Random constraint sampling and duality for convex optimization
Optimization and Control
2016-11-29 v2
Abstract
We are interested in solving convex optimization problems with large numbers of constraints. Randomized algorithms, such as random constraint sampling, have been very successful in giving nearly optimal solutions to such problems. In this paper, we combine random constraint sampling with the classical primal-dual algorithm for convex optimization problems with large numbers of constraints, and we give a convergence rate analysis. We then report numerical experiments that verify the effectiveness of this algorithm.
Cite
@article{arxiv.1610.06702,
title = {Random constraint sampling and duality for convex optimization},
author = {William B. Haskell and Yu Pengqian},
journal= {arXiv preprint arXiv:1610.06702},
year = {2016}
}
Comments
Substantially revised draft in preparation, with much stronger results