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

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.

References in corpus (2)

• A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
• Convex Optimization for Big Data

Cited by in corpus (4)

• A Generalized Accelerated Composite Gradient Method: Uniting Nesterov's Fast Gradient Method and FISTA
• Acceleration Methods
• Linearly-Convergent FISTA Variant for Composite Optimization with Duality
• A New Class of Composite Objective Multi-step Estimating-sequence Techniques (COMET)