A modified inexact implicit method for mixed variational inequalities

In this paper, we suggest and analyze an inexact implicit method with a variable parameter for mixed variational inequalities by using a new inexactness restriction. Under certain conditions, the global convergence of the proposed method is proved. Some preliminary computational results are given to illustrate the efficiency of the new inexactness restriction. The results proved in this paper may be viewed as improvement and refinement of the previously known results.     

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.     

An improved Goldstein's type method for a class of variant variational inequalities

This paper aims at presenting an improved Goldstein's type method for a class of variant variational inequalities. In particular, the iterate computed by an existing Goldstein's type method [He, A Goldstein's type projection method for a class of variant variational inequalities J. Comput. Math. 17(4) (1999) 425–434]. is used to construct a descent direction, and thus the new method generates the new iterate by searching the optimal step size along the descent direction. Some restrictions on the involving functions of the existing Goldstein's type methods are relaxed, while the global convergence of the new method is proved without additional assumptions. The computational superiority of the new method is verified by the comparison to some existing methods.     

Self-adaptive methods for general variational inequalities

It is well known that the general variational inequalities are equivalent to the fixed point problems and the Wiener-Hopf equations. In this paper, we use these alternative equivalent formulations to suggest and analyze some new self-adaptive iterative methods for solving the general variational inequalities. Our results can be viewed as a significant extension of the previously known results for variational inequalities. An example is given to illustrate the efficiency of the proposed method.     

An LQP-based descent method for structured monotone variational inequalities

This paper proposes a descent method to solve a class of structured monotone variational inequalities. The descent directions are constructed from the iterates generated by a prediction-correction method [B.S. He, Y. Xu, X.M. Yuan, A logarithmic-quadratic proximal prediction-correction method for structured monotone variational inequalities, Comput. Optim. Appl. 35 (2006) 19-46], which is based on the logarithmic-quadratic proximal method. In addition, the optimal step-sizes along these descent directions are identified to accelerate the convergence of the new method. Finally, some numerical results for solving traffic equilibrium problems are reported.     

Homotopy method for solving ball-constrained variational inequalities

In this paper, we present a smoothing homotopy method for solving ball-constrained variational inequalities by utilizing a similar Chen-Harker-Kanzow-Smale function to smooth Robinson’s normal equation. Without any monotonicity condition on the defining map F, for the starting point chosen almost everywhere in Rⁿ, the existence and convergence of the homotopy pathway are proven. Numerical experiments illustrate that the method is feasible and effective.     

Homotopy method for solving variational inequalities with bounded box constraints

In this paper, using the Gabriel–Moré smoothing function of the median function, a smooth homotopy method for solving nonsmooth equation reformulation of bounded box constrained variational inequality problem VIP(l,u,F$l,u,F$) is given. Without any monotonicity condition on the defining map F$F$, for starting point chosen almost everywhere in Rⁿ$R^n$, existence and convergence of the homotopy pathway are proven. Nevertheless, it is also proven that, if the starting point is chosen to be an interior point of the box, the proposed homotopy method can also serve as an interior point method.     

A modified projection method for solving co-coercive variational inequalities

This paper presents a modified projection method for solving variational inequalities, which can be viewed as an improvement of the method of Yan, Han and Sun [X.H. Yan, D.R. Han, W.Y. Sun, A modified projection method with a new direction for solving variational inequalities, Applied Mathematics and Computation 211 (2009) 118-129], by adopting a new prediction step. Under the same assumptions, we establish the global convergence of the proposed algorithm. Some preliminary computational results are reported.     

Alternating proximal algorithms for linearly constrained variational inequalities: Application to domain decomposition for PDE’s

Let X,Y,Z be real Hilbert spaces, let f:X→R∪{+∞}, g:Y→R∪{+∞} be closed convex functions and let A:X→Z, B:Y→Z be linear continuous operators. Let us consider the constrained minimization problem Given a sequence (γ_n) which tends toward 0 as n→+∞, we study the following alternating proximal algorithm where α and ν are positive parameters. It is shown that if the sequence (γ_n) tends moderately slowly toward 0, then the iterates of (A) weakly converge toward a solution of (P). The study is extended to the setting of maximal monotone operators, for which a general ergodic convergence result is obtained. Applications are given in the area of domain decomposition for PDE’s.     

An interior point type QP-free algorithm with superlinear convergence for inequality constrained optimization

A feasible interior point type algorithm is proposed for the inequality constrained optimization. Iterate points are prevented from leaving to interior of the feasible set. It is observed that the algorithm is merely necessary to solve three systems of linear equations with the same coefficient matrix. Under some suitable conditions, superlinear convergence rate is obtained. Some numerical results are also reported.     

A new inexact alternating directions method for monotone variational inequalities

The alternating directions method (ADM) is an effective method for solving a class of variational inequalities (VI) when the proximal and penalty parameters in sub-VI problems are properly selected. In this paper, we propose a new ADM method which needs to solve two strongly monotone sub-VI problems in each iteration approximately and allows the parameters to vary from iteration to iteration. The convergence of the proposed ADM method is proved under quite mild assumptions and flexible parameter conditions. Received: January 4, 2000 / Accepted: October 2001?Published online February 14, 2002     

Finite termination of a Newton-type algorithm for a class of affine variational inequality problems

Many optimization problems can be reformulated as a system of equations. One may use the generalized Newton method or the smoothing Newton method to solve the reformulated equations so that a solution of the original problem can be found. Such methods have been powerful tools to solve many optimization problems in the literature. In this paper, we propose a Newton-type algorithm for solving a class of monotone affine variational inequality problems (AVIPs for short). In the proposed algorithm, the techniques based on both the generalized Newton method and the smoothing Newton method are used. In particular, we show that the algorithm can find an exact solution of the AVIP in a finite number of iterations under an assumption that the solution set of the AVIP is nonempty. Preliminary numerical results are reported.     

An additional projection step to He and Liao's method for solving variational inequalities

In this paper, we proposed a modified extragradient method for solving variational inequalities. The method can be viewed as an extension of the method proposed by He and Liao [Improvement of some projection methods for monotone variational inequalities, J. Optim. Theory Appl. 112 (2002) 111–128], by performing an additional projection step at each iteration and another optimal step length is employed to reach substantial progress in each iteration. We used a self-adaptive technique to adjust parameter ρ$\rho$ at each iteration. Under certain conditions, the global convergence of the proposed method is proved. Preliminary numerical experiments are included to compare our method with some known methods.     

A p-version finite element method for nonlinear elliptic variational inequalities in 2D

This article introduces and analyzes a p-version FEM for variational inequalities resulting from obstacle problems for some quasi-linear elliptic partial differential operators. We approximate the solution by controlling the obstacle condition in images of the Gauss–Lobatto points. We show existence and uniqueness for the discrete solution u _p from the p-version for the obstacle problem. We prove the convergence of u _p towards the solution with respect to the energy norm, and assuming some additional regularity for the solution we derive an a priori error estimate. In numerical experiments the p-version turns out to be superior to the h-version concerning the convergence rate and the number of unknowns needed to achieve a certain exactness of the approximation.     

Error estimates for the approximation of semicoercive variational inequalities

Summary. An abstract error estimate for the approximation of semicoercive variational inequalities is obtained provided a certain condition holds for the exact solution. This condition turns out to be necessary as is demonstrated analytically and numerically. The results are applied to the finite element approximation of Poisson's equation with Signorini boundary conditions and to the obstacle problem for the beam with no fixed boundary conditions. For second order variational inequalities the condition is always satisfied, whereas for the beam problem the condition holds if the center of forces belongs to the interior of the convex hull of the contact set. Applying the error estimate yields optimal order of convergence in terms of the mesh size . The numerical convergence rates observed are in good agreement with the predicted ones. Received August 16, 1993 / Revised version received March 21, 1994     

On the computation of an element of Clarke generalized Jacobian for a vector-valued max function

In this paper, we present an algorithm for calculating an element of Clarke generalized Jacobian for a vector-valued max-type function. The algorithm reduces the computational cost of an existing algorithm.     

On combining feasibility,descent and superlinear convergence in inequality constrained optimization

Extension of quasi-Newton techniques from unconstrained to constrained optimization via Sequential Quadratic Programming (SQP) presents several difficulties. Among these are the possible inconsistency, away from the solution, of first order approximations to the constraints, resulting in infeasibility of the quadratic programs; and the task of selecting a suitable merit function, to induce global convergence. In ths case of inequality constrained optimization, both of these difficulties disappear if the algorithm is forced to generate iterates that all satisfy the constraints, and that yield monotonically decreasing objective function values. (Feasibility of the successive iterates is in fact required in many contexts such as in real-time applications or when the objective function is not well defined outside the feasible set.) It has been recently shown that this can be achieved while preserving local two-step superlinear convergence. In this note, the essential ingredients for an SQP-based method exhibiting the desired properties are highlighted. Correspondingly, a class of such algorithms is described and analyzed. Tests performed with an efficient implementation are discussed.This research was supported in part by NSF's Engineering Research Centers Program No. NSFD-CDR-88-03012, and by NSF grants No. DMC-84-51515 and DMC-88-15996.     

An ODE-based trust region method for unconstrained optimization problems

In this paper, a new trust region algorithm is proposed for solving unconstrained optimization problems. This method can be regarded as a combination of trust region technique, fixed step-length and ODE-based methods. A feature of this proposed method is that at each iteration, only a system of linear equations is solved to obtain a trial step. Another is that when a trial step is not accepted, the method generates an iterative point whose step-length is defined by a formula. Under some standard assumptions, it is proven that the algorithm is globally convergent and locally superlinear convergent. Preliminary numerical results are reported.