A globally and locally superlinearly convergent inexact Newton-GMRES method for large-scale variational inequality problem

In this paper, we propose an inexact Newton-generalized minimal residual method for solving the variational inequality problem. Based on a new smoothing function, the variational inequality problem is reformulated as a system of parameterized smooth equations. In each iteration, the corresponding linear system is solved only approximately. Under mild assumptions, it is proved that the proposed algorithm has global convergence and local superlinear convergence properties. Preliminary numerical results indicate that the method is effective for a large-scale variational inequality problem.     

A smoothing inexact Newton method for variational inequality problems

In this paper, we reformulate the variational inequality problem as an equivalent smooth non-linear equation system by introducing the Chen–Harker–Kanzow–Smale smoothing function. A new smoothing inexact Newton algorithm is proposed to solve the smooth equations. In each iteration, the corresponding linear system is solved approximately. We prove that the proposed algorithm converges globally and superlinearly under mild conditions. Preliminary numerical results indicate that the method is effective.     

A hybrid splitting method for variational inequality problems with separable structure

Alternating direction method (ADM), which decomposes a large-scale original variational inequality (VI) problem into a series of smaller scale subproblems, is very attractive for solving a class of VI problems with a separable structure. This type of method can greatly improve the efficiency, but cannot avoid solving VI subproblems. In this paper, we propose a hybrid splitting method with variable parameters for separable VI problems. Specifically, the proposed method solves only one strongly monotone VI subproblem and a well-posed system of nonlinear equations in each iteration. The global convergence of the new method is established under some standard assumptions as those in classical ADMs. Finally, some preliminary numerical results show that the proposed method performs favourably in practice.     

A globally convergent method based on Fischer–Burmeister operators for solving second-order cone constrained variational inequality problems

The Karush–Kuhn–Tucker system of a second-order cone constrained variational inequality problem is transformed into a semismooth system of equations with the help of Fischer–Burmeister operators over second-order cones. The Clarke generalized differential of the semismooth mapping is presented. A modified Newton method with Armijo line search is proved to have global convergence with local superlinear rate of convergence under certain assumptions on the variational inequality problem. An illustrative example is given to show how the globally convergent method works.     

A class of nonlinear proximal point algorithms for variational inequality problems

This work is motivated by a recent work on an extended linear proximal point algorithm (PPA) [B.S. He, X.L. Fu, and Z.K. Jiang, Proximal-point algorithm using a linear proximal term, J. Optim. Theory Appl. 141 (2009), pp. 299–319], which aims at relaxing the requirement of the linear proximal term of classical PPA. In this paper, we make further contributions along the line. First, we generalize the linear PPA-based contraction method by using a nonlinear proximal term instead of the linear one. A notable superiority over traditional PPA-like methods is that the nonlinear proximal term of the proposed method may not necessarily be a gradient of any functions. In addition, the nonlinearity of the proximal term makes the new method more flexible. To avoid solving a variational inequality subproblem exactly, we then propose an inexact version of the developed method, which may be more computationally attractive in terms of requiring lower computational cost. Finally, we gainfully employ our new methods to solve linearly constrained convex minimization and variational inequality problems.     

A continuous method model for solving general variational inequality

Based on the projection operator, this paper presents a continuous method model for solving general variational inequality problems (VIPs) with bound constraints. A main feature of the proposed model is that it does not involve any form of matrix information in analysing its convergence properties. Under some reasonable assumptions, the convergence results of the proposed method model are established. Numerical results on some problems show that the proposed approach is efficient and can be applied to solve large scale VIPs with bound constraints.     

A hybrid LQP-based method for structured variational inequalities

This paper proposes a hybrid LQP-based method (LQP, logarithmic-quadratic proximal) to solve a class of structured variational inequalities. In this method, an intermediate point is produced by solving a nonlinear equation system based on the LQP method; a descent direction is constructed using this iterate and the new iterate is obtained by a convex combination of the previous point and the one generated by a projection-type method along this descent direction. Global convergence of the new method is proved under mild assumptions. Preliminary numerical results for traffic equilibrium problems verify the computational preferences of the new method.     

A preconditioner for solving large-scale variational inequality problems by a semismooth inexact approach

The numerical solution of a large-scale variational inequality problem can be obtained using the generalization of an inexact Newton method applied to a semismooth nonlinear system. This approach requires a sparse and large linear system to be solved at each step. In this work we obtain an approximate solution of this system by the LSQR algorithm of Paige and Saunders combined with a convenient preconditioner that is a variant of the incomplete LU–factorization. Since the computation of the factorization of the preconditioning matrix can be very expensive and memory consuming, we propose a preconditioner that admits block-factorization. Thus the direct factorization is only applied to submatrices of smaller sizes. Numerical experiments on a set of test-problems arising from the literature show the effectiveness of this approach.     

A new modified Goldstein-Levitin-Polyakprojection method for variational inequality problems

In this paper, we first show that the adjustment parameter in the step size choice strategy of the modified Goldstein-Levitin-Polyak projection method proposed by He et al. for asymmetric strongly monotone variational inequality problems can be bounded away from zero by a positive constant. Under this observation, we propose a new step size rule which seems to be more practical and robust than the original one. We show that the new modified method is globally convergent under the same conditions and report some computational results to illustrate the method.     

A variational method for unconstrained optimization problems

We introduce a new method for solving an unconstrained optimization problem. The method is based on the solution of a variational inequality problem and may be considered a modified trust region algorithm. We prove the convergence of the method and present some numerical results.     

Strong convergence theorems based on a new modified extragradient method for variational inequality problems and fixed point problems in Banach spaces

In this paper, we introduce a new iterative algorithm by a modified extragradient method for finding a common element of the set of solutions of a general variational inequality and the set of common fixed points of an infinite family of k_i-strict pseudocontractions in a Banach space. We obtain some strong convergence theorems under suitable conditions. The results obtained in this paper improve and extend the recent ones announced by many others.     

Hybrid algorithm for generalized mixed equilibrium problems and variational inequality problems and fixed point problems

In this paper, we introduce a new iterative algorithm by hybrid method for finding a common element of the set of solutions of finite general mixed equilibrium problems and the set of solutions of a general variational inequality problem for finite inverse strongly monotone mappings and the set of common fixed points of infinite family of strictly pseudocontractive mappings in a real Hilbert space. Then we prove strong convergence of the scheme to a common element of the three above described sets. Our result improves and extends the corresponding results announced by many others.     

A viscosity approximation scheme for finite mixed equilibrium problems and variational inequality problems and fixed point problems

In this paper, we introduce a new iterative scheme for finding a common element of the set of solutions of finite mixed equilibrium problems, the set of solutions of variational inequalities for two cocoercive mappings, the set of common fixed points of an infinite family of nonexpansive mappings and the set of common fixed points of a nonexpansive semigroup in Hilbert space. Then we prove a strong convergence theorem under some suitable conditions. The results obtained in this paper extend and improve many recent ones announced by many others.     

Evolutionary variational inequality with long-term memory and applications to economic networks

The aim of this paper is to present the relation between an evolutionary variational inequality with long-term memory and Lagrange multipliers. More precisely, we study the oligopolistic market equilibrium problem in which the profit function depends also on previous events of the market by means of a long-term memory which takes into account the previous states of the equilibrium. Moreover, thanks to the variational formulation, we are able to show existence and regularity results for equilibrium solutions. Then, we apply the infinite dimensional duality theory through which we obtain the existence of Lagrange multipliers which are great utility in order to understand the behaviour of the market. Finally, an example is provided, which allows to analyse the influence of the long-term memory on the equilibrium solution.     

A method for non-differentiable optimization problems

We introduce a new method for solving a class of non-smooth unconstrained optimization problems. The method constructs a sequence<texlscub>x _k </texlscub>in ? ⁿ , and at the kth iteration, a search direction h _k is considered, where h _k is a solution to a variational inequality problem. A convergence theorem for our algorithm model and its discrete version can be easily proved. Furthermore, preliminary computational results show that the new method performs quite well and can compete with other methods.     

Generalized projection method for a variational inequality system with different mapping in Banach spaces

The approximate solution of a system for variational inequality with different mapping in Hilbert spaces has been studied, based on the convergence of projection methods. However, little research has been done in Banach space. The primary reason is that projection mapping lacks preferable properties in Banach space. In this paper, we introduce generalized projection methods. By using these methods, the results presented in this paper have extended the main results of Chang et al.’s and Huang and Noor’s recent works [S.S. Chang, H.W. Joseph Lee, C.K. Chan, Generalized system for relaxed cocoercive variational inequalities in Hilbert spaces, Appl. Math. Lett. 20 (2007) 329–334; Zhenyu Huang, Muhammad Aslam Noor, An explicit projection method for a system of nonlinear variational inequalities with different (γ,r)-cocoercive mappings, Appl. Math. Comput. 190 (2007) 356–361] from Hilbert spaces to Banach space.     

The quasi-newtonian method of solution of convex variational inequalities with descent decomposition

The problem of numerical solution of convex variational inequalities with nonlinear constraints is considered. An equivalent optimization problem is constructed. To solve it, a Newtonian-type method with descent decomposition with respect to direct and dual variables of a variational inequality is developed. The nonlocal convergence of the algorithm and the superlinear rate of convergence in a neighborhood of the solution are proved. Translated from Kibemetika i Sistemnyi Analiz, No. 4, pp. 90–105, July–August, 1999.     

A quasi-Newton method for unconstrained non-smooth problems

We present a method, based on a variational problem, for solving a non-smooth unconstrained optimization problem. We assume that the objective function is a Lipschitz continuous and a regular function. In this case the function of our variational problem is semismooth and a quasi-Newton method may be used to solve the variational problem. A convergence theorem for our algorithm and its discrete version is also proved. Preliminary computational results show that the method performs quite well and can compete with other methods.     

A variational inequality approach for marketable pollution permits

In this paper, we present a variational inequality framework for the modeling, qualitative analysis, and computation of equilibria in markets in pollution permits. The models developed herein allow for different modes of competitive behavior, including oligopolistic behavior, while taking into account the cost of polluting plus the prices of the licenses to pollute at different receptor points. The models deal explicitly with spatial differentiation and also ensure that the imposed environmental quality standards are met through the initial allocation of licenses. An algorithm is proposed, along with convergence results, to compute the profit-maximized quantities of the firms' products and quantities of emissions, as well as the equilibrium allocation of licenses and their prices. Numerical examples are included to illustrate the approach. This is the first time that this methodology is utilized in environmental economics.