共查询到20条相似文献,搜索用时 23 毫秒
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A new conjugate gradient method is proposed in this paper. For any (inexact) line search, our scheme satifies the sufficient descent property. The method is proved to be globally convergent if the restricted Wolfe-Powell line search is used. Preliminary numerical result shows that it is efficient. 相似文献
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根据CG-DESCENT算法[1]的结构和Powell在综述文献[11]中的建议,给出了两种新的求解无约束优化问题的非线性共轭梯度算法. 它们在任意线搜索下都具有充分下降性质, 并在标准Wolfe线搜索下对一般函数能够保证全局收敛性. 通过对CUTEr函数库中部分著名的函数进行试验, 并借助著名的Dolan & Moré[2]评价方法, 展示了新算法的有效性. 相似文献
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《Optimization》2012,61(4):993-1009
Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, we propose a new two-parameter family of conjugate gradient methods for unconstrained optimization. The two-parameter family of methods not only includes the already existing three practical nonlinear conjugate gradient methods, but has other family of conjugate gradient methods as subfamily. The two-parameter family of methods with the Wolfe line search is shown to ensure the descent property of each search direction. Some general convergence results are also established for the two-parameter family of methods. The numerical results show that this method is efficient for the given test problems. In addition, the methods related to this family are uniformly discussed. 相似文献
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In this paper, a new gradient-related algorithm for solving large-scale unconstrained optimization problems is proposed. The new algorithm is a kind of line search method. The basic idea is to choose a combination of the current gradient and some previous search directions as a new search direction and to find a step-size by using various inexact line searches. Using more information at the current iterative step may improve the performance of the algorithm. This motivates us to find some new gradient algorithms which may be more effective than standard conjugate gradient methods. Uniformly gradient-related conception is useful and it can be used to analyze global convergence of the new algorithm. The global convergence and linear convergence rate of the new algorithm are investigated under diverse weak conditions. Numerical experiments show that the new algorithm seems to converge more stably and is superior to other similar methods in many situations. 相似文献
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Yu-Hong Dai. 《Mathematics of Computation》2003,72(243):1317-1328
Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. This paper proposes a three-parameter family of hybrid conjugate gradient methods. Two important features of the family are that (i) it can avoid the propensity of small steps, namely, if a small step is generated away from the solution point, the next search direction will be close to the negative gradient direction; and (ii) its descent property and global convergence are likely to be achieved provided that the line search satisfies the Wolfe conditions. Some numerical results with the family are also presented.
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共轭梯度法是最优化中最常用的方法之一,广泛地应用于求解大规模优化问题,其中参数β_k的不同选取可以构成不同的共轭梯度法.给出了一类含有三个参数的共轭梯度算法,这种算法能够在给定的条件下证明选定的β_k在每一步都能产生一个下降方向,同时在强Wolfe线搜索下,这种算法具有全局收敛性. 相似文献
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A three-parameter family of nonlinear conjugate gradient methods 总被引:3,自引:0,他引:3
In this paper, we propose a three-parameter family of conjugate gradient methods for unconstrained optimization. The three-parameter family of methods not only includes the already existing six practical nonlinear conjugate gradient methods, but subsumes some other families of nonlinear conjugate gradient methods as its subfamilies. With Powell's restart criterion, the three-parameter family of methods with the strong Wolfe line search is shown to ensure the descent property of each search direction. Some general convergence results are also established for the three-parameter family of methods. This paper can also be regarded as a brief review on nonlinear conjugate gradient methods.
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共轭梯度法是求解无约束优化问题的一种重要的方法.本文提出一族新的共轭梯度法,证明了其在推广的Wolfe非精确线搜索条件下具有全局收敛性.最后对算法进行了数值实验,实验结果验证了该算法的有效性. 相似文献
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本文我们讨论了一簇共轭梯度法,它可被看作是FR法和DY法的凸组合.我们提出了两种Armijo型线搜索,并在这两种线搜索下,讨论了共轭梯度法簇的全局收敛性. 相似文献
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This paper explores the convergence of nonlinear conjugate gradient methods with Goldstein line search without regular restarts.
Under this line search, global convergence for a subsequence is given for the famous conjugate gradient methods, Fletcher-Reeves
method. The same result can be obtained for Polak-Ribiére-Polyak method and others.
*This work was partially supported by National Hitech Program (863,2002AA104540) and National Natural Science Foundation of
China (No.60373060). 相似文献
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Conjugate Gradient Methods with Armijo-type Line Searches 总被引:14,自引:0,他引:14
Yu-Hong DAIState Key Laboratory of Scientific Engineering Computing Institute of Computational Mathematics Academy of Mathematics System Sciences Chinese Academy of Sciences Beijing China 《应用数学学报(英文版)》2002,18(1):123-130
Abstract Two Armijo-type line searches are proposed in this paper for nonlinear conjugate gradient methods.Under these line searches, global convergence results are established for several famous conjugate gradientmethods, including the Fletcher-Reeves method, the Polak-Ribiere-Polyak method, and the conjugate descentmethod. 相似文献
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本文提出了一类与HS方法相关的新的共轭梯度法.在强Wolfe线搜索的条件下,该方法能够保证搜索方向的充分下降性,并且在不需要假设目标函数为凸的情况下,证明了该方法的全局收敛性.同时,给出了这类新共轭梯度法的一种特殊形式,通过调整参数ρ,验证了它对给定测试函数的有效性. 相似文献
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共轭梯度法是求解大规模无约束优化问题最有效的方法之一.对HS共轭梯度法参数公式进行改进,得到了一个新公式,并以新公式建立一个算法框架.在不依赖于任何线搜索条件下,证明了由算法框架产生的迭代方向均满足充分下降条件,且在标准Wolfe线搜索条件下证明了算法的全局收敛性.最后,对新算法进行数值测试,结果表明所改进的方法是有效的. 相似文献
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P. Armand 《Journal of Optimization Theory and Applications》2007,132(2):287-305
This paper proposes a line search technique to satisfy a relaxed form of the strong Wolfe conditions in order to guarantee
the descent condition at each iteration of the Polak-Ribière-Polyak conjugate gradient algorithm. It is proved that this line
search algorithm preserves the usual convergence properties of any descent algorithm. In particular, it is shown that the
Zoutendijk condition holds under mild assumptions. It is also proved that the resulting conjugate gradient algorithm is convergent
under a strong convexity assumption. For the nonconvex case, a globally convergent modification is proposed. Numerical tests
are presented.
This paper is based on an earlier work presented at the International Symposium on Mathematical Programming in Lausanne in
1997. The author thanks J. C. Gilbert for his advice and M. Albaali for some recent discussions which motivated him to write
this paper. Special thanks to G. Liu, J. Nocedal, and R. Waltz for the availability of the software CG+ and to one of the
referees who indicated to him the paper of Grippo and Lucidi (Ref. 1). 相似文献