A two-stage prediction–correction method for solving monotone variational inequalities

In this paper, we present a two-stage prediction–correction method for solving monotone variational inequalities. The method generates the two predictors which should satisfy two acceptance criteria. We also enhance the method with an adaptive rule to update prediction step size which makes the method more effective. Under mild assumptions, we prove the convergence of the proposed method. Our proposed method based on projection only needs the function values, so it is practical and the computation load is quite tiny. Some numerical experiments were carried out to validate its efficiency and practicality.     

A generalized proximal-point-based prediction–correction method for variational inequality problems

In a class of variational inequality problems arising frequently from applications, the underlying mappings have no explicit expression, which make the subproblems involved in most numerical methods for solving them difficult to implement. In this paper, we propose a generalized proximal-point-based prediction–correction method for solving such problems. At each iteration, we first find a prediction point, which only needs several function evaluations; then using the information from the prediction, we update the iteration. Under mild conditions, we prove the global convergence of the method. The preliminary numerical results illustrate the simplicity and effectiveness of the method.     

An improved proximal alternating direction method for monotone variational inequalities with separable structure

To solve a class of variational inequalities with separable structure, this paper presents a new method to improve the proximal alternating direction method (PADM) in the following senses: an iterate generated by the PADM is utilized to generate a descent direction; and an appropriate step size along this descent direction is identified. Hence, a descent-like method is developed. Convergence of the new method is proved under mild assumptions. Some numerical results demonstrate that the new method is efficient.     

A Kantorovich-type convergence analysis of the Newton–Josephy method for solving variational inequalities

We present a Kantorovich-type semilocal convergence analysis of the Newton–Josephy method for solving a certain class of variational inequalities. By using a combination of Lipschitz and center-Lipschitz conditions, and our new idea of recurrent functions, we provide an analysis with the following advantages over the earlier works (Wang 2009, Wang and Shen, Appl Math Mech 25:1291–1297, 2004) (under the same or less computational cost): weaker sufficient convergence conditions, larger convergence domain, finer error bounds on the distances involved, and an at least as precise information on the location of the solution.     

A path-following cutting plane method for some monotone variational inequalities ∗

The paper considers two cases of variational inequality problems. The first case involves an affine monotone operator over a convex set defined by a separation oracle. Aninterior-point algorithm that mixes an interior cutting plane method and a short-step path-following method will be presented. Its complexity will be established. The second case is an extension of the first and involves a nonlinear monotone operator defined over the same type of convex set. The algorithm for the latter case is different from the former one only in the path-following stage     

A Hummel–Seebeck type method for variational inclusions

We study the stability of a Hummel–Seebeck like method for solving variational inclusions of the form 0?∈?f(x)?+?G(x), where f is a single-valued function while G stands for a set-valued mapping, both of them acting in Banach spaces. Then, we investigate a measure of conditioning of these inclusions under canonical perturbations.     

Convergence theorems based on the shrinking projection method for variational inequality and?equilibrium problems

The purpose of this paper is to introduce a hybrid projection algorithm based on the shrinking projection method for two relatively weak nonexpansive mappings. We prove strong convergence theorem which approximate the common element in the fixed point set of two such mappings, the solution set of the variational inequality and the solution set of the equilibrium problem in the framework of Banach spaces. Our results improve and extend previous results.     

A modified Hestenes–Stiefel projection method for constrained nonlinear equations and its linear convergence rate

The Hestenes–Stiefel (HS) method is an efficient method for solving large-scale unconstrained optimization problems. In this paper, we extend the HS method to solve constrained nonlinear equations, and propose a modified HS projection method, which combines the modified HS method proposed by Zhang et al. with the projection method developed by Solodov and Svaiter. Under some mild assumptions, we show that the new method is globally convergent with an Armijo line search. Moreover, the R-linear convergence rate of the new method is established. Some preliminary numerical results show that the new method is efficient even for large-scale constrained nonlinear equations.     

Minimax principles for elliptic mixed hemivariational–variational inequalities

In this paper, minimax principles are explored for elliptic mixed hemivariational–variational inequalities. Under certain conditions, a saddle-point formulation is shown to be equivalent to a mixed hemivariational–variational inequality. While the minimax principle is of independent interest, it is employed in this paper to provide an elementary proof of the solution existence of the mixed hemivariational–variational inequality. Theoretical results are illustrated in the applications of two contact problems.     

An Iterative method for nonlinear variational inequalities arising in elastoplasticity ∗

In this paper, we study an iterative method, which includes the Ka?anov method as a particular case, for solving nonlinear variational inequalities of the second kind. A convergence result is also proved under suitable assumptions. We apply our iterative method to solve an elastoplasticity problem.     

A family of predictor–corrector schemes for a class of variational inequalities

This work is concerned with an abstract problem in the form of a variational inequality, or equivalently a minimization problem involving a non-differential functional. The problem is inspired by a formulation of the initial–boundary value problem of elastoplasticity. The objective of this work is to revisit the predictor–corrector algorithms that are commonly used in computational applications, and to establish conditions under which these are convergent or, at least, under which they lead to decreasing sequences of the functional for the problem. The focus is on the predictor step, given that the corrector step by definition leads to a decrease in the functional. The predictor step may be formulated as a minimization problem. Attention is given to the tangent predictor, a line search approach, the method of steepest descent, and a Newton-like method. These are all shown to lead to decreasing sequences.     

Existence results for evolutionary inclusions and variational–hemivariational inequalities

We consider an abstract first-order evolutionary inclusion in a reflexive Banach space. The inclusion contains the sum of L-pseudomonotone operator and a maximal monotone operator. We provide an existence theorem which is a generalization of former results known in the literature. Next, we apply our result to the case of nonlinear variational–hemivariational inequalities considered in the setting of an evolution triple of spaces. We specify the multivalued operators in the problem and obtain existence results for several classes of variational–hemivariational inequality problems. Finally, we illustrate our existence result and treat a class of quasilinear parabolic problems under nonmonotone and multivalued flux boundary conditions.     

A stabilized structured Dantzig–Wolfe decomposition method

We discuss an algorithmic scheme, which we call the stabilized structured Dantzig–Wolfe decomposition method, for solving large-scale structured linear programs. It can be applied when the subproblem of the standard Dantzig–Wolfe approach admits an alternative master model amenable to column generation, other than the standard one in which there is a variable for each of the extreme points and extreme rays of the corresponding polyhedron. Stabilization is achieved by the same techniques developed for the standard Dantzig–Wolfe approach and it is equally useful to improve the performance, as shown by computational results obtained on an application to the multicommodity capacitated network design problem.     

A new self-adaptive alternating direction method for variational inequality problems with linear equality and inequality constraints

In this paper, we present a new self-adaptive alternating direction method for solving a class of variational inequality problems with both linear equality and inequality constraints without the need to add any extra slack variables. The method is simple because it needs only to perform some projections and function evaluations. In addition, to further enhance its efficiency, we adopt a self-adaptive strategy to adjust parameter μ at each iteration. Convergence of the proposed method is proved under certain conditions. Numerical experience illustrates the efficiency of the new method.     

A parallel two-level finite element variational multiscale method for the Navier–Stokes equations

A combination method of two-grid discretization approach with a recent finite element variational multiscale algorithm for simulation of the incompressible Navier–Stokes equations is proposed and analyzed. The method consists of a global small-scale nonlinear Navier–Stokes problem on a coarse grid and local linearized residual problems in overlapped fine grid subdomains, where the numerical form of the Navier–Stokes equations on the coarse grid is stabilized by a stabilization term based on two local Gauss integrations at element level and defined by the difference between a consistent and an under-integrated matrix involving the gradient of velocity. By the technical tool of local a priori estimate for the finite element solution, error bounds of the discrete solution are estimated. Algorithmic parameter scalings are derived. Numerical tests are also given to verify the theoretical predictions and demonstrate the effectiveness of the method.     

Gap functions and global error bounds for differential variational–hemivariational inequalities

This paper concerns with the study of a differential variational–hemivariational inequality (DVHVI, for short) in infinite-dimensional Banach spaces. We first introduce the new concept of gap functions for the variational control system of (DVHVI). Then, we consider two kinds of gap functions which are regularized gap function and Moreau–Yosida regularized gap function, respectively, and examine the relevant properties of the gap functions. Moreover, two global error bounds which depend implicitly on the regularized gap function and the Moreau–Yosida regularized gap function, accordingly, are obtained. Finally, in order to illustrate the applicability of the theoretical results, we investigate a coupled dynamic system which is formulated by a nonlinear reaction–diffusion equation described by a time-dependent nonsmooth semipermeability problem.