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A Framework for Applying Subgradient Methods to Conic Optimization Problems (version 2)

James Renegar (renegar***at***cornell.edu)

Abstract: A framework is presented whereby a general convex conic optimization problem is transformed into an equivalent convex optimization problem whose only constraints are linear equations and whose objective function is Lipschitz continuous. Virtually any subgradient method can be applied to solve the equivalent problem. Two methods are analyzed. (In version 2, the development of algorithms is streamlined and considerably strengthened.)

Keywords: conic optimization, subgradient methods

Category 1: Linear, Cone and Semidefinite Programming

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Citation: arXiv:1503.02611

Download: [PDF]

Entry Submitted: 03/22/2015
Entry Accepted: 03/22/2015
Entry Last Modified: 06/17/2015

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