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The method presents the solutions in terms of convergent series with easily computable components and improves the convergence of the series solution.
This generally improves the convergence drastically compared with GMRES m).
This combination significantly improves the convergence characteristics of the original algorithm.
The simulation results show that the algorithm improves the convergence speed and system performance through improving users' SINR.
The regularization step not only improves the convergence rate of the presented algorithm, but also increases its stability bound.
A deterministic anti-annealing expectation maximization (DAAEM) algorithm not only prevents poor local optima, but also improves the convergence speed.
Also, it is shown that using the graph theory greatly improves the convergence rate of the optimization.
This improves the convergence of perturbative results, and also the stability by which nonperturbative parameters encode the underlying soft physics.
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An external archive is used to improve the convergence speed.
The design of approaches to improve the convergence of the COA is a challenging issue.
Moreover, understanding the form of the error allows creation of extrapolation processes to improve the convergence behaviour of the numerical method.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com