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Journal ArticleDOI

A Class of Projection and Contraction Methods for Monotone Variational-Inequalities

Bingsheng He
- 01 Jan 1997 - 
- Vol. 35, Iss: 1, pp 69-76
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TLDR
In this article, a new class of iterative methods for solving monotone variational inequalities is introduced, where each iteration consists essentially only of the computation ofF(u), a projection to Ω,v:=P ≥ 0, and the mappingF(v) The distance of the iterates to the solution set monotonically converges to zero.
Abstract
In this paper we introduce a new class of iterative methods for solving the monotone variational inequalities $$u* \in \Omega , (u - u*)^T F(u*) \geqslant 0, \forall u \in \Omega $$ Each iteration of the methods presented consists essentially only of the computation ofF(u), a projection to Ω,v:=P Ω[u-F(u)], and the mappingF(v) The distance of the iterates to the solution set monotonically converges to zero Both the methods and the convergence proof are quite simple

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Citations
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Journal ArticleDOI

Some developments in general variational inequalities

TL;DR: This paper presents a number of new and known numerical techniques for solving general variational inequalities using various techniques including projection, Wiener-Hopf equations, updating the solution, auxiliary principle, inertial proximal, penalty function, dynamical system and well-posedness.
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Parallel Multi-Block ADMM with o(1 / k) Convergence

TL;DR: The classic ADMM can be extended to the N-block Jacobi fashion and preserve convergence in the following two cases: (i) matrices A_i and Ai are mutually near-orthogonal and have full column-rank, or (ii) proximal terms are added to theN subproblems (but without any assumption on matrices $$A_i$$Ai).
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Improvements of some projection methods for monotone nonlinear variational inequalities

TL;DR: In this article, the relationship of projection-type methods for monotone nonlinear variational inequalities was investigated and improvements were made. But the work in this paper is restricted to the case where the proximal point method is the corresponding implicit method.
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Inertial projection and contraction algorithms for variational inequalities

TL;DR: A modified version of the algorithm to find a common element of the set of solutions of a variational inequality and theset of fixed points of a nonexpansive mapping in H.
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Projection methods, algorithms, and a new system of nonlinear variational inequalities

TL;DR: In this paper, the convergence of projection methods is based on a new iterative algorithm for the approximation-solvability of the following system of nonlinear variational inequalities (SNVI): determine elements x*, y* ϵ K such that 〈ϱT(y*) + x* − y*, x − x*
References
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Journal ArticleDOI

Finite-dimensional variational inequality and nonlinear complementarity problems: a survey of theory, algorithms and applications

TL;DR: The field of finite-dimensional variational inequality and complementarity problems has seen a rapid development in its theory of existence, uniqueness and sensitivity of solution(s), in the theory of algorithms, and in the application of these techniques to transportation planning, regional science, socio-economic analysis, energy modeling, and game theory as mentioned in this paper.
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On the basic theorem of complementarity

TL;DR: Using a fixed point theorem of Browder, the basic existence theorem of Lemke in linear complementarity theory is generalized to the nonlinear case.
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Iterative methods for variational and complementarity problems

TL;DR: This paper studies both the local and global convergence of various iterative methods for solving the variational inequality and the nonlinear complementarity problems and several convergence results are obtained for some nonlinear approximation methods.
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An iterative scheme for variational inequalities

TL;DR: A general iterative scheme for the numerical solution of finite dimensional variational inequalities that contains the projection, linear approximation and relaxation methods but also induces new algorithms and allows the possibility of adjusting the norm at each step of the algorithm.