The proximal augmented Lagrangian method for nonsmooth composite optimization
For researchers in optimization and control, this work provides a novel framework that combines the benefits of augmented Lagrangian and proximal methods, offering improved convergence and step-size rules for a broad class of nonsmooth composite problems.
The paper introduces the proximal augmented Lagrangian method for nonsmooth composite optimization, which transforms the problem into a continuously differentiable function, enabling the use of the method of multipliers with stronger convergence guarantees than proximal gradient methods and ADMM. The method achieves global exponential stability for convex problems and is applicable to structured optimal control.
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.