Free-Steering Relaxation Methods for Problems with Strictly Convex Costs and Linear Constraints

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/3891 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/3891 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/3891 archive 351 disk0/00/00/41/22 2016-01-15 02:04:11 2021-08-27 17:14:49 2016-01-15 02:04:11 monograph show 2 Kiwiel K. Free-Steering Relaxation Methods for Problems with Strictly Convex Costs and Linear Constraints pub iis_cmp iis_met iis_mod prog_opt We consider dual coordinate ascent methods for minimizing a strictly convex (possibly nondifferentiable) function subject to linear constraints. Such methods are useful in large-scale applications (e.g., entropy maximization, quadratic programming, network flows), because they are simple, can exploit sparsity and in certain cases are highly parallelizable. We establish their global convergence under weak conditions and a free-steering order of relaxation. Previous comparable results were restricted to special problems with separable costs and equality constraints. Our convergence framework unifies to a certain extent the approaches of Bregman, Censor and Lent, De Pierro and Iusem, and Luo and Tseng, and complements that of Bertsekas and Tseng. 1994-09 published WP-94-089 WP-94-089 10 public working_paper IIASA, Laxenburg, Austria 27 FALSE info:eu-repo/semantics/book