\[\left(\alpha > -1 \land \beta > -1\right) \land i > 1\]
\[ \begin{array}{c}[alpha, beta] = \mathsf{sort}([alpha, beta])\\ \end{array} \]
\[\frac{\frac{\left(i \cdot \left(\left(\alpha + \beta\right) + i\right)\right) \cdot \left(\beta \cdot \alpha + i \cdot \left(\left(\alpha + \beta\right) + i\right)\right)}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right)}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right) - 1}
\]
↓
\[\frac{\frac{\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)}}{\frac{1 + \mathsf{fma}\left(i, 2, \beta + \alpha\right)}{i + \beta}} \cdot \frac{i}{\mathsf{fma}\left(i, 2, \beta\right) + -1}}{\frac{\mathsf{fma}\left(i, 2, \beta\right)}{i + \beta}}
\]
(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
(/
(*
(/ (/ i (fma i 2.0 beta)) (/ (+ 1.0 (fma i 2.0 (+ beta alpha))) (+ i beta)))
(/ i (+ (fma i 2.0 beta) -1.0)))
(/ (fma i 2.0 beta) (+ i beta))))
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) {
return (((i / fma(i, 2.0, beta)) / ((1.0 + fma(i, 2.0, (beta + alpha))) / (i + beta))) * (i / (fma(i, 2.0, beta) + -1.0))) / (fma(i, 2.0, beta) / (i + beta));
}
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)
return Float64(Float64(Float64(Float64(i / fma(i, 2.0, beta)) / Float64(Float64(1.0 + fma(i, 2.0, Float64(beta + alpha))) / Float64(i + beta))) * Float64(i / Float64(fma(i, 2.0, beta) + -1.0))) / Float64(fma(i, 2.0, beta) / Float64(i + beta)))
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_] := N[(N[(N[(N[(i / N[(i * 2.0 + beta), $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 + N[(i * 2.0 + N[(beta + alpha), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(i + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(i / N[(N[(i * 2.0 + beta), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(i * 2.0 + beta), $MachinePrecision] / N[(i + beta), $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}
↓
\frac{\frac{\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)}}{\frac{1 + \mathsf{fma}\left(i, 2, \beta + \alpha\right)}{i + \beta}} \cdot \frac{i}{\mathsf{fma}\left(i, 2, \beta\right) + -1}}{\frac{\mathsf{fma}\left(i, 2, \beta\right)}{i + \beta}}
Alternatives
| Alternative 1 |
|---|
| Error | 96.8% |
|---|
| Cost | 27712.00 |
|---|
\[\frac{\frac{i}{\frac{\mathsf{fma}\left(i, 2, \beta\right)}{i + \beta}}}{1 + \mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right) + -1} \cdot \frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right)}\right)
\]
| Alternative 2 |
|---|
| Error | 90.0% |
|---|
| Cost | 27396.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;i \leq 1.1 \cdot 10^{+152}:\\
\;\;\;\;\frac{\frac{{\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \left(i + \beta\right)\right)}^{2}}{\alpha + \left(\mathsf{fma}\left(i, 2, \beta\right) + -1\right)}}{1 + \left(\mathsf{fma}\left(i, 2, \beta\right) + \alpha\right)}\\
\mathbf{else}:\\
\;\;\;\;\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \beta}{\beta + i \cdot 2}\right) \cdot 0.25\\
\end{array}
\]
| Alternative 3 |
|---|
| Error | 84.5% |
|---|
| Cost | 14544.00 |
|---|
\[\begin{array}{l}
t_0 := \left(\beta + \alpha\right) \cdot \frac{0.125}{i}\\
\mathbf{if}\;\beta \leq 6.2 \cdot 10^{+128}:\\
\;\;\;\;\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \beta}{\beta + i \cdot 2}\right) \cdot 0.25\\
\mathbf{elif}\;\beta \leq 5.3 \cdot 10^{+143}:\\
\;\;\;\;\frac{i}{\frac{\beta \cdot \beta}{i + \alpha}}\\
\mathbf{elif}\;\beta \leq 3 \cdot 10^{+162}:\\
\;\;\;\;\frac{{\left(0.0625 + t_0\right)}^{2} + t_0 \cdot \left(\left(\beta + \alpha\right) \cdot \frac{-0.125}{i}\right)}{0.0625 + \left(t_0 + t_0\right)}\\
\mathbf{elif}\;\beta \leq 3 \cdot 10^{+206}:\\
\;\;\;\;\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right) + -1} \cdot \frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right)}\right) \cdot \frac{i}{\beta}\\
\mathbf{else}:\\
\;\;\;\;\frac{i \cdot {\beta}^{-1}}{\beta \cdot \frac{1}{i + \alpha}}\\
\end{array}
\]
| Alternative 4 |
|---|
| Error | 85.1% |
|---|
| Cost | 14532.00 |
|---|
\[\begin{array}{l}
t_0 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
t_1 := \frac{i}{t_0}\\
\mathbf{if}\;\beta \leq 6.4 \cdot 10^{+128}:\\
\;\;\;\;\left(t_1 \cdot \frac{i + \beta}{\beta + i \cdot 2}\right) \cdot 0.25\\
\mathbf{else}:\\
\;\;\;\;\left(t_1 \cdot \frac{i + \left(\beta + \alpha\right)}{t_0}\right) \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 5 |
|---|
| Error | 85.0% |
|---|
| Cost | 7748.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 7.2 \cdot 10^{+128}:\\
\;\;\;\;\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \beta}{\beta + i \cdot 2}\right) \cdot 0.25\\
\mathbf{else}:\\
\;\;\;\;\frac{i \cdot {\beta}^{-1}}{\beta \cdot \frac{1}{i + \alpha}}\\
\end{array}
\]
| Alternative 6 |
|---|
| Error | 84.9% |
|---|
| Cost | 7300.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 2.85 \cdot 10^{+128}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i \cdot {\beta}^{-1}}{\beta \cdot \frac{1}{i + \alpha}}\\
\end{array}
\]
| Alternative 7 |
|---|
| Error | 84.9% |
|---|
| Cost | 708.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 1.65 \cdot 10^{+128}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\beta} \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 8 |
|---|
| Error | 77.1% |
|---|
| Cost | 580.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 6.3 \cdot 10^{+162}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;i \cdot \frac{\frac{i}{\beta}}{\beta}\\
\end{array}
\]
| Alternative 9 |
|---|
| Error | 83.1% |
|---|
| Cost | 580.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 2.5 \cdot 10^{+162}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 10 |
|---|
| Error | 73.5% |
|---|
| Cost | 196.00 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3.6 \cdot 10^{+214}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;0\\
\end{array}
\]
| Alternative 11 |
|---|
| Error | 10.1% |
|---|
| Cost | 64.00 |
|---|
\[0
\]