An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems

• 成果类型: Article
• 署名作者: Zhang, Yangjing; Zhang, Ning; Sun, Defeng; Toh, Kim-Chuan
• 署名单位: National University of Singapore; Hong Kong Polytechnic University; National University of Singapore
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-018-1329-6
• 发表日期: 2020
• 页码: 223-263
• 关键词: Augmented Lagrangian method; convergence analysis; newton; minimization; regression

摘要

The sparse group Lasso is a widely used statistical model which encourages the sparsity both on a group and within the group level. In this paper, we develop an efficient augmented Lagrangian method for large-scale non-overlapping sparse group Lasso problems with each subproblem being solved by a superlinearly convergent inexact semismooth Newton method. Theoretically, we prove that, if the penalty parameter is chosen sufficiently large, the augmented Lagrangian method converges globally at an arbitrarily fast linear rate for the primal iterative sequence, the dual infeasibility, and the duality gap of the primal and dual objective functions. Computationally, we derive explicitly the generalized Jacobian of the proximal mapping associated with the sparse group Lasso regularizer and exploit fully the underlying second order sparsity through the semismooth Newton method. The efficiency and robustness of our proposed algorithm are demonstrated by numerical experiments on both the synthetic and real data sets.