Self-adaptive subgradient extragradient method with inertial modification for solving monotone variational inequality problems and quasi-nonexpansive fixed point problems

被引:0
作者
Ming Tian
Mengying Tong
机构
[1] Civil Aviation University of China,College of Science
来源
Journal of Inequalities and Applications | / 2019卷
关键词
Variational inequality problem; Fixed point problem; Extragradient method; Subgradient extragradient method; Inertial method; Self-adaptive method;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, we introduce a new algorithm with self-adaptive method for finding a solution of the variational inequality problem involving monotone operator and the fixed point problem of a quasi-nonexpansive mapping with a demiclosedness property in a real Hilbert space. The algorithm is based on the subgradient extragradient method and inertial method. At the same time, it can be considered as an improvement of the inertial extragradient method over each computational step which was previously known. The weak convergence of the algorithm is studied under standard assumptions. It is worth emphasizing that the algorithm that we propose does not require one to know the Lipschitz constant of the operator. Finally, we provide some numerical experiments to verify the effectiveness and advantage of the proposed algorithm.
引用
收藏
相关论文
共 50 条
[1]  
Gibali A.(2018)Two simple relaxed perturbed extragradient methods for solving variational inequalities in Euclidean spaces J. Nonlinear Var. Anal. 2 49-61
[2]  
Yao Y.H.(2012)Strong convergence of a proximal point algorithm with general errors Optim. Lett. 6 621-628
[3]  
Shahzad N.(2010)Schemes for finding minimum-norm solutions of variational inequalities Nonlinear Anal. 72 3447-3456
[4]  
Yao Y.H.(1976)The extragradient method for finding saddle points and other problem Èkon. Mat. Metody 12 747-756
[5]  
Chen R.D.(2000)A modified forward-backward splitting method for maximal monotone mappings SIAM J. Control Optim. 38 431-446
[6]  
Xu H.K.(2011)The subgradient extragradient method for solving variational inequalities in Hilbert space J. Optim. Theory Appl. 148 318-335
[7]  
Korpelevich G.M.(2001)An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping Set-Valued Anal. 9 3-11
[8]  
Tseng P.(2017)Inertial projection and contraction algorithms for variational inequalities J. Glob. Optim. 171 146-168
[9]  
Censor Y.(2017)Weak and strong convergence theorems for variational inequality problems Numer. Algorithms 10 1293-1303
[10]  
Gibali A.(2016)Covergence of one-step projected gradient methods for variational inequalities J. Optim. Theory Appl. 61 341-350