Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/60984
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Tian, B | en_US |
dc.creator | Li, D | en_US |
dc.creator | Yang, X | en_US |
dc.date.accessioned | 2016-12-19T08:54:16Z | - |
dc.date.available | 2016-12-19T08:54:16Z | - |
dc.identifier.issn | 1055-6788 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/60984 | - |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.rights | © 2016 Informa UK Limited, trading as Taylor & Francis Group | en_US |
dc.rights | This is an Accepted Manuscript of an article published by Taylor & Francis in Optimization Methods and Software on 24 Feb 2016 (published online), available at: http://www.tandfonline.com/10.1080/10556788.2016.1146266 | en_US |
dc.subject | Exponential convergence rate | en_US |
dc.subject | Implicit complementarity problems | en_US |
dc.subject | Lower order penalty method | en_US |
dc.subject | Trust-region Gauss–Newton method | en_US |
dc.title | An unconstrained differentiable penalty method for implicit complementarity problems | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 775 | en_US |
dc.identifier.epage | 790 | en_US |
dc.identifier.volume | 31 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.doi | 10.1080/10556788.2016.1146266 | en_US |
dcterms.abstract | In this paper, we introduce an unconstrained differentiable penalty method for solving implicit complementarity problems, which has an exponential convergence rate under the assumption of a uniform ξ-P-function. Instead of solving the unconstrained penalized equations directly, we consider a corresponding unconstrained optimization problem and apply the trust-region Gauss–Newton method to solve it. We prove that the local solution of the unconstrained optimization problem identifies that of the complementarity problems under monotone assumptions. We carry out numerical experiments on the test problems from MCPLIB, and show that the proposed method is efficient and robust. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Optimization methods and software, 2016, v. 31, no. 4, p. 775-790 | en_US |
dcterms.isPartOf | Optimization methods and software | en_US |
dcterms.issued | 2016 | - |
dc.identifier.isi | WOS:000377145400007 | - |
dc.identifier.scopus | 2-s2.0-84959047367 | - |
dc.identifier.ros | 2016000197 | - |
dc.identifier.rosgroupid | 2016000196 | - |
dc.description.ros | 2016-2017 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.validate | 201804_a bcma | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AMA-0567 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | PolyU | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6619447 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Yang_Unconstrained_Differentiable_Penalty.pdf | Pre-Published version | 966.92 kB | Adobe PDF | View/Open |
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