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 language||English (US)|
|Title of host publication||2018 Annual American Control Conference, ACC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Aug 9 2018|
|Event||2018 Annual American Control Conference, ACC 2018 - Milwauke, United States|
Duration: Jun 27 2018 → Jun 29 2018
|Name||Proceedings of the American Control Conference|
|Other||2018 Annual American Control Conference, ACC 2018|
|Period||6/27/18 → 6/29/18|
Bibliographical notePublisher Copyright:
© 2018 AACC.
Copyright 2018 Elsevier B.V., All rights reserved.