Convergence of nonmonotone line search method

In this paper, we develop a new nonmonotone line search for general line search method and establish some global convergence theorems. The new nonmonotone line search is a novel form of the nonmonotone Armijo line search and allows one to choose a larger step size at each iteration, which is available in constructing new line search methods and possibly reduces the function evaluations at each iteration. Moreover, we analyze the convergence rate of some special line search methods with the new line search. Preliminary numerical results show that some line search methods with the new nonmonotone line search are available and efficient in practical computation.     

变步长非单调模式搜索法

A varied steplength nonmonotone pattern search method is proposed in this paper. The varied steplength search strategy is designed in this method such that the pattern direction is more approximated to efficient descent direction. The interpolation and nonmonotone technique are used for improving local search and global convergence. The theoretical and numerical results show that this method is an efficient direct search method.     

On the Nonmonotone Line Search

The technique of nonmonotone line search has received many successful applications and extensions in nonlinear optimization. This paper provides some basic analyses of the nonmonotone line search. Specifically, we analyze the nonmonotone line search methods for general nonconvex functions along different lines. The analyses are helpful in establishing the global convergence of a nonmonotone line search method under weaker conditions on the search direction. We explore also the relations between nonmonotone line search and R-linear convergence assuming that the objective function is uniformly convex. In addition, by taking the inexact Newton method as an example, we observe a numerical drawback of the original nonmonotone line search and suggest a standard Armijo line search when the nonmonotone line search condition is not satisfied by the prior trial steplength. The numerical results show the usefulness of such suggestion for the inexact Newton method.     

一类非单调修正PRP算法的全局收敛性

本文给出一类非单调线性搜索下的修正PRP算法,该方法保证每次迭代中的搜索方向是充分下降的.在较弱的条件下,我们证明了此类非单调修正PRP算法具有全局收敛性.     

非单调滤子曲率线搜索算法解无约束非凸优化

宇和濮在文[Yu Z S,Pu D G.A new nonmonotone line search technique for unconstrained optimization[J].J Comput Appl Math,2008,219:134-144]中提出了一种非单调的线搜索算法解无约束优化问题.和他们的工作不同,当优化问题非凸时,本文给出了一种非单调滤子曲率线搜索算法.通过使用海森矩阵的负曲率信息,算法产生的迭代序列被证明收敛于一个满足二阶充分性条件的点.在不需要假设极限点存在的情况下,证明了算法具有整体收敛性,而且分析了该算法的收敛速率.数值试验表明算法的有效性.     

求解界约束优化的一种新的非单调谱投影梯度法

本文给出求解界约束优化问题的一种新的非单调谱投影梯度算法. 该算法是将谱投影梯度算法与Zhang and Hager [SIAM Journal on Optimization,2004,4（4）：1043-1056]提出的非单调线搜索结合得到的方法. 在合理的假设条件下,证明了算法的全局收敛性.数值实验结果表明,与已有的界约束优化问题的谱投影梯度法比较,利用本文给出的算法求解界约束优化问题是有竞争力的.     

Nonmonotone Algorithms for Pattern Search Methods

This work deals with the solution of ill-conditioned unconstrained minimization problems by means of pattern search methods. To this end, the usual requirement of monotonic reduction of the objective function is relaxed and nonmonotone pattern search methods are proposed, which maintain the convergence properties of their monotone counterparts. Numerical experimentation on several well-known ill-conditioned functions is reported. The results highlight a class of pattern search methods which benefit very much by the introduction of nonmonotone strategies.     

GLOBAL CONVERGENCE RESULTS OF A THREE TERM MEMORY GRADIENT METHOD WITH A NON-MONOTONE LINE SEARCH TECHNIQUE

In this paper, a new class of three term memory gradient method with nonmonotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Combining the quasi-Newton method with the new method, the former is modified to have global convergence property. Numerical results show that the new algorithm is efficient.     

Global Convergence of Algorithms with Nonmonotone Line Search Strategy in Unconstrained Optimization

In this paper we state some nonmonotone line search strategies for unconstrained optimization algorithms. Abstracting from the concrete line search strategy we prove two general convergence results. Using this theory we can show the global convergence of the BFGS method with nonmonotone line search strategy. In contrast to some former results about nonmonotone line search strategies, both our convergence results and their proofs are natural generalizations of known results for the monotone case.     

Combining nonmonotone conic trust region and line search techniques for unconstrained optimization

In this paper, we propose a trust region method for unconstrained optimization that can be regarded as a combination of conic model, nonmonotone and line search techniques. Unlike in traditional trust region methods, the subproblem of our algorithm is the conic minimization subproblem; moreover, our algorithm performs a nonmonotone line search to find the next iteration point when a trial step is not accepted, instead of resolving the subproblem. The global and superlinear convergence results for the algorithm are established under reasonable assumptions. Numerical results show that the new method is efficient for unconstrained optimization problems.