DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chang, Min Ho | ko |
dc.contributor.author | Park, Young Cheol | ko |
dc.contributor.author | Lee, Tai-Yong | ko |
dc.date.accessioned | 2013-03-07T10:57:47Z | - |
dc.date.available | 2013-03-07T10:57:47Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2007-09 | - |
dc.identifier.citation | JOURNAL OF GLOBAL OPTIMIZATION, v.39, no.1, pp.79 - 100 | - |
dc.identifier.issn | 0925-5001 | - |
dc.identifier.uri | http://hdl.handle.net/10203/90007 | - |
dc.description.abstract | In this paper, a new global optimization method is proposed for an optimization problem with twice-differentiable objective and constraint functions of a single variable. The method employs a difference of convex underestimator and a convex cut function, where the former is a continuous piecewise concave quadratic function, and the latter is a convex quadratic function. The main objectives of this research are to determine a quadratic concave underestimator that does not need an iterative local optimizer to determine the lower bounding value of the objective function and to determine a convex cut function that effectively detects infeasible regions for nonconvex constraints. The proposed method is proven to have a finite epsilon-convergence to locate the global optimum point. The numerical experiments indicate that the proposed method competes with another covering method, the index branch-and-bound algorithm, which uses the Lipschitz constant. | - |
dc.language | English | - |
dc.publisher | SPRINGER | - |
dc.subject | ALPHA-BB | - |
dc.subject | CONVEX UNDERESTIMATORS | - |
dc.subject | MULTIEXTREMAL CONSTRAINTS | - |
dc.subject | INTERVAL-ANALYSIS | - |
dc.subject | PROCESS DESIGN | - |
dc.subject | ALGORITHM | - |
dc.subject | SYSTEMS | - |
dc.subject | MINLPS | - |
dc.title | A new global optimization method for univariate constrained twice-differentiable NLP problems | - |
dc.type | Article | - |
dc.identifier.wosid | 000248328200004 | - |
dc.identifier.scopusid | 2-s2.0-34249028689 | - |
dc.type.rims | ART | - |
dc.citation.volume | 39 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 79 | - |
dc.citation.endingpage | 100 | - |
dc.citation.publicationname | JOURNAL OF GLOBAL OPTIMIZATION | - |
dc.identifier.doi | 10.1007/s10898-006-9121-1 | - |
dc.contributor.localauthor | Lee, Tai-Yong | - |
dc.contributor.nonIdAuthor | Chang, Min Ho | - |
dc.contributor.nonIdAuthor | Park, Young Cheol | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | global optimization | - |
dc.subject.keywordAuthor | difference of convex underestimator | - |
dc.subject.keywordAuthor | convex cut function | - |
dc.subject.keywordAuthor | univariate NLP | - |
dc.subject.keywordPlus | ALPHA-BB | - |
dc.subject.keywordPlus | CONVEX UNDERESTIMATORS | - |
dc.subject.keywordPlus | MULTIEXTREMAL CONSTRAINTS | - |
dc.subject.keywordPlus | INTERVAL-ANALYSIS | - |
dc.subject.keywordPlus | PROCESS DESIGN | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | MINLPS | - |
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