| A deterministic annealing algorithm for the minimum concave cost network flow problem. | |
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MedLine Citation:
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PMID: 21482456 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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The existing algorithms for the minimum concave cost network flow problems mainly focus on the single-source problems. To handle both the single-source and the multiple-source problem in the same way, especially the problems with dense arcs, a deterministic annealing algorithm is proposed in this paper. The algorithm is derived from an application of the Lagrange and Hopfield-type barrier function. It consists of two major steps: one is to find a feasible descent direction by updating Lagrange multipliers with a globally convergent iterative procedure, which forms the major contribution of this paper, and the other is to generate a point in the feasible descent direction, which always automatically satisfies lower and upper bound constraints on variables provided that the step size is a number between zero and one. The algorithm is applicable to both the single-source and the multiple-source capacitated problem and is especially effective and efficient for the problems with dense arcs. Numerical results on 48 test problems show that the algorithm is effective and efficient. |
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Authors:
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Chuangyin Dang; Yabin Sun; Yuping Wang; Yang Yang |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-4-9 |
Journal Detail:
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Title: Neural networks : the official journal of the International Neural Network Society Volume: - ISSN: 1879-2782 ISO Abbreviation: - Publication Date: 2011 Apr |
Date Detail:
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Created Date: 2011-4-12 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8805018 Medline TA: Neural Netw Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2011 Elsevier Ltd. All rights reserved. |
Affiliation:
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Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Hong Kong, China. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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