- An Extragradient-Based Alternating Direction Method for Convex Minimization Tianyi Lin (lintyse.cuhk.edu.hk) Shiqian Ma (sqmase.cuhk.edu.hk) Shuzhong Zhang (zhangsumn.edu) Abstract: In this paper, we consider the problem of minimizing the sum of two convex functions subject to linear linking constraints. The classical alternating direction type methods usually assume that the two convex functions have relatively easy proximal mappings. However, many problems arising from statistics, image processing and other fields have the structure that while one of the two functions has easy proximal mapping, the other function is smoothly convex but does not have an easy proximal mapping. Therefore, the classical alternating direction methods cannot be applied. To deal with the difficulty, we propose in this paper an alternating direction method based on extragradients. Under the assumption that the smooth function has a Lipschitz continuous gradient, we prove that the proposed method returns an $\epsilon$-optimal solution within $O(1/\epsilon)$ iterations. We apply the proposed method to solve a new statistical model called fused logistic regression. Our numerical experiments show that the proposed method performs very well when solving the test problems. We also test the performance of the proposed method through solving the lasso problem arising from statistics and compare the result with several existing efficient solvers for this problem; the results are very encouraging indeed. Keywords: Alternating Direction Method; Extragradient; Iteration Complexity; Basis Pursuit; Fused Logistic Regression Category 1: Convex and Nonsmooth Optimization Citation: Download: [PDF]Entry Submitted: 01/26/2013Entry Accepted: 01/27/2013Entry Last Modified: 07/08/2015Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.