An Accelerated Composite Gradient Method for Large-scale Composite Objective Problems
Optimization and Control
2019-04-24 v2
Abstract
We introduce a framework, which we denote as the augmented estimate sequence, for deriving fast algorithms with provable convergence guarantees. We use this framework to construct a new first-order scheme, the Accelerated Composite Gradient Method (ACGM), for large-scale problems with composite objective structure. ACGM surpasses the state-of-the-art methods for this problem class in terms of provable convergence rate, both in the strongly and non-strongly convex cases, and is endowed with an efficient step size search procedure. We support the effectiveness of our new method with simulation results.
Keywords
Cite
@article{arxiv.1612.02352,
title = {An Accelerated Composite Gradient Method for Large-scale Composite Objective Problems},
author = {Mihai I. Florea and Sergiy A. Vorobyov},
journal= {arXiv preprint arXiv:1612.02352},
year = {2019}
}