Titlebook: Large-Scale and Distributed Optimization; Pontus Giselsson,Anders Rantzer Book 2018 Springer Nature Switzerland AG 2018 Large-Scale Optimi

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t by a "many-to-one or one-to­ many application between contextually defined perceptual objects within contexts that are mutually incompatible but complementary. " This should not, however, be 978-90-481-5926-0978-94-017-1767-0Series ISSN 0068-0346 Series E-ISSN 2214-7942

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Allure

Decentralized Consensus Optimization and Resource Allocation,r solving the decentralized consensus optimization problem and, based on the “mirror relationship”, we then develop some algorithms for solving the decentralized resource allocation problem. We also provide some numerical experiments to demonstrate the efficacy of the algorithms and validate the methodology of using the “mirror relation”.

要素

0075-8434 for large-scale optimization.Offers a valuable source of inThis book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade

场所

Exploiting Chordality in Optimization Algorithms for Model Predictive Control,point method for solving the problem. The chordal structure can stem both from the sequential nature of the problem as well as from distributed formulations of the problem related to scenario trees or other formulations. The framework enables efficient parallel computations.

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Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization,to update all the algorithmic parameters automatically with clear impact on the convergence performance. We also provide a representative numerical example showing the advantages of our methods over the classical alternating direction methods using a well-known feasibility problem.

Emmenagogue

热心

Mirror Descent and Convex Optimization Problems with Non-smooth Inequality Constraints, also construct Mirror Descent for problems with objective function, which is not Lipschitz, e.g., is a quadratic function. Besides that, we address the question of recovering the dual solution in the considered problem.

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