Mixed-integer nonlinear programming 2018

Mixed-Integer Nonlinear Programming (MINLP) is the area of optimization that addresses nonlinear problems with continuous and integer variables. MINLP has proven to be a powerful tool for modeling. At the same time, it combines algorithmic design challenges from combinatorial and nonlinear optimizat...

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Veröffentlicht in:Optimization and engineering Jg. 20; H. 2; S. 301 - 306
1. Verfasser: Sahinidis, Nikolaos V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2019
Springer Nature B.V
Schlagworte:
Algorithms
Combinatorial analysis
Continuity (mathematics)
Control
Design optimization
Editorial
Engineering
Environmental Management
Financial Engineering
Mathematics
Mathematics and Statistics
Nonlinear programming
Operations Research/Decision Theory
Optimization
Systems Theory
Combinatorial optimization
MINLP
Nonlinear optimization
ISSN:1389-4420, 1573-2924
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Beschreibung
Zusammenfassung:Mixed-Integer Nonlinear Programming (MINLP) is the area of optimization that addresses nonlinear problems with continuous and integer variables. MINLP has proven to be a powerful tool for modeling. At the same time, it combines algorithmic design challenges from combinatorial and nonlinear optimization. The MINLP field has received increased attention over the past two decades with contributions on the theoretical, algorithmic, and computational side originating from a growing community that involves engineers, mathematicians, and operations researchers. This special issue was motivated by a seminar on MINLP that took place in Dagstuhl, Germany in 2018. The purpose of this article is to provide a brief introduction to the field and the articles of the special issue.
Bibliographie:ObjectType-Article-1
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ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-019-09438-1