\[\left(\alpha > -1 \land \beta > -1\right) \land i > 1\]
\[ \begin{array}{c}[alpha, beta] = \mathsf{sort}([alpha, beta])\\ \end{array} \]
\[\frac{\frac{\left(i \cdot \left(\left(\alpha + \beta\right) + i\right)\right) \cdot \left(\beta \cdot \alpha + i \cdot \left(\left(\alpha + \beta\right) + i\right)\right)}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right)}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right) - 1}
\]
↓
\[\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \frac{i + \beta}{\left(\beta + 1\right) + i \cdot 2}\right) \cdot \frac{\frac{i}{\frac{\mathsf{fma}\left(i, 2, \beta\right)}{i + \beta}}}{\alpha + \left(\mathsf{fma}\left(i, 2, \beta\right) + -1\right)}
\]
(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)) (/ (+ i beta) (+ (+ beta 1.0) (* i 2.0))))
(/
(/ i (/ (fma i 2.0 beta) (+ i beta)))
(+ alpha (+ (fma i 2.0 beta) -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) {
return ((i / fma(i, 2.0, beta)) * ((i + beta) / ((beta + 1.0) + (i * 2.0)))) * ((i / (fma(i, 2.0, beta) / (i + beta))) / (alpha + (fma(i, 2.0, beta) + -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)
return Float64(Float64(Float64(i / fma(i, 2.0, beta)) * Float64(Float64(i + beta) / Float64(Float64(beta + 1.0) + Float64(i * 2.0)))) * Float64(Float64(i / Float64(fma(i, 2.0, beta) / Float64(i + beta))) / Float64(alpha + Float64(fma(i, 2.0, beta) + -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_] := N[(N[(N[(i / N[(i * 2.0 + beta), $MachinePrecision]), $MachinePrecision] * N[(N[(i + beta), $MachinePrecision] / N[(N[(beta + 1.0), $MachinePrecision] + N[(i * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(N[(i / N[(N[(i * 2.0 + beta), $MachinePrecision] / N[(i + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(alpha + N[(N[(i * 2.0 + beta), $MachinePrecision] + -1.0), $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}
↓
\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \frac{i + \beta}{\left(\beta + 1\right) + i \cdot 2}\right) \cdot \frac{\frac{i}{\frac{\mathsf{fma}\left(i, 2, \beta\right)}{i + \beta}}}{\alpha + \left(\mathsf{fma}\left(i, 2, \beta\right) + -1\right)}
Alternatives
| Alternative 1 |
|---|
| Error | 9.3 |
|---|
| Cost | 14532 |
|---|
\[\begin{array}{l}
t_0 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
t_1 := \frac{i}{t_0} \cdot \frac{i + \left(\beta + \alpha\right)}{t_0}\\
\mathbf{if}\;\beta \leq 6.5 \cdot 10^{+181}:\\
\;\;\;\;t_1 \cdot 0.25\\
\mathbf{else}:\\
\;\;\;\;t_1 \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 2 |
|---|
| Error | 9.3 |
|---|
| Cost | 14532 |
|---|
\[\begin{array}{l}
t_0 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
t_1 := \frac{i + \left(\beta + \alpha\right)}{t_0}\\
\mathbf{if}\;\beta \leq 7 \cdot 10^{+181}:\\
\;\;\;\;\left(\frac{i}{t_0} \cdot t_1\right) \cdot 0.25\\
\mathbf{else}:\\
\;\;\;\;\frac{t_1}{\frac{t_0}{i}} \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 3 |
|---|
| Error | 9.3 |
|---|
| Cost | 14276 |
|---|
\[\begin{array}{l}
t_0 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
t_1 := \frac{i}{t_0}\\
\mathbf{if}\;\beta \leq 3.8 \cdot 10^{+181}:\\
\;\;\;\;\left(t_1 \cdot \frac{i + \left(\beta + \alpha\right)}{t_0}\right) \cdot 0.25\\
\mathbf{else}:\\
\;\;\;\;t_1 \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 4 |
|---|
| Error | 9.4 |
|---|
| Cost | 7364 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3.6 \cdot 10^{+181}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 5 |
|---|
| Error | 9.4 |
|---|
| Cost | 708 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3.5 \cdot 10^{+181}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i + \alpha}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 6 |
|---|
| Error | 14.5 |
|---|
| Cost | 580 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 4.5 \cdot 10^{+196}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i \cdot i}{\beta}}{\beta}\\
\end{array}
\]
| Alternative 7 |
|---|
| Error | 15.5 |
|---|
| Cost | 196 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3.9 \cdot 10^{+217}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;0\\
\end{array}
\]
| Alternative 8 |
|---|
| Error | 57.4 |
|---|
| Cost | 64 |
|---|
\[0
\]