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Optimization and Engineering
Erschienen in: Optimization and Engineering 2/2019

24.04.2019 | Editorial

Mixed-integer nonlinear programming 2018

verfasst von: Nikolaos V. Sahinidis

Erschienen in: Optimization and Engineering | Ausgabe 2/2019

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Abstract

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.
Nächster Artikel New SOCP relaxation and branching rule for bipartite bilinear programs

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Metadaten
Titel
Mixed-integer nonlinear programming 2018
verfasst von
Nikolaos V. Sahinidis
Publikationsdatum
24.04.2019
Verlag
Springer US
Erschienen in
Optimization and Engineering / Ausgabe 2/2019
Print ISSN: 1389-4420
Elektronische ISSN: 1573-2924
DOI
https://doi.org/10.1007/s11081-019-09438-1

Weitere Artikel der Ausgabe 2/2019

Optimization and Engineering 2/2019 Zur Ausgabe

Research Article

New SOCP relaxation and branching rule for bipartite bilinear programs

Research Article

An adaptive discretization algorithm for the design of water usage and treatment networks

Research Article

Resilient layout, design and operation of energy-efficient water distribution networks for high-rise buildings using MINLP

Research Article

A review and comparison of solvers for convex MINLP

Research Article

Operational decisions for multi-period industrial gas pipeline networks under uncertainty

Research Article

Dynamic optimization of a district energy system with storage using a novel mixed-integer quadratic programming algorithm

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