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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

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.
AlkuperäiskieliEi tiedossa
OtsikkoProceedings of the 9th International Conference on Foundations of Computer-Aided Process Design
ToimittajatSalvador Garcia Muñoz, Carl D. Laird, Matthew J. Realff
KustantajaElsevier
Sivut245–250
Sivumäärä6
ISBN (painettu)978-0-12-818597-1
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Foundations of Computer-Aided Process Design - 9th International Conference on Foundations of Computer-Aided Process Design
Kesto: 14 heinäkuuta 201918 heinäkuuta 2019

Konferenssi

KonferenssiInternational Conference on Foundations of Computer-Aided Process Design
Ajanjakso14/07/1918/07/19

Keywords

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

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