The proximal augmented Lagrangian method for nonsmooth composite optimization

TL;DR

This paper introduces a novel proximal augmented Lagrangian method for nonsmooth composite optimization, enabling efficient primal-dual solutions with strong convergence guarantees, applicable to structured control problems and distributed settings.

Contribution

The paper develops a differentiable proximal augmented Lagrangian framework that extends the method of multipliers to nonsmooth, nonconvex problems, with proven stability and distributed implementation advantages.

Findings

Proposed method has stronger convergence guarantees than ADMM.

Applicable to a broader class of problems than proximal gradient methods.

Demonstrated global exponential stability under certain convexity conditions.

Abstract

We study a class of optimization problems in which the objective function is given by the sum of a differentiable but possibly nonconvex component and a nondifferentiable convex regularization term. We introduce an auxiliary variable to separate the objective function components and utilize the Moreau envelope of the regularization term to derive the proximal augmented Lagrangian $-$ a continuously differentiable function obtained by constraining the augmented Lagrangian to the manifold that corresponds to the explicit minimization over the variable in the nonsmooth term. The continuous differentiability of this function with respect to both primal and dual variables allows us to leverage the method of multipliers (MM) to compute optimal primal-dual pairs by solving a sequence of differentiable problems. The MM algorithm is applicable to a broader class of problems than proximal gradient methods and it has stronger convergence guarantees and a more refined step-size update rules than the alternating direction method of multipliers. These features make it an attractive option for solving structured optimal control problems. We also develop an algorithm based on the primal-descent dual-ascent gradient method and prove global (exponential) asymptotic stability when the differentiable component of the objective function is (strongly) convex and the regularization term is convex. Finally, we identify classes of problems for which the primal-dual gradient flow dynamics are convenient for distributed implementation and compare/contrast our framework to the existing approaches.