A sequential cutting plane algorithm for solving convex NLP problems

In this paper we look at a new algorithm for solving convex nonlinear programming optimization problems. The algorithm is a cutting plane-based method, where the sizes of the subproblems remain fixed, thus avoiding the issue with constantly growing subproblems we have for the classical Kelley’s cutt...

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Vydané v:European journal of operational research Ročník 173; číslo 2; s. 444 - 464
Hlavní autori: Still, Claus, Westerlund, Tapio
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 01.09.2006
Elsevier
Elsevier Sequoia S.A
Edícia:European Journal of Operational Research
Predmet:
Algorithms
Applied sciences
Convex programming
Cutting plane algorithms
Exact sciences and technology
Nonlinear programming
Operational research and scientific management
Operational research. Management science
Operations research
Optimization techniques
Sequential linear programming
Studies
Cutting plane algorithms
Nonlinear programming
Convex programming
Sequential linear programming
Sequential method
Linear programming
Numerical method
Non linear programming
Cutting plane method
Competitive algorithms
ISSN:0377-2217, 1872-6860
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Shrnutí:In this paper we look at a new algorithm for solving convex nonlinear programming optimization problems. The algorithm is a cutting plane-based method, where the sizes of the subproblems remain fixed, thus avoiding the issue with constantly growing subproblems we have for the classical Kelley’s cutting plane algorithm. Initial numerical experiments indicate that the algorithm is considerably faster than Kelley’s cutting plane algorithm and also competitive with existing nonlinear programming algorithms.
Bibliografia:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2005.02.045