The interior proximal extragradient method for solving equilibrium problems

In this article we present a new and efficient method for solving equilibrium problems on polyhedra. The method is based on an interior-quadratic proximal term which replaces the usual quadratic proximal term. This leads to an interior proximal type algorithm. Each iteration consists in a prediction step followed by a correction step as in the extragradient method. In a first algorithm each of these steps is obtained by solving an unconstrained minimization problem, while in a second algorithm the correction step is replaced by an Armijo-backtracking linesearch followed by an hyperplane projection step. We prove that our algorithms are convergent under mild assumptions: pseudomonotonicity for the two algorithms and a Lipschitz property for the first one. Finally we present some numerical experiments to illustrate the behavior of the proposed algorithms.     

Hybrid projection methods for equilibrium problems with non‐Lipschitz type bifunctions

Based on the extended extragradient‐like method and the linesearch technique, we propose three projection methods for finding a common solution of a finite family of equilibrium problems. The linesearch used in the proposed algorithms has allowed to reduce some conditions imposed on equilibrium bifunctions. The strongly convergent theorems are established without the Lipschitz‐type condition of bifunctions. The paper also helps in the design and analysis of practical algorithms and gives us a generalization of some previously known problems. Copyright © 2017 John Wiley & Sons, Ltd.     

解变分不等式的一种二次投影迭代算法

本文研究了一类新的求解伪单调变分不等式的二次投影迭代算法.利用Armijo型线性搜寻程序,建立了一类新的超平面,他们严格分离当前迭代点与变分不等式的解集.运用超平面的这种分离性质,在较弱的条件下证明了该算法生成的无穷序列是全局收敛的.数值实验证明该算法是有效的.     

Numerical Experiences with New Truncated Newton Methods in Large Scale Unconstrained Optimization

Recently, in [12] a very general class oftruncated Newton methods has been proposed for solving large scale unconstrained optimization problems. In this work we present the results of an extensive numericalexperience obtained by different algorithms which belong to the preceding class. This numerical study, besides investigating which arethe best algorithmic choices of the proposed approach, clarifies some significant points which underlies every truncated Newton based algorithm.     

非单调带参数Perry-Shanno无记忆拟牛顿法的收敛性

给出了一种非单调带参数的Perry-Shanno无记忆拟牛顿法, 对于目标函数为凸函数, 在参数满足适当范围的情况下, 证明了算法的全局收敛性.     

Global Convergence Analysis of a New Nonmonotone BFGS Algorithm on Convex Objective Functions

In this paper, a new nonmonotone BFGS algorithmfor unconstrained optimization is introduced. Under mild conditions,the global convergence of this new algorithm on convex functions isproved. Some numerical experiments show that this new nonmonotoneBFGS algorithm is competitive to the BFGS algorithm.     

无约束最优化线搜索一般模型及BFGS方法的整体收敛性

本文给出了无约束最优化的算法中线性搜索的可接受的步长选择律的一种一般形式，它概括了大多数已有的步长律为其特例，并且研究了它基本性质，最后证明了此线性搜索一般模拟相结合的无约束优化的ＢＦＧＳ算法的整体收敛性。     

Extragradient Methods for Vector Equilibrium Problems in Banach Spaces

We consider the vector equilibrium problem, which extends the scalar equilibrium problem to vector valued bifunctions, in a Banach space setting. We propose an extragradient method for solving this problem. Under suitable assumptions on the bifunction, we prove that the generated sequence is weakly convergent to a solution of the problem. Then, we propose a regularization procedure which ensures strong convergence of the generated sequence to a solution of the problem.     

A new technique for avoiding the Maratos effect

A well-known difficulty arising in the convergence globalization of Newton-type constrained optimization methods is the Maratos effect, which prevents these methods from achieving a superlinear convergence rate and, in many cases, reduces their general efficiency. For the sequential quadratic programming method with linesearch, a new simple and rather promising technique is proposed to avoid the Maratos effect.     

采用广义Curry线搜索的重新开始共轭梯度算法及其全局收敛性

本文对无约束最优化问题：ｍｉｎｆ（ｘ），ｘ∈Ｒ^ｎ，提出一种新的重新开始共轭梯度算法．该算法采用一类广义Ｃｕｒｒｙ线搜索原则，参数β_ｋ可在一个有限闭区间内选择，且允许β_ｋ取负值．在较弱的条件下证明了该算法的全局收敛性．