Scalable Semidefinite Programming
Optimization and Control
2021-03-26 v2 Combinatorics
Abstract
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct randomized algorithm for solving large, weakly constrained SDP problems by economizing on the storage and arithmetic costs. Numerical evidence shows that the method is effective for a range of applications, including relaxations of MaxCut, abstract phase retrieval, and quadratic assignment. Running on a laptop equivalent, the algorithm can handle SDP instances where the matrix variable has over entries.
Cite
@article{arxiv.1912.02949,
title = {Scalable Semidefinite Programming},
author = {Alp Yurtsever and Joel A. Tropp and Olivier Fercoq and Madeleine Udell and Volkan Cevher},
journal= {arXiv preprint arXiv:1912.02949},
year = {2021}
}