Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints
Abstract
Convex optimization with equality and inequality constraints is a ubiquitous problem in several optimization and control problems in large-scale systems. Recently there has been a lot of interest in establishing accelerated convergence of the loss function. A class of high-order tuners was recently proposed in an effort to lead to accelerated convergence for the case when no constraints are present. In this paper, we propose a new high-order tuner that can accommodate the presence of equality constraints. In order to accommodate the underlying box constraints, time-varying gains are introduced in the high-order tuner which leverage convexity and ensure anytime feasibility of the constraints. Numerical examples are provided to support the theoretical derivations.
Cite
@article{arxiv.2305.04433,
title = {Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints},
author = {Anjali Parashar and Priyank Srivastava and Anuradha M. Annaswamy},
journal= {arXiv preprint arXiv:2305.04433},
year = {2023}
}
Comments
6 pages, accepted in ACC 2023 (American Control Conference, 2023)