Journal article
Non-convex mixed-integer nonlinear programming: A survey
Surveys in operations research and management science, Vol.17(2), pp.97-106
07/2012
DOI: 10.1016/j.sorms.2012.08.001
Abstract
A wide range of problems arising in practical applications can be formulated as Mixed-Integer Nonlinear Programs (MINLPs). For the case in which the objective and constraint functions are convex, some quite effective exact and heuristic algorithms are available. When non-convexities are present, however, things become much more difficult, since then even the continuous relaxation is a global optimization problem. We survey the literature on non-convex MINLPs, discussing applications, algorithms, and software. Special attention is paid to the case in which the objective and constraint functions are quadratic.
Details
- Title: Subtitle
- Non-convex mixed-integer nonlinear programming: A survey
- Creators
- Samuel Burer - University of IowaAdam N. Letchford - Lancaster University
- Resource Type
- Journal article
- Publication Details
- Surveys in operations research and management science, Vol.17(2), pp.97-106
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.sorms.2012.08.001
- ISSN
- 1876-7354
- eISSN
- 1876-7362
- Language
- English
- Date published
- 07/2012
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380554502771
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