Non-convex mixed-integer nonlinear programming: A survey
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, how...
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| Vydáno v: | Surveys in operations research and management science Ročník 17; číslo 2; s. 97 - 106 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.07.2012
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| ISSN: | 1876-7354, 1876-7362 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| ISSN: | 1876-7354 1876-7362 |
| DOI: | 10.1016/j.sorms.2012.08.001 |