Burer, S and Letchford, Adam (2012) Non-convex mixed-integer nonlinear programming: a survey. Surveys in Operations Research and Management Science, 17 (2). pp. 97-106. ISSN 1876-7354Full text not available from this repository.
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 nonconvexities are present, however, things become much more difficult, since then even the continuous relaxation is a global optimisation problem. We survey the literature on non-convex MINLP, discussing applications, algorithms and software. Special attention is paid to the case in which the objective and constraint functions are quadratic.
|Journal or Publication Title:||Surveys in Operations Research and Management Science|
|Uncontrolled Keywords:||mixed-integer nonlinear programming ; global optimisation|
|Subjects:||?? hb ??|
|Departments:||Lancaster University Management School > Management Science|
|Deposited On:||13 Jul 2012 13:27|
|Last Modified:||01 May 2017 00:03|
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