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arXiv · 1612.02352

An Accelerated Composite Gradient Method for Large-scale Composite Objective Problems

Mihai I. Florea·Sergiy A. Vorobyov
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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.

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Mihai I. Florea, Sergiy A. Vorobyov. 2016-12-07. An Accelerated Composite Gradient Method for Large-scale Composite Objective Problems. https://doi.org/10.1109/tsp.2018.2866409

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