A Framework for Applying Subgradient Methods to Conic Optimization Problems

James Renegar

A Framework for Applying Subgradient Methods to Conic Optimization Problems

Optimization and Control 2015-06-17 v2

Authors: James Renegar

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.

Keywords

convex optimization proximal optimization gradient descent optimization

Cite

@article{arxiv.1503.02611,
  title  = {A Framework for Applying Subgradient Methods to Conic Optimization Problems},
  author = {James Renegar},
  journal= {arXiv preprint arXiv:1503.02611},
  year   = {2015}
}

Comments

In the revised version, the development of algorithms has been streamlined and the results considerably strengthened

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