Latest Research News on Convergence Analysis: Oct – 2019

Convergence analysis of canonical genetic algorithms

This paper analyzes the convergence properties of the canonical genetic algorithmic rule (CGA) with mutation, crossover and proportional copy applied to static optimisation issues. it’s verified by suggests that of homogenized finite Markov chain analysis that a CGA can ne’er converge to the world optimum notwithstanding the data format, crossover, operator and objective operate. however variants of CGA’s that continually maintain the simplest answer within the population, either before or when choice, ar shown to converge to the world optimum thanks to the irreducibility property of the underlying original parallel CGA. These results ar mentioned with relevance the schema theorem. [1]

The particle swarm optimization algorithm: convergence analysis and parameter selection

The particle swarm optimisation algorithmic program is analyzed exploitation normal results from the dynamic system theory. Graphical parameter choice pointers square measure derived. The exploration–exploitation exchange is mentioned and illustrated. samples of performance on benchmark functions superior to antecedently printed results square measure given. [2]

Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations

This paper presents convergence analysis of some algorithms for finding systems of nonlinear equations outlined by domestically Lipschitzian functions. For the directional derivative-based and also the generalized Jacobian-based Newton ways, each the iterates and also the corresponding operate values square measure domestically, superlinearly confluent. Globally, a limiting purpose of the ingeminate sequence generated by the damped, directional derivative-based Newton technique could be a zero of the system if and providing the ingeminate sequence converges to the current purpose and also the stepsize eventually becomes one, as long as the system is powerfully BD-regular and semismooth at now. during this case, the convergence is superlinear. A general attraction theorem is bestowed, which might be applied to 2 algorithms projected by dynasty, Pang and Rangaraj. [3]

A joint matrix minimization approach for seismic wavefield recovery

Reconstruction of the seismal wavefield from sub-sampled information is very important and necessary in seismal image processing; this can be partially because of limitations of the observations that sometimes yield incomplete information. to form the simplest of the ascertained seismal signals, we have a tendency to propose a joint matrix reduction model to recover the seismal wavefield. using matrix rather than vector as weight variable will categorical all the sub-sampled traces at the same time. This theme utilizes the collective illustration instead of a private one to recover a given set of sub-samples. The matrix model takes the interrelatedness of the multiple observations under consideration to facilitate recovery, for instance, the similarity of constant seismal trace and distinctions of various ones. [4]

General Version of Gauss-type Proximal Point Method and Its Uniform Convergence Analysis for Metrically Regular Mappings

We study the uniform convergence of the final version of Gauss-type proximal purpose rule (GG-PPA), introduced by Alom et al. [1], for finding the constant quantity generalized equations y ∈ T(x), wherever T : X 2Y could be a set-valued mapping with regionally closed graph, y could be a parameter, and X and Y area unit Banach areas. especially, we tend to establish the uniform convergence of the GG-PPA by considering a sequence of Lipschitz continuous functions gk : X → Y with gk(0) = zero and positive Lipschitz constants λk within the sense that it’s stable underneath tiny perturbations once T is metrically regular at a given purpose. additionally, we tend to provides a numerical example to justify theuniform convergence of the GG-PPA. [5]

Reference

[1] Rudolph, G., 1994. Convergence analysis of canonical genetic algorithms. IEEE transactions on neural networks, 5(1), (Web Link)

[2] Trelea, I.C., 2003. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information processing letters, 85(6), (Web Link)

[3] Qi, L., 1993. Convergence analysis of some algorithms for solving nonsmooth equations. Mathematics of operations research, 18(1), (Web Link)

[4] A joint matrix minimization approach for seismic wavefield recovery
Liping Wang & Yanfei Wang
Scientific Reports volume 8, Article number: 2188 (2018) (Web Link)

[5] Alom, M. A., Rashid, M. H. and Dey, K. K. (2017) “General Version of Gauss-type Proximal Point Method and Its Uniform Convergence Analysis for Metrically Regular Mappings”, Journal of Advances in Mathematics and Computer Science, 20(4), (Web Link)

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