\[\left(\alpha > -1 \land \beta > -1\right) \land i > 1\]
\[ \begin{array}{c}[alpha, beta] = \mathsf{sort}([alpha, beta])\\ \end{array} \]
\[\frac{\frac{\left(i \cdot \left(\left(\alpha + \beta\right) + i\right)\right) \cdot \left(\beta \cdot \alpha + i \cdot \left(\left(\alpha + \beta\right) + i\right)\right)}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right)}}{\left(\left(\alpha + \beta\right) + 2 \cdot i\right) \cdot \left(\left(\alpha + \beta\right) + 2 \cdot i\right) - 1}
\]
↓
\[\frac{i \cdot \frac{\frac{\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \left(i + \beta\right)}{\mathsf{fma}\left(i, 2, \beta\right) + \left(-1 + \alpha\right)}}{\frac{\mathsf{fma}\left(i, 2, \beta\right) + 1}{i + \beta}}}{\mathsf{fma}\left(i, 2, \beta\right)}
\]
(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
(/
(/
(* (/ i (fma i 2.0 beta)) (+ i beta))
(+ (fma i 2.0 beta) (+ -1.0 alpha)))
(/ (+ (fma i 2.0 beta) 1.0) (+ i beta))))
(fma i 2.0 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 * ((((i / fma(i, 2.0, beta)) * (i + beta)) / (fma(i, 2.0, beta) + (-1.0 + alpha))) / ((fma(i, 2.0, beta) + 1.0) / (i + beta)))) / fma(i, 2.0, 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(i * Float64(Float64(Float64(Float64(i / fma(i, 2.0, beta)) * Float64(i + beta)) / Float64(fma(i, 2.0, beta) + Float64(-1.0 + alpha))) / Float64(Float64(fma(i, 2.0, beta) + 1.0) / Float64(i + beta)))) / fma(i, 2.0, 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[(i * N[(N[(N[(N[(i / N[(i * 2.0 + beta), $MachinePrecision]), $MachinePrecision] * N[(i + beta), $MachinePrecision]), $MachinePrecision] / N[(N[(i * 2.0 + beta), $MachinePrecision] + N[(-1.0 + alpha), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(i * 2.0 + beta), $MachinePrecision] + 1.0), $MachinePrecision] / N[(i + beta), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(i * 2.0 + beta), $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{i \cdot \frac{\frac{\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \left(i + \beta\right)}{\mathsf{fma}\left(i, 2, \beta\right) + \left(-1 + \alpha\right)}}{\frac{\mathsf{fma}\left(i, 2, \beta\right) + 1}{i + \beta}}}{\mathsf{fma}\left(i, 2, \beta\right)}
Alternatives
| Alternative 1 |
|---|
| Error | 2.2 |
|---|
| Cost | 21440 |
|---|
\[\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \frac{i + \beta}{\left(\beta + 1\right) + i \cdot 2}\right) \cdot \frac{\frac{i}{\frac{\mathsf{fma}\left(i, 2, \beta\right)}{i + \beta}}}{\alpha + \left(\mathsf{fma}\left(i, 2, \beta\right) + -1\right)}
\]
| Alternative 2 |
|---|
| Error | 2.2 |
|---|
| Cost | 21312 |
|---|
\[\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \frac{i + \beta}{\left(\beta + 1\right) + i \cdot 2}\right) \cdot \left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right) + -1} \cdot \frac{i + \beta}{\mathsf{fma}\left(i, 2, \beta\right)}\right)
\]
| Alternative 3 |
|---|
| Error | 9.7 |
|---|
| Cost | 14796 |
|---|
\[\begin{array}{l}
t_0 := \left(\beta + \alpha\right) + i \cdot 2\\
t_1 := \mathsf{fma}\left(i, 2, \beta + \alpha\right)\\
t_2 := \frac{i}{t_1}\\
t_3 := \left(t_2 \cdot \frac{i + \beta}{\beta + i \cdot 2}\right) \cdot 0.25\\
\mathbf{if}\;\beta \leq 3 \cdot 10^{+102}:\\
\;\;\;\;t_3\\
\mathbf{elif}\;\beta \leq 3.5 \cdot 10^{+140}:\\
\;\;\;\;\frac{\frac{i \cdot i}{\frac{\beta \cdot \beta + 4 \cdot \left(i \cdot i + i \cdot \beta\right)}{{\left(i + \beta\right)}^{2}}}}{-1 + t_0 \cdot t_0}\\
\mathbf{elif}\;\beta \leq 3.6 \cdot 10^{+157}:\\
\;\;\;\;t_3\\
\mathbf{else}:\\
\;\;\;\;\left(t_2 \cdot \frac{i + \left(\beta + \alpha\right)}{t_1}\right) \cdot \frac{i + \alpha}{\beta}\\
\end{array}
\]
| Alternative 4 |
|---|
| Error | 9.7 |
|---|
| Cost | 9224 |
|---|
\[\begin{array}{l}
t_0 := \left(\beta + \alpha\right) + i \cdot 2\\
t_1 := \left(\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \beta}{\beta + i \cdot 2}\right) \cdot 0.25\\
\mathbf{if}\;\beta \leq 3.7 \cdot 10^{+102}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;\beta \leq 2 \cdot 10^{+140}:\\
\;\;\;\;\frac{\frac{i \cdot i}{\frac{\beta \cdot \beta + 4 \cdot \left(i \cdot i + i \cdot \beta\right)}{{\left(i + \beta\right)}^{2}}}}{-1 + t_0 \cdot t_0}\\
\mathbf{elif}\;\beta \leq 8.5 \cdot 10^{+158}:\\
\;\;\;\;t_1\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 5 |
|---|
| Error | 9.8 |
|---|
| Cost | 9032 |
|---|
\[\begin{array}{l}
t_0 := \beta + i \cdot 2\\
t_1 := \left(\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \beta}{t_0}\right) \cdot 0.25\\
\mathbf{if}\;\beta \leq 2.05 \cdot 10^{+103}:\\
\;\;\;\;t_1\\
\mathbf{elif}\;\beta \leq 7.5 \cdot 10^{+140}:\\
\;\;\;\;\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta\right)} \cdot \frac{i + \beta}{\left(\beta + 1\right) + i \cdot 2}\right) \cdot \frac{i \cdot \left(i + \beta\right)}{t_0 \cdot \left(-1 + t_0\right)}\\
\mathbf{elif}\;\beta \leq 1.28 \cdot 10^{+160}:\\
\;\;\;\;t_1\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 6 |
|---|
| Error | 9.6 |
|---|
| Cost | 7748 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 2.6 \cdot 10^{+123}:\\
\;\;\;\;\left(\frac{i}{\mathsf{fma}\left(i, 2, \beta + \alpha\right)} \cdot \frac{i + \beta}{\beta + i \cdot 2}\right) \cdot 0.25\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 7 |
|---|
| Error | 11.6 |
|---|
| Cost | 844 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 1.3 \cdot 10^{+123}:\\
\;\;\;\;0.0625\\
\mathbf{elif}\;\beta \leq 2.15 \cdot 10^{+192}:\\
\;\;\;\;\left(i + \alpha\right) \cdot \frac{\frac{i}{\beta}}{\beta}\\
\mathbf{elif}\;\beta \leq 8 \cdot 10^{+216}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 8 |
|---|
| Error | 9.6 |
|---|
| Cost | 708 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 1.9 \cdot 10^{+123}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i + \alpha}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 9 |
|---|
| Error | 9.6 |
|---|
| Cost | 708 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 3 \cdot 10^{+123}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{\frac{i}{\beta}}{\frac{\beta}{i + \alpha}}\\
\end{array}
\]
| Alternative 10 |
|---|
| Error | 16.4 |
|---|
| Cost | 580 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 9.8 \cdot 10^{+223}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;i \cdot \frac{i}{\beta \cdot \beta}\\
\end{array}
\]
| Alternative 11 |
|---|
| Error | 15.5 |
|---|
| Cost | 580 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 1.15 \cdot 10^{+224}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\beta} \cdot \frac{\alpha}{\beta}\\
\end{array}
\]
| Alternative 12 |
|---|
| Error | 11.2 |
|---|
| Cost | 580 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 2.7 \cdot 10^{+123}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;\frac{i}{\beta} \cdot \frac{i}{\beta}\\
\end{array}
\]
| Alternative 13 |
|---|
| Error | 17.3 |
|---|
| Cost | 452 |
|---|
\[\begin{array}{l}
\mathbf{if}\;\beta \leq 9 \cdot 10^{+246}:\\
\;\;\;\;0.0625\\
\mathbf{else}:\\
\;\;\;\;0.25 \cdot \frac{\alpha}{\beta}\\
\end{array}
\]
| Alternative 14 |
|---|
| Error | 18.5 |
|---|
| Cost | 64 |
|---|
\[0.0625
\]