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Enlarging neighborhoods of interior-point algorithms for linear programming via least values of proximity measure functions
Zhao, Y. B.
2007-09-01
Source PublicationAPPLIED NUMERICAL MATHEMATICS
ISSN0168-9274
Volume57Issue:9Pages:1033-1049
AbstractIt is well known that a wide-neighborhood interior-point algorithm for linear programming performs much better in implementation than its small-neighborhood counterparts. In this paper, we provide a unified way to enlarge the neighborhoods of predictor-corrector interior-point algorithms for linear programming. We prove that our methods not only enlarge the neighborhoods but also retain the so-far best known iteration complexity and superlinear (or quadratic) convergence of the original interior-point algorithms. The idea of our methods is to use the global minimizers of proximity measure functions. (C) 2006 IMACS. Published by Elsevier B.V. All rights reserved.
Keywordlinear programming interior-point algorithms iteration complexity neighborhoods
DOI10.1016/j.apnum.2006.09.009
Language英语
WOS Research AreaMathematics
WOS SubjectMathematics, Applied
WOS IDWOS:000248182700005
PublisherELSEVIER SCIENCE BV
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/4373
Collection中国科学院数学与系统科学研究院
Corresponding AuthorZhao, Y. B.
AffiliationChinese Acad Sci, AMSS, Inst Appl Math, Beijing 100080, Peoples R China
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Zhao, Y. B.. Enlarging neighborhoods of interior-point algorithms for linear programming via least values of proximity measure functions[J]. APPLIED NUMERICAL MATHEMATICS,2007,57(9):1033-1049.
APA Zhao, Y. B..(2007).Enlarging neighborhoods of interior-point algorithms for linear programming via least values of proximity measure functions.APPLIED NUMERICAL MATHEMATICS,57(9),1033-1049.
MLA Zhao, Y. B.."Enlarging neighborhoods of interior-point algorithms for linear programming via least values of proximity measure functions".APPLIED NUMERICAL MATHEMATICS 57.9(2007):1033-1049.
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