A hybrid splitting method for variational inequality problems with separable structure

Alternating direction method (ADM), which decomposes a large-scale original variational inequality (VI) problem into a series of smaller scale subproblems, is very attractive for solving a class of VI problems with a separable structure. This type of method can greatly improve the efficiency, but cannot avoid solving VI subproblems. In this paper, we propose a hybrid splitting method with variable parameters for separable VI problems. Specifically, the proposed method solves only one strongly monotone VI subproblem and a well-posed system of nonlinear equations in each iteration. The global convergence of the new method is established under some standard assumptions as those in classical ADMs. Finally, some preliminary numerical results show that the proposed method performs favourably in practice.

[1]  Paul Tseng,et al.  Alternating Projection-Proximal Methods for Convex Programming and Variational Inequalities , 1997, SIAM J. Optim..

[2]  Deren Han,et al.  Inexact Operator Splitting Methods with Selfadaptive Strategy for Variational Inequality Problems , 2007 .

[3]  Stanley Osher,et al.  A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration , 2010, J. Sci. Comput..

[4]  Xiaoming Yuan An improved proximal alternating direction method for monotone variational inequalities with separable structure , 2011, Comput. Optim. Appl..

[5]  B. Mercier,et al.  A dual algorithm for the solution of nonlinear variational problems via finite element approximation , 1976 .

[6]  Bingsheng He,et al.  Self-adaptive operator splitting methods for monotone variational inequalities , 2003, Numerische Mathematik.

[7]  T. Zhu,et al.  A simple proof for some important properties of the projection mapping , 2004 .

[8]  R. Glowinski,et al.  Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics , 1987 .

[9]  A. Nagurney Network Economics: A Variational Inequality Approach , 1992 .

[10]  Z. Luo A Class of Iterative Methods for Solving Nonlinear Projection Equations , 2005 .

[11]  M. Fortin,et al.  Augmented Lagrangian methods : applications to the numerical solution of boundary-value problems , 1983 .

[12]  Bingsheng He,et al.  Inexact implicit methods for monotone general variational inequalities , 1999, Math. Program..

[13]  Defeng Sun Calibrating Least Squares Covariance Matrix Problems with Equality and Inequality Constraints , 2008 .

[14]  R. Tyrrell Rockafellar,et al.  Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming , 1976, Math. Oper. Res..

[15]  Stella Dafermos,et al.  Traffic Equilibrium and Variational Inequalities , 1980 .

[16]  Wotao Yin,et al.  Alternating direction augmented Lagrangian methods for semidefinite programming , 2010, Math. Program. Comput..

[17]  Dimitri P. Bertsekas,et al.  On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators , 1992, Math. Program..

[18]  Yan Gao,et al.  Calibrating Least Squares Semidefinite Programming with Equality and Inequality Constraints , 2009, SIAM J. Matrix Anal. Appl..

[19]  Bingsheng He,et al.  A new inexact alternating directions method for monotone variational inequalities , 2002, Math. Program..

[20]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[21]  B. He,et al.  Alternating Direction Method with Self-Adaptive Penalty Parameters for Monotone Variational Inequalities , 2000 .

[22]  Xiaoming Yuan,et al.  Convergence analysis of primal-dual algorithms for total variation image restoration , 2010 .

[23]  D. Gabay Applications of the method of multipliers to variational inequalities , 1983 .

[24]  Min Zhang,et al.  A new alternating direction method for solving separable variational inequality problems , 2012 .

[25]  Wenyu Sun,et al.  New decomposition methods for solving variational inequality problems , 2003 .

[26]  R. Rockafellar Monotone Operators and the Proximal Point Algorithm , 1976 .

[27]  Xiaoming Yuan,et al.  Sparse and low-rank matrix decomposition via alternating direction method , 2013 .

[28]  Marc Teboulle,et al.  A proximal-based decomposition method for convex minimization problems , 1994, Math. Program..

[29]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[30]  Robert R. Meyer,et al.  A variable-penalty alternating directions method for convex optimization , 1998, Math. Program..

[31]  Bingsheng He,et al.  Proximal-Point Algorithm Using a Linear Proximal Term , 2009 .

[32]  Wei Xu,et al.  An operator splitting method for variational inequalities with partially unknown mappings , 2008, Numerische Mathematik.

[33]  Patrick T. Harker,et al.  Finite-dimensional variational inequality and nonlinear complementarity problems: A survey of theory, algorithms and applications , 1990, Math. Program..

[34]  Masao Fukushima,et al.  Application of the alternating direction method of multipliers to separable convex programming problems , 1992, Comput. Optim. Appl..

[35]  Ya-Xiang Yuan,et al.  Optimization Theory and Methods: Nonlinear Programming , 2010 .