A Semismooth Newton-Based Proximal Augmented Lagrangian Method for Joint Estimation of Multiple Gaussian Graphical Models with Clustered Structure

Abstract

In this paper, we consider a class of convex composite optimization problems arising from the joint estimation of clustered multiple Gaussian graphical models. The resulting model combines a log-determinant loss term with a nonsmooth sparse clustered regularizer, which encourages both similar sparsity patterns and consistent edge values across different graphs. We first establish a necessary and sufficient condition under which the solution is block diagonal, enabling a large-scale problem to be decomposed into smaller independent subproblems and substantially reducing computational complexity. To efficiently solve this problem, we develop a proximal augmented Lagrangian method in which each subproblem is handled by a superlinearly convergent semismooth Newton method. Unlike widely used first order methods, our approach fully exploits the underlying second order information through the semismooth Newton framework, thereby achieving faster convergence and improved robustness. The efficiency and robustness of the proposed algorithm are demonstrated through comparisons with state-of-the-art methods on both synthetic and real data sets.