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Due to the monotonicity of g, the optimal value η(τ s ∗) which occurs at g=0 can be found by using the bisection algorithm.
However, we can obtain its optimal solution easily by using the bisection search technique since the throughput function is quite smooth [35].
Therefore, μ ¯ b can be efficiently determined iteratively to satisfy ∑ l = 1 N c ∑ k = 1 K p b, k, l = T p b by using the bisection method.
An inner bound can then be found by using the bisection technique described above to find the largest value of for which maximizing the approximated objective yields a valid power allocation.
Remark 2. Notice that when other variables are fixed, can be optimized by solving the Karush-Kuhn-Tucker (KKT) conditions, where the Lagrangian multiplier that arises due to the relay power constraint can be obtained by using the bisection algorithm like in [15].
From Theorem 3.5, by Rules (i) and (ii) the main computational effort for deriving (R[p,q]) is to solve some univariate equations about the variables ({hat{alpha }}_{k}^{i}) and (hat{beta }_{k}^{i}), which is easy to solve, for example, by using the bisection approach.
Similar(54)
After a preprocessing step (mean removing and RGB to YCbCr transformation), the DCT transform is applied and followed by an iterative phase (using the bisection method) including the thresholding, the quantization, dequantization, the inverse DCT, YCbCr to RGB transform and the mean recovering.
The optimal power allocation is computed using the bisection method (relative error below 10−5).
Then, the jammer uses the bisection method to find the α that satisfies Equation 20 and computes the NBS-based power allocation solution by using Equation 19.
Thus, we can use the bisection method to find the optimal global multiplier μ [19].
We use the bisection method to solve this constrained optimization problem.
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