One Mirror Descent Algorithm for Convex Constrained Optimization Problems with non-standard growth properties
Year: 2018
Type: preprint
Abstract: The paper is devoted to a special Mirror Descent algorithm for problems of convex minimization with functional constraints. The objective function may not satisfy the Lipschitz condition, but it must necessarily have the Lipshitz-continuous gradient. We assume, that the functional constraint can be non-smooth, but satisfying the Lipschitz condition. In particular, such functionals appear in the well-known Truss Topology Design problem. Also we have applied the technique of restarts in the mentioned version of Mirror Descent for strongly convex problems. Some estimations for a rate of convergence are investigated for considered Mirror Descent algorithms.
Source: arXiv (Cornell University)
Authors: Stonyakin, Fedor S., Titov, Alexander A.
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2789786233