# A Generalized Accelerated Composite Gradient Method: Uniting Nesterov's Fast Gradient Method and FISTA

## TL;DR

This paper introduces a unified accelerated gradient method that combines the strengths of FGM and FISTA, providing improved robustness, flexibility, and convergence analysis for large-scale convex optimization problems.

## Contribution

The authors develop a generalized accelerated composite gradient method uniting FGM and FISTA, with a Lyapunov-based analysis and enhanced monotonicity and line-search features.

## Key findings

- The new method outperforms FGM and FISTA in robustness and usability.
- Monotonicity stabilizes convergence in composite optimization.
- Simulation results validate the method's effectiveness on benchmark problems.

## Abstract

The most popular first-order accelerated black-box methods for solving large-scale convex optimization problems are the Fast Gradient Method (FGM) and the Fast Iterative Shrinkage Thresholding Algorithm (FISTA). FGM requires that the objective be finite and differentiable with known gradient Lipschitz constant. FISTA is applicable to the more broad class of composite objectives and is equipped with a line-search procedure for estimating the Lipschitz constant. Nonetheless, FISTA cannot increase the step size and is unable to take advantage of strong convexity. FGM and FISTA are very similar in form. Despite this, they appear to have vastly differing convergence analyses. In this work we generalize the previously introduced augmented estimate sequence framework as well as the related notion of the gap sequence. We showcase the flexibility of our tools by constructing a Generalized Accelerated Composite Gradient Method, that unites FGM and FISTA, along with their most popular variants. The Lyapunov property of the generalized gap sequence used in deriving our method implies that both FGM and FISTA are amenable to a Lyapunov analysis, common among optimization algorithms. We further showcase the flexibility of our tools by endowing our method with monotonically decreasing objective function values alongside a versatile line-search procedure. By simultaneously incorporating the strengths of FGM and FISTA, our method is able to surpass both in terms of robustness and usability. We support our findings with simulation results on an extensive benchmark of composite problems. Our experiments show that monotonicity has a stabilizing effect on convergence and challenge the notion present in the literature that for strongly convex objectives, accelerated proximal schemes can be reduced to fixed momentum methods.