中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A smoothing Newton method for extended vertical linear complementarity problems

文献类型:期刊论文

作者Qi, HD; Liao, LZ
刊名SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS
出版日期1999-10-27
卷号21期号:1页码:45-66
关键词aggregation function smoothing Newton method semismoothness global convergence
ISSN号0895-4798
英文摘要In this paper, we reformulate the extended vertical linear complementarity problem (EVLCP(m, q)) as a nonsmooth equation H(t, x) = 0, where H : Rn+1 --> Rn+1, t is an element of R is a parameter variable, and x is an element of R is the original variable. H is continuously differentiable except at such points (t, x) with t = 0. Furthermore H is strongly semismooth. The reformulation of EVLCP(m; q) as a nonsmooth equation is based on the so-called aggregation (smoothing) function. As a result, a Newton-type method is proposed which generates a sequence {w(k) = (t(k), x(k))} with all t(k) > 0. We prove that every accumulation point of this sequence is a solution of EVLCP(M,q) under the assumption of row W-0-property. If row W-property holds at the solution point, then the convergence rate is quadratic. Promising numerical results are also presented.
WOS研究方向Mathematics
语种英语
WOS记录号WOS:000083320600003
出版者SIAM PUBLICATIONS
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/14126]  
专题中国科学院数学与系统科学研究院
通讯作者Qi, HD
作者单位1.Chinese Acad Sci, Inst Computat Math & Sci Engn Comp, Beijing, Peoples R China
2.Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong
推荐引用方式
GB/T 7714
Qi, HD,Liao, LZ. A smoothing Newton method for extended vertical linear complementarity problems[J]. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS,1999,21(1):45-66.
APA Qi, HD,&Liao, LZ.(1999).A smoothing Newton method for extended vertical linear complementarity problems.SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS,21(1),45-66.
MLA Qi, HD,et al."A smoothing Newton method for extended vertical linear complementarity problems".SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS 21.1(1999):45-66.

入库方式: OAI收割

来源:数学与系统科学研究院

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