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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Abstract	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.
AbstractList	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. [PUBLICATION ABSTRACT] 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.
Author	Still, Claus Westerlund, Tapio
Author_xml	– sequence: 1 givenname: Claus surname: Still fullname: Still, Claus email: claus.still@abo.fi organization: Department of Mathematics, Åbo Akademi University, Fänriksgatan 3B, FIN-20500 Åbo, Finland – sequence: 2 givenname: Tapio surname: Westerlund fullname: Westerlund, Tapio email: tapio.westerlund@abo.fi organization: Process Design Laboratory, Åbo Akademi University, Biskopsgatan 8, FIN-20500 Åbo, Finland
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Keywords	Cutting plane algorithms Nonlinear programming Convex programming Sequential linear programming Sequential method Linear programming Numerical method Non linear programming Cutting plane method Competitive algorithms
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SubjectTerms	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
Title	A sequential cutting plane algorithm for solving convex NLP problems
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