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In many practical applications, we have to stop the distributed algorithm after a finite number of iterations before converging the algorithm.
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The originality of the method relies first on constraining the domain of the most probable crystals, before converging towards the predicted packing by a double genetic-refining optimization algorithm.
After convergence of the algorithm, if the value of the local SNR γ1 decreases from 0 dB to -10 dB, the algorithm requires an average of 1200 iterations before converging to the new optimal solution M1 = M2 = 1.
If this NLP does not converge, the entire algorithm might fail.
The output activity approaches targets 3, 4, and 1 successively before converging to the fixed point.
Looking at (44), it is obvious that before the algorithm converges, σ e (n) is large compared to σ v and, consequently, α NPVSS(n)≈1.
The algorithm of sequential recognition is obtained on the basis of the recursive recognition approach presented in [14] applied to accomplishing the different hypotheses in order to identify an optimal hypothesis before the algorithm converges.
Since γ is the average of the SINR values obtained independently in all N BSs, the intermediate values of γ may not be feasible for all BSs before the algorithm converges.
Assuming that in a specific scenario action a i alleviates the aggressive behavior of a specific flow while keeping the throughput at its maximum possible, and τ iteration is needed before the algorithm converges.
STEP-4 If c xy (i, j) has converged, then terminate the algorithm.
The result is a rapidly converging algorithm that provides a decent approximation to the Karcher mean.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com