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 language | Undefined/Unknown |
|---|---|
| Title of host publication | Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design |
| Editors | Salvador Garcia Muñoz, Carl D. Laird, Matthew J. Realff |
| Publisher | Elsevier |
| Pages | 245–250 |
| Number of pages | 6 |
| ISBN (Print) | 978-0-12-818597-1 |
| DOIs | |
| Publication status | Published - 2019 |
| MoE publication type | A4 Article in a conference publication |
| Event | International Conference on Foundations of Computer-Aided Process Design - 9th International Conference on Foundations of Computer-Aided Process Design Duration: 14 Jul 2019 → 18 Jul 2019 |
Conference
| Conference | International Conference on Foundations of Computer-Aided Process Design |
|---|---|
| Period | 14/07/19 → 18/07/19 |
Keywords
- Convex MINLP
- FOCAPD 2019
- MINLP modeling
- MINLP solvers