A new projection and contraction method for linear variational inequalities

In this paper, we presented a new projection and contraction method for linear variational inequalities, which can be regarded as an extension of He's method. The proposed method includes several new methods as special cases. We used a self-adaptive technique to adjust parameter β at each iteration. This method is simple, the global convergence is proved under the same assumptions as He's method. Some preliminary computational results are given to illustrate the efficiency of the proposed method.     

一种新的求解拟单调变分不等式的压缩投影算法

2020年Liu和Yang提出了求解Hilbert空间中拟单调且Lipschitz连续的变分不等式问题的投影算法,简称LYA。本文在欧氏空间中提出了一种新的求解拟单调变分不等式的压缩投影算法,简称NPCA。新算法削弱了LYA中映射的Lipschitz连续性。在映射连续、拟单调且对偶变分不等式解集非空的条件下得到了NPCA所生成点列的聚点是解的结论。当变分不等式的解集还满足一定条件时,得到了NPCA的全局收敛性。数值实验结果表明NPCA所需的迭代步数少于LYA的迭代步数,NPCA在高维拟单调例子中所需的计算机耗时也更少。     

A class of projection and contraction methods for monotone variational inequalities

In this paper we introduce a new class of iterative methods for solving the monotone variational inequalities $$u* \in \Omega , (u - u*)^T F(u*) \geqslant 0, \forall u \in \Omega .$$ Each iteration of the methods presented consists essentially only of the computation ofF(u), a projection to Ω,v:=P _Ω[u-F(u)], and the mappingF(v). The distance of the iterates to the solution set monotonically converges to zero. Both the methods and the convergence proof are quite simple.     

Self-adaptive gradient projection algorithms for variational inequalities involving non-Lipschitz continuous operators

Numerical Algorithms - In this paper, we introduce a self-adaptive inertial gradient projection algorithm for solving monotone or strongly pseudomonotone variational inequalities in real Hilbert...     

Accelerating the convergence of the diagonalization and projection algorithms for finite-dimensional variational inequalities

This paper presents an acceleration step for the linearly convergent diagonalization and projection algorithms for finite-dimensional variational inequalities which is reminiscent of a PARTAN step in nonlinear programming. After establishing the convergence of this technique for both algorithms, several numerical examples are presented to illustrate the sometimes dramatic savings in computation time which this simple acceleration step yields.     

Approximately solving multi-valued variational inequalities by using a projection and contraction algorithm

A projection and contraction algorithm for solving multi-valued variational inequalities is proposed. The algorithm is proved to converge globally to a solution of a given multi-valued variational inequality under standard conditions. We present an analysis of the convergence rate. Finally, preliminary computational experiments illustrate the advantage of the proposed algorithm.     

A relaxed projection method for variational inequalities

This paper presents a modification of the projection methods for solving variational inequality problems. Each iteration of the proposed algorithm consists of projection onto a halfspace containing the given closed convex set rather than the latter set itself. The algorithm can thus be implemented very easily and its global convergence to the solution can be established under suitable conditions.This work was supported in part by Scientific Research Grant-in-Aid from the Ministry of Education, Science and Culture, Japan.     

Modified projection method for pseudomonotone variational inequalities

We consider and analyze a new projection method for solving pseudomonotone variational inequalities by modifying the extragradient method. The modified method converges for pseudomonotone Lipschitz continuous operators, which is a much weaker condition than monotonicity. The new iterative method differs from the existing projection methods. Our proof of convergence is very simple as compared with other methods.     

Self-adaptive projection method for co-coercive variational inequalities

In some real-world problems, the mapping of the variational inequalities does not have any explicit forms and only the function value can be evaluated or observed for given variables. In this case, if the mapping is co-coercive, the basic projection method is applicable. However, in order to determine the step size, the existing basic projection method needs to know the co-coercive modulus in advance. In practice, usually even if the mapping can be characterized co-coercive, it is difficult to evaluate the modulus, and a conservative estimation will lead an extremely slow convergence. In view of this point, this paper presents a self-adaptive projection method without knowing the co-coercive modulus. We also give a real-life example to demonstrate the practicability of the proposed method.     

Iterative algorithms for generalized monotone variational inequalities

We propose some new iterative methods for solving the generalized variational inequalities where the underlying operator T is monotone. These methods may be viewed as projection-type methods. Convergence of these methods requires that the operator T is only monotone. The methods and the proof of the convergence are very simple. The results proved in this paper also represent a significant improvement and refinement of the known results.     

Interior-point algorithms for monotone affine variational inequalities

Given ann×n matrixM, a vectorq in ⁿ , a polyhedral convex setX={x|Axb, Bx=d}, whereA is anm×n matrix andB is ap×n matrix, the affinne variational inequality problem is to findxX such that (Mx+q) ^T (y–x)0 for allyX. IfM is positive semidefinite (not necessarily symmetric), the affine variational inequality can be transformeo to a generalized complementarity problem, which can be solved in polynomial time using interior-point algorithms due to Kojima et al. We develop interior-point algorithms that exploit the particular structure of the problem, rather than direictly reducing the problem to a standard linear complemntarity problem.This work was partially supported by the Air Force Office of Scientific Research, Grant AFOSR-89-0410 and the National Science Foundation, Grant CCR-91-57632.The authors acknowledge Professor Osman Güler for pointing out the valoidity of Theorem 2.1 without further assumptions and the proof to that effect. They are also grateful for his comments to improve the presentation of this paper.     

Modified projection method for strongly pseudomonotone variational inequalities

A modified projection method for strongly pseudomonotone variational inequalities is considered. Strong convergence and error estimates for the sequences generated by this method are studied in two versions of the method: the stepsizes are chosen arbitrarily from a given fixed closed interval and the stepsizes form a non-summable decreasing sequence of positive real numbers. We also propose some interesting examples to analyze the obtained results.     

A new double projection algorithm for variational inequalities

We present a modification of a double projection algorithm proposed by Solodov and Svaiter for solving variational inequalities. The main modification is to use a different Armijo-type linesearch to obtain a hyperplane strictly separating current iterate from the solutions of the variational inequalities. Our method is proven to be globally convergent under very mild assumptions. If in addition a certain error bound holds, we analyze the convergence rate of the iterative sequence. We use numerical experiments to compare our method with that proposed by Solodov and Svaiter.     

On variable-step relaxed projection algorithm for variational inequalities

Projection algorithms are practically useful for solving variational inequalities (VI). However some among them require the knowledge related to VI in advance, such as Lipschitz constant. Usually it is impossible in practice. This paper studies the variable-step basic projection algorithm and its relaxed version under weakly co-coercive condition. The algorithms discussed need not know constant/function associated with the co-coercivity or weak co-coercivity and the step-size is varied from one iteration to the next. Under certain conditions the convergence of the variable-step basic projection algorithm is established. For the practical consideration, we also give the relaxed version of this algorithm, in which the projection onto a closed convex set is replaced by another projection at each iteration and latter is easy to calculate. The convergence of relaxed scheme is also obtained under certain assumptions. Finally we apply these two algorithms to the Split Feasibility Problem (SFP).     

Mixed projection methods for systems of variational inequalities

Let H be a real Hilbert space. Let be bounded and continuous mappings where D(F) and D(K) are closed convex subsets of H. We introduce and consider the following system of variational inequalities: find [u ^*,v ^*]∈D(F) × D(K) such that This system of variational inequalities is closely related to a pseudomonotone variational inequality. The well-known projection method is extended to develop a mixed projection method for solving this system of variational inequalities. No invertibility assumption is imposed on F and K. The operators K and F also need not be defined on compact subsets of H.      

Two modified inertial projection algorithms for bilevel pseudomonotone variational inequalities with applications to optimal control problems

Numerical Algorithms - In this paper, we investigate two new algorithms for solving bilevel pseudomonotone variational inequality problems in real Hilbert spaces. The advantages of our algorithms...     

Some algorithms for general monotone mixed variational inequalities

We consider some new iterative methods for solving general monotone mixed variational inequalities by using the updating technique of the solution. The convergence analysis of these new methods is considered and the proof of convergence is very simple. These new methods are versatile and are easy to implement. Our results differ from those of He [1,2], Solodov and Tseng [3], and Noor [4–6] for solving the monotone variational inequalities.     

New extragradient-like algorithms for strongly pseudomonotone variational inequalities

The paper considers two extragradient-like algorithms for solving variational inequality problems involving strongly pseudomonotone and Lipschitz continuous operators in Hilbert spaces. The projection method is used to design the algorithms which can be computed more easily than the regularized method. The construction of solution approximations and the proof of convergence of the algorithms are performed without the prior knowledge of the modulus of strong pseudomonotonicity and the Lipschitz constant of the cost operator. Instead of that, the algorithms use variable stepsize sequences which are diminishing and non-summable. The numerical behaviors of the proposed algorithms on a test problem are illustrated and compared with those of several previously known algorithms.     

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.