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Consider a class of variational inequality problems of finding $${x^*\in S}$$, such that where the underlying mapping f is hard to evaluate (sometimes its explicit form is unknown), and S has the following structure For any given Lagrangian multiplier y associated with the linear inequality constraint in S, a solution of the relaxed variational inequality problem of finding $${\hat x\in K}$$, such that can be given by an oracle. This class of problems arises frequently in economics and engineering. In this paper, we focus on considering the above problems where the underlying mapping f, though is unknown, is strongly monotone. We propose an iterative method for solving this class of variational inequality problems. At each iteration, the method consists of two steps: predictor and corrector. At the predictor step, a trial multiplier is given and the oracle is called for a solution of the relaxed variational inequality problem (1); then at the corrector step, the multiplier y is updated, using the information from the predictor step. We allow the oracle to give just an inexact solution of the relaxed variational inequality problem at the predictor step, which makes the method very efficient and practical. Under some suitable conditions, the global convergence of the method is proved. Some numerical examples are presented to illustrate the method. [ABSTRACT FROM AUTHOR]

Abstract  In this paper, we derive necessary and sufficient conditions for the strong ellipticity condition of anisotropic elastic materials. We first observe that the strong ellipticity condition holds if and only if a second order tensor function is positive definite for any unit vectors. Then we further link this condition to the rank-one positive definiteness of three second-order tensors, three fourth-order tensors and a sixth-order tensor. In particular, we consider conditions of strong ellipticity of the rhombic classes, for which we need to check the copositivity of three second-order tensors and the positive definiteness of a sixth-order tensor. A direct method is presented to verify our conditions. [ABSTRACT FROM AUTHOR]

The efficiency of the implicit method proposed by He (1999) depends on the parameter β heavily; while it varies for individual problem, that is, different problem has different "suitable" parameter, which is difficult to find. In this paper, we present a modified implicit method, which adjusts the parameter β automatically per iteration, based on the message from former iterates. To improve the performance of the algorithm, an inexact version is proposed, where the subproblem is just solved approximately. Under mild conditions as those for variational inequalities, we prove the global convergence of both exact and inexact versions of the new method. We also present several preliminary numerical results, which demonstrate that the self-adaptive implicit method, especially the inexact version, is efficient and robust. [ABSTRACT FROM AUTHOR]

Abstract  In this paper, we propose a new operator splitting method for solving a class of variational inequality problems in which part of the underlying mappings are unknown. This class of problems arises frequently from engineering, economics and transportation equilibrium problems. At each iteration, by using the information observed from the system, the method solves a system of nonlinear equations, which is well-defined. Under mild assumptions, the global convergence of the method is proved, and its efficiency is demonstrated with numerical examples. [ABSTRACT FROM AUTHOR]

The alternating direction method solves large scale variational inequality problems with linear constraints via solving a series of small scale variational inequality problems with simple constraints. The algorithm is attractive if the subproblems can be solved efficiently and exactly. However, the subproblem is itself variational inequality problem, which is structurally also difficult to solve. In this paper, we develop a new decomposition algorithm, which, at each iteration, just solves a system of well-conditioned linear equations and performs a line search. We allow to solve the subproblem approximately and the accuracy criterion is the constructive one developed recently by Solodov and Svaiter. Under mild assumptions on the problem's data, the algorithm is proved to converge globally. Some preliminary computational results are also reported to illustrate the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modified Bregman-function-based proximal point algorithm for solving variational inequality problems. The algorithm adopts a similar constructive approximate criterion as the one developed by Solodov and Svaiter (Set Valued Analysis 7 (1999) 323) for solving the classical proximal subproblems. Under some suitable conditions, we can get an approximate solution satisfying the accuracy criterion via a single Newton-type step. We obtain the Fejér monotonicity to solutions of VIP for paramonotone operators. Some preliminary computational results are also reported to illustrate the method. [ABSTRACT FROM AUTHOR]