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Showing 1 to 3 of 3 for “"Stochastic approximation methods"”.
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Stochastic approximation schemes for stochastic optimization and variational problems: adaptive steplengths, smoothing, and regularization
Stochastic approximation (SA) methods, first proposed by Robbins and Monro in 1951 for root- finding problems, have been widely used in the literature to solve problems arising from stochastic convex optimization, stochastic Nash games and more recently stochastic variational inequalities. Several …
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Stochastic Approximation Algorithms With Applications To Particle Swarm Optimization, Adaptive Optimization, And Consensus
… three problems arising in recent applications of stochastic approximation methods. In Chapter 2, we use stochastic approximation to analyze Particle Swarm Optimization (PSO) algorithm. We introduce four coefficients and rewrite the PSO procedure as a stochastic approximation type iterative …
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Distributed algorithms for networked multi-agent systems: optimization and competition
… of distributed gradient-based algorithms on an approximation of the multiuser problem. Such an approximation is obtained through a regularization and is equipped with bounds of the difference between the optimal function values of the original problem and its regularized counterpart. In the …