On the Softplus Penalty for Constrained Convex Optimization
Optimization and Control
2023-05-23 v1
Abstract
We study a new penalty reformulation of constrained convex optimization based on the softplus penalty function. We develop novel and tight upper bounds on the objective value gap and the violation of constraints for the solutions to the penalty reformulations by analyzing the solution path of the reformulation with respect to the smoothness parameter. We use these upper bounds to analyze the complexity of applying gradient methods, which are advantageous when the number of constraints is large, to the reformulation.
Cite
@article{arxiv.2305.12603,
title = {On the Softplus Penalty for Constrained Convex Optimization},
author = {Meng Li and Paul Grigas and Alper Atamturk},
journal= {arXiv preprint arXiv:2305.12603},
year = {2023}
}