Method For Monotone Variational Inequalities Research Articles

A prediction-correction based proximal method for monotone variational inequalities with linear constraints

<abstract><p>The monotone variational inequalities are being widely used as mathematical tools for studying optimal control problems and convex programming. In this paper, we propose a new prediction-correction method for monotone variational inequalities with linear constraints. The method consists of two procedures. The first procedure (prediction) utilizes projections to generate a predictor. The second procedure (correction) produces the new iteration via some minor computations. The main advantage of the method is that its main computational effort only depends on evaluating the resolvent mapping of the monotone operator, and its primal and dual step sizes can be enlarged. We prove the global convergence of the method. Numerical results are provided to demonstrate the efficiency of the method.</p></abstract>

An efficient projection-type method for monotone variational inequalities in Hilbert spaces

We consider the monotone variational inequality problem in a Hilbert space and describe a projection-type method with inertial terms under the following properties: (a) The method generates a strongly convergent iteration sequence; (b) The method requires, at each iteration, only one projection onto the feasible set and two evaluations of the operator; (c) The method is designed for variational inequality for which the underline operator is monotone and uniformly continuous; (d) The method includes an inertial term. The latter is also shown to speed up the convergence in our numerical results. A comparison with some related methods is given and indicates that the new method is promising.

A Modified Projected Gradient Method for Monotone Variational Inequalities

In this paper, we investigate and analyze classical variational inequalities with Lipschitz continuous and monotone mapping in real Hilbert space. The projected reflected gradient method, with varying step size, requires at most two projections onto the feasible set and one value of the mapping per iteration. We modify the method with a simple structure; a weak convergence theorem for our algorithm is proved without any requirement of additional projections and the knowledge of the Lipschitz constant of the mapping. Meanwhile, R-linear convergence rate is obtained under strong monotonicity assumption of the mapping. Preliminary results from numerical experiments are performed.

Strong convergence of a double projection-type method for monotone variational inequalities in Hilbert spaces

We introduce a projection-type algorithm for solving monotone variational inequality problems in real Hilbert spaces without assuming Lipschitz continuity of the corresponding operator. We prove that the whole sequence of iterates converges strongly to a solution of the variational inequality. The method uses only two projections onto the feasible set in each iteration in contrast to other strongly convergent algorithms which either require plenty of projections within a step size rule or have to compute projections on possibly more complicated sets. Some numerical results illustrate the behavior of our method.

Proximal extrapolated gradient methods for variational inequalities

The paper concerns with novel first-order methods for monotone variational inequalities. They use a very simple linesearch procedure that takes into account a local information of the operator. Also, the methods do not require Lipschitz continuity of the operator and the linesearch procedure uses only values of the operator. Moreover, when the operator is affine our linesearch becomes very simple, namely, it needs only simple vector–vector operations. For all our methods, we establish the ergodic convergence rate. In addition, we modify one of the proposed methods for the case of a composite minimization. Preliminary results from numerical experiments are quite promising.

An operator splitting method for monotone variational inequalities with a new perturbation strategy

In variational inequalities arising from applications such as engineering, economics and transportation, partial mappings are usually unknown, e.g., the demand function in traffic assignment problem. As a consequence, classical methods can not deal with this class of problems. On the other hand, the recently developed methods require restrictive conditions such as strong monotonicity of some mappings, which excludes many interesting applications. In this paper, we propose an operator splitting method with a new perturbation strategy for solving variational inequality problems with partially unknown mappings. Under the mild condition that the underlying mapping is monotone, we prove the global convergence of the method. We also report some preliminary numerical results which show that the new algorithm is also interesting from the numerical point of view.

On the Convergence Rate of a Class of Proximal-Based Decomposition Methods for Monotone Variational Inequalities

A unified efficient algorithm framework of proximal-based decomposition methods has been proposed for monotone variational inequalities in 2012, while only global convergence is proved at the same time. In this paper, we give a unified proof on the $$\mathcal{{O}}(1/t)$$ iteration complexity, together with the linear convergence rate for this kind of proximal-based decomposition methods. Besides the $$\varepsilon $$ -optimal iteration complexity result defined by variational inequality, the non-ergodic relative error of adjacent iteration points is also proved to decrease in the same order. Further, the linear convergence rate of this algorithm framework can be constructed based on some special variational inequality properties, without necessary strong monotone conditions.

Projected Reflected Gradient Methods for Monotone Variational Inequalities

This paper is concerned with some new projection methods for solving variational inequality problems with monotone and Lipschitz-continuous mapping in Hilbert space. First, we propose the projected reflected gradient algorithm with a constant stepsize. It is similar to the projected gradient method, namely, the method requires only one projection onto the feasible set and only one value of the mapping per iteration. This distinguishes our method from most other projection-type methods for variational inequalities with monotone mapping. Also we prove that it has R-linear rate of convergence under the strong monotonicity assumption. The usual drawback of algorithms with constant stepsize is the requirement to know the Lipschitz constant of the mapping. To avoid this, we modify our first algorithm so that the algorithm needs at most two projections per iteration. In fact, our computational experience shows that such cases with two projections are very rare. This scheme, at least theoretically, seems to be very effective. All methods are shown to be globally convergent to a solution of the variational inequality. Preliminary results from numerical experiments are quite promising.

Variant extragradient-type method for monotone variational inequalities

Abstract Korpelevich’s extragradient method has been studied and extended extensively due to its applicability to the whole class of monotone variational inequalities. In the present paper, we propose a variant extragradient-type method for solving monotone variational inequalities. Convergence analysis of the method is presented under reasonable assumptions on the problem data. MSC:47H05, 47J05, 47J25.

A New Implementable Prediction-Correction Method for Monotone Variational Inequalities with Separable Structure

The monotone variational inequalities capture various concrete applications arising in many areas. In this paper, we develop a new prediction-correction method for monotone variational inequalities with separable structure. The new method can be easily implementable, and the main computational effort in each iteration of the method is to evaluate the proximal mappings of the involved operators. At each iteration, the algorithm also allows the involved subvariational inequalities to be solved in parallel. We establish the global convergence of the proposed method. Preliminary numerical results show that the new method can be competitive with Chen's proximal-based decomposition method in Chen and Teboulle (1994).

Iteration-Complexity of a Newton Proximal Extragradient Method for Monotone Variational Inequalities and Inclusion Problems

In a recent paper by Monteiro and Svaiter, a hybrid proximal extragradient (HPE) framework was used to study the iteration-complexity of a first-order (or, in the context of optimization, second-order) method for solving monotone nonlinear equations. The purpose of this paper is to extend this analysis to study a prox-type first-order method for monotone smooth variational inequalities and inclusion problems consisting of the sum of a smooth monotone map and a maximal monotone point-to-set operator. Each iteration of the method computes an approximate solution of a proximal subproblem obtained by linearizing the smooth part of the operator in the corresponding proximal equation for the original problem, which is then used to perform an extragradient step as prescribed by the HPE framework. Both pointwise and ergodic iteration-complexity results are derived for the aforementioned first-order method using corresponding results obtained here for a subfamily of the HPE framework.

Strong convergence of a hybrid method for monotone variational inequalities and fixed point problems

Abstract In this paper, we suggest a hybrid method for finding a common element of the set of solution of a monotone, Lipschitz-continuous variational inequality problem and the set of common fixed points of an infinite family of nonexpansive mappings. The proposed iterative method combines two well-known methods: extragradient method and CQ method. Under some mild conditions, we prove the strong convergence of the sequences generated by the proposed method. Mathematics Subject Classification (2000): 47H05; 47H09; 47H10; 47J05; 47J25.

An exact penalty method for monotone variational inequalities and order optimal algorithms for finding saddle points

We consider variational inequalities in a Banach space. We propose an exact penalty method which enables one to remove functional constraints. The obtained result is used for constructing optimal (in the sense of complexity) iterative schemes for finding saddle points under functional constraints.

An inexact parallel splitting augmented Lagrangian method for monotone variational inequalities with separable structures

Splitting methods have been extensively studied in the context of convex programming and variational inequalities with separable structures. Recently, a parallel splitting method based on the augmented Lagrangian method (abbreviated as PSALM) was proposed in He (Comput. Optim. Appl. 42:195---212, 2009) for solving variational inequalities with separable structures. In this paper, we propose the inexact version of the PSALM approach, which solves the resulting subproblems of PSALM approximately by an inexact proximal point method. For the inexact PSALM, the resulting proximal subproblems have closed-form solutions when the proximal parameters and inexact terms are chosen appropriately. We show the efficiency of the inexact PSALM numerically by some preliminary numerical experiments.

Modified self-adaptive projection method for solving pseudomonotone variational inequalities

In this paper, a self-adaptive projection method with a new search direction for solving pseudomonotone variational inequality (VI) problems is proposed, which can be viewed as an extension of the methods in [B.S. He, X.M. Yuan, J.Z. Zhang, Comparison of two kinds of prediction-correction methods for monotone variational inequalities, Computational Optimization and Applications 27 (2004) 247–267] and [X.H. Yan, D.R. Han, W.Y. Sun, A self-adaptive projection method with improved step-size for solving variational inequalities, Computers & Mathematics with Applications 55 (2008) 819–832]. The descent property of the new search direction is proved, which is useful to guarantee the convergence. Under the relatively relaxed condition that F is continuous and pseudomonotone, the global convergence of the proposed method is proved. Numerical experiments are provided to illustrate the efficiency of the proposed method.

Global convergence of an inexact operator splitting method for monotone variational inequalities

Recently, Han (Han D, Inexact operator splitting methods with self-adaptive strategy for variational inequality problems, Journal of Optimization Theory and Applications132, 227-243 (2007))proposed an inexact operator splitting method for solvingvariational inequality problems. It has advantage over the classicaloperator splitting method of Douglas-Peaceman-Rachford-Vargaoperator splitting methods (DPRV methods) and some of theirvariants, since it adopts a very flexible approximate rule insolving the subproblem in each iteration.However, its convergence is established under somewhat stringent condition that the underlyingmapping $F$ is strongly monotone. In this paper, we mainly establish the globalconvergence of the method under weaker condition that the underlying mapping$F$ is monotone, which extends the fields of applications of the method relatively.Some numerical results are also presented to illustrate the method.

A Hybrid Method for Monotone Variational Inequalities Involving Pseudocontractions

AbstractWe use strongly pseudocontraction to regularize the following ill-posed monotone variational inequality: finding a point "Equation missing" with the property "Equation missing" such that "Equation missing", "Equation missing" where "Equation missing", "Equation missing" are two pseudocontractive self-mappings of a closed convex subset "Equation missing" of a Hilbert space with the set of fixed points "Equation missing". Assume the solution set "Equation missing" of (VI) is nonempty. In this paper, we introduce one implicit scheme which can be used to find an element "Equation missing". Our results improve and extend a recent result of (Lu et al. 2009).

Proximal alternating direction-based contraction methods for separable linearly constrained convex optimization

Alternating direction method (ADM) has been well studied in the context of linearly constrained convex programming problems. Recently, because of its significant efficiency and easy implementation in novel applications, ADM is extended to the case where the number of separable parts is a finite number. The algorithmic framework of the extended method consists of two phases. At each iteration, it first produces a trial point by using the usual alternating direction scheme, and then the next iterate is updated by using a distance-descent direction offered by the trial point. The generated sequence approaches the solution set monotonically in the Fejer sense, and the method is called alternating direction-based contraction (ADBC) method. In this paper, in order to simplify the subproblems in the first phase, we add a proximal term to the objective function of the minimization subproblems. The resulted algorithm is called proximal alternating direction-based contraction (PADBC) methods. In addition, we present different linearized versions of the PADBC methods which substantially broaden the applicable scope of the ADBC method. All the presented algorithms are guided by a general framework of the contraction methods for monotone variational inequalities, and thus, the convergence follows directly.

Proximal-like contraction methods for monotone variational inequalities in a unified framework II: general methods and numerical experiments

Approximate proximal point algorithms (abbreviated as APPAs) are classical approaches for convex optimization problems and monotone variational inequalities. In Part I of this paper (He et al. in Proximal-like contraction methods for monotone variational inequalities in a unified framework I: effective quadruplet and primary methods, 2010), we proposed a unified framework consisting of an effective quadruplet and a corresponding accepting rule. Under the framework, various existing APPAs can be grouped in the same class of methods (called primary or elementary methods) which adopt one of the geminate directions in the effective quadruplet and take the unit step size. In this paper, we extend the primary methods by using the same effective quadruplet and the accepting rule. The extended (general) contraction methods need only minor extra even negligible costs in each iteration, whereas having better properties than the primary methods in sense of the distance to the solution set. A set of matrix approximation examples as well as six other groups of numerical experiments are constructed to compare the performance between the primary (elementary) and extended (general) methods. As expected, the numerical results show the efficiency of the extended (general) methods are much better than that of the primary (elementary) ones.

Proximal-like contraction methods for monotone variational inequalities in a unified framework I: Effective quadruplet and primary methods

Approximate proximal point algorithms (abbreviated as APPAs) are classical approaches for convex optimization problems and monotone variational inequalities. To solve the subproblems of these algorithms, the projection method takes the iteration in form of u k+1=P ? [u k ?? k d k ]. Interestingly, many of them can be paired such that $\tilde{u}^{k} = P_{\varOmega}[u^{k} - \beta_{k}F(v^{k})] = P_{\varOmega}[\tilde {u}^{k} - (d_{2}^{k} - G d_{1}^{k})]$ , where inf?{β k }>0 and G is a symmetric positive definite matrix. In other words, this projection equation offers a pair of directions, i.e., $d_{1}^{k}$ and $d_{2}^{k}$ for each step. In this paper, for various APPAs we present a unified framework involving the above equations. Unified characterization is investigated for the contraction and convergence properties under the framework. This shows some essential views behind various outlooks. To study and pair various APPAs for different types of variational inequalities, we thus construct the above equations in different expressions according to the framework. Based on our constructed frameworks, it is interesting to see that, by choosing one of the directions ( $d_{1}^{k}$ and $d_{2}^{k}$ ) those studied proximal-like methods always utilize the unit step size namely ? k ?1.