Convex Minlp – An Efficient Tool for Design and Optimization Tasks?

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Abstract

Convex mixed-integer nonlinear programming (MINLP) has reached a certain maturity, and this paper is intended to show that there are efficient solvers available for convex MINLP problems. The presence of efficient solvers, in combination with the extended modeling capabilities compared to mixed-integer linear programming, make convex MINLP an attractive framework for dealing with industry-relevant optimization tasks. In the paper, we describe some frequently used modeling techniques within MINLP, and a numerical comparison shows how these techniques affect some commonly available solvers. Some solver features are also described along with a discussion of future possibilities and challenges for convex MINLP solvers.
Original languageUndefined/Unknown
Title of host publicationProceedings of the 9th International Conference on Foundations of Computer-Aided Process Design
EditorsSalvador Garcia Muñoz, Carl D. Laird, Matthew J. Realff
PublisherElsevier
Pages245–250
Number of pages6
ISBN (Print)978-0-12-818597-1
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Foundations of Computer-Aided Process Design - 9th International Conference on Foundations of Computer-Aided Process Design
Duration: 14 Jul 201918 Jul 2019

Conference

ConferenceInternational Conference on Foundations of Computer-Aided Process Design
Period14/07/1918/07/19

Keywords

  • Convex MINLP
  • FOCAPD 2019
  • MINLP modeling
  • MINLP solvers

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