A Random Algorithm for Semidefinite Programming Problems

Jianjun Yuan, Andrew Lamperski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce a first-order method for solving semidefinite programming problems. This method has low computational complexity per iteration and is easy to implement. In each iteration, it alternates in two steps: gradient-descent to optimize the objective function, and random projection to reduce the infeasibility of the constraints. Due to its low computational complexity per iteration, it can be scaled to large problems. We also prove the algorithm's convergence and demonstrate its performance in numerical examples.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1382-1387
Number of pages6
ISBN (Print)9781538654286
DOIs
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
Volume2018-June
ISSN (Print)0743-1619

Other

Other2018 Annual American Control Conference, ACC 2018
CountryUnited States
CityMilwauke
Period6/27/186/29/18

Bibliographical note

Publisher Copyright:
© 2018 AACC.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

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