Sequential Randomized Algorithms for Convex Optimization in the Presence of Uncertainty
Abstract
In this paper, we propose new sequential randomized algorithms for convex optimization problems in the presence of uncertainty. A rigorous analysis of the theoretical properties of the solutions obtained by these algorithms, for full constraint satisfaction and partial constraint satisfaction, respectively, is given. The proposed methods allow to enlarge the applicability of the existing randomized methods to real-world applications involving a large number of design variables. Since the proposed approach does not provide a priori bounds on the sample complexity, extensive numerical simulations, dealing with an application to hard-disk drive servo design, are provided. These simulations testify the goodness of the proposed solution.
Cite
@article{arxiv.1304.2222,
title = {Sequential Randomized Algorithms for Convex Optimization in the Presence of Uncertainty},
author = {Mohammadreza Chamanbaz and Fabrizio Dabbene and Roberto Tempo and Venkatakrishnan Venkataramanan and Qing-Guo Wang},
journal= {arXiv preprint arXiv:1304.2222},
year = {2016}
}
Comments
18 pages, Submitted for publication to IEEE Transactions on Automatic Control