Solving policy design problems: Alternating direction method of multipliers-based methods for structured inverse variational inequalities

被引:11
作者
Jiang, Yaning [1 ]
Cai, Xingju [2 ]
Han, Deren [3 ]
机构
[1] Nanjing Normal Univ, Sch Math Sci, Jiangsu Key Lab NSLSCS, Nanjing 210023, Jiangsu, Peoples R China
[2] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China
[3] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp BDB, Sch Math & Syst Sci, Beijing 100191, Peoples R China
关键词
Convex programming; Inverse variational inequalities; Alternating direction method of multipliers; Monotone mapping; SPLITTING ALGORITHM; DECOMPOSITION;
D O I
10.1016/j.ejor.2019.05.044
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Inverse variational inequalities have broad applications in various disciplines, and some of them have very appealing structures. There are several algorithms (e.g., proximal point algorithms and projection-type algorithms) for solving the inverse variational inequalities in general settings, while few of them have fully exploited the special structures. In this paper, we consider a class of inverse variational inequalities that has a separable structure and linear constraints, which has its root in spatial economic equilibrium problems. To design an efficient algorithm, we develop an alternating direction method of multipliers (ADMM) based method by utilizing the separable structure. Under some mild assumptions, we prove its global convergence. We propose an improved variant that makes the subproblems much easier and derive the convergence result under the same conditions. Finally, we present the preliminary numerical results to show the capability and efficiency of the proposed methods. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:417 / 427
页数:11
相关论文
共 29 条
[1]  
[Anonymous], [No title captured]
[2]  
[Anonymous], 1983, AUGMENTED LAGRANGIAN, DOI DOI 10.1016/S0168-2024(08)70034-1
[3]   INVERSE VARIATIONAL INEQUALITY APPROACH AND APPLICATIONS [J].
Barbagallo, Annamaria ;
Mauro, Paolo .
NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION, 2014, 35 (7-9) :851-867
[4]   Distributed optimization and statistical learning via the alternating direction method of multipliers [J].
Boyd S. ;
Parikh N. ;
Chu E. ;
Peleato B. ;
Eckstein J. .
Foundations and Trends in Machine Learning, 2010, 3 (01) :1-122
[5]   Alternating Direction Method for Image Inpainting in Wavelet Domains [J].
Chan, Raymond H. ;
Yang, Junfeng ;
Yuan, Xiaoming .
SIAM JOURNAL ON IMAGING SCIENCES, 2011, 4 (03) :807-826
[6]   Matrix completion via an alternating direction method [J].
Chen, Caihua ;
He, Bingsheng ;
Yuan, Xiaoming .
IMA JOURNAL OF NUMERICAL ANALYSIS, 2012, 32 (01) :227-245
[7]  
Ding Zheng-sheng, 2010, Proceedings of the 2010 International Conference on Computational Aspects of Social Networks (CASoN 2010), P239, DOI 10.1109/CASoN.2010.61
[8]  
Douglas J., 1956, T AM MATH SOC, V82, P421, DOI [DOI 10.1090/S0002-9947-1956-0084194-4, DOI 10.2307/1993056]
[9]   ON THE DOUGLAS-RACHFORD SPLITTING METHOD AND THE PROXIMAL POINT ALGORITHM FOR MAXIMAL MONOTONE-OPERATORS [J].
ECKSTEIN, J ;
BERTSEKAS, DP .
MATHEMATICAL PROGRAMMING, 1992, 55 (03) :293-318
[10]  
Esser E., 2009, CAM Rep., V9