Complementary Composite Minimization, Small Gradients in General Norms, and Applications

TL;DR

This paper introduces a new framework for complementary composite minimization in general normed spaces, providing near-optimal algorithms for broad classes of problems, including making gradients small in various norms, with applications in statistics and machine learning.

Contribution

The authors develop a unified accelerated algorithmic framework for complementary composite minimization in general normed spaces, achieving near-optimal complexity bounds and broad applicability.

Findings

Proposed a new algorithmic framework for complementary composite minimization.

Achieved near-optimal complexity bounds in standard optimization settings.

Extended small gradient methods to general normed spaces, including the $ ext{l}_1$ setup.

Abstract

Composite minimization is a powerful framework in large-scale convex optimization, based on decoupling of the objective function into terms with structurally different properties and allowing for more flexible algorithmic design. We introduce a new algorithmic framework for complementary composite minimization, where the objective function decouples into a (weakly) smooth and a uniformly convex term. This particular form of decoupling is pervasive in statistics and machine learning, due to its link to regularization. The main contributions of our work are summarized as follows. First, we introduce the problem of complementary composite minimization in general normed spaces; second, we provide a unified accelerated algorithmic framework to address broad classes of complementary composite minimization problems; and third, we prove that the algorithms resulting from our framework are near-optimal in most of the standard optimization settings. Additionally, we show that our algorithmic framework can be used to address the problem of making the gradients small in general normed spaces. As a concrete example, we obtain a nearly-optimal method for the standard $\ell_1$ setup (small gradients in the $\ell_{\infty}$ norm), essentially matching the bound of Nesterov (2012) that was previously known only for the Euclidean setup. Finally, we show that our composite methods are broadly applicable to a number of regression and other classes of optimization problems, where regularization plays a key role. Our methods lead to complexity bounds that are either new or match the best existing ones.