cost | 7364 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 1.15 \cdot 10^{+185}:\\
\;\;\;\;\left(0.0625 + 0.0625 \cdot \frac{2 \cdot \beta + 2 \cdot \alpha}{i}\right) + \frac{\beta + \alpha}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 4 |
|---|
| Error | 9.9 |
|---|
| Cost | 1476 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 9.8 \cdot 10^{+185}:\\
\;\;\;\;\left(0.0625 + 0.0625 \cdot \frac{2 \cdot \beta + 2 \cdot \alpha}{i}\right) + \frac{\beta + \alpha}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 5 |
|---|
| Error | 10.0 |
|---|
| Cost | 1092 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3.5 \cdot 10^{+185}:\\
\;\;\;\;\left(0.0625 + \beta \cdot \frac{0.125}{i}\right) + \frac{\beta + \alpha}{i} \cdot -0.125\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 6 |
|---|
| Error | 9.9 |
|---|
| Cost | 708 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 8.4 \cdot 10^{+184}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i + \alpha}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 7 |
|---|
| Error | 10.0 |
|---|
| Cost | 708 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 2.6 \cdot 10^{+185}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 8 |
|---|
| Error | 16.8 |
|---|
| Cost | 580 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3 \cdot 10^{+224}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\alpha \cdot \frac{\frac{i}{\beta}}{\beta}\\
\end{array}
\]
| Alternative 9 |
|---|
| Error | 15.0 |
|---|
| Cost | 580 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 5.8 \cdot 10^{+191}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;i \cdot \frac{\frac{i}{\beta}}{\beta}\\
\end{array}
\]
| Alternative 10 |
|---|
| Error | 11.2 |
|---|
| Cost | 580 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3 \cdot 10^{+185}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 11 |
|---|
| Error | 16.8 |
|---|
| Cost | 324 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3 \cdot 10^{+224}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{0}{i}\\
\end{array}
\]
| Alternative 12 |
|---|
| Error | 18.8 |
|---|
| Cost | 64 |
|---|
\[0.0625
\]