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Non-convex mixed-integer nonlinear programming: a survey

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Non-convex mixed-integer nonlinear programming : a survey. / Burer, S; Letchford, Adam.

In: Surveys in Operations Research and Management Science, Vol. 17, No. 2, 2012, p. 97-106.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Burer, S & Letchford, A 2012, 'Non-convex mixed-integer nonlinear programming: a survey', Surveys in Operations Research and Management Science, vol. 17, no. 2, pp. 97-106. https://doi.org/10.1016/j.sorms.2012.08.001

APA

Burer, S., & Letchford, A. (2012). Non-convex mixed-integer nonlinear programming: a survey. Surveys in Operations Research and Management Science, 17(2), 97-106. https://doi.org/10.1016/j.sorms.2012.08.001

Vancouver

Burer S, Letchford A. Non-convex mixed-integer nonlinear programming: a survey. Surveys in Operations Research and Management Science. 2012;17(2):97-106. doi: 10.1016/j.sorms.2012.08.001

Author

Burer, S ; Letchford, Adam. / Non-convex mixed-integer nonlinear programming : a survey. In: Surveys in Operations Research and Management Science. 2012 ; Vol. 17, No. 2. pp. 97-106.

Bibtex

@article{af2a19ff2b7849319c54335e62bda5fc,
title = "Non-convex mixed-integer nonlinear programming: a survey",
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 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.",
keywords = "mixed-integer nonlinear programming, global optimisation",
author = "S Burer and Adam Letchford",
year = "2012",
doi = "10.1016/j.sorms.2012.08.001",
language = "English",
volume = "17",
pages = "97--106",
journal = "Surveys in Operations Research and Management Science",
issn = "1876-7354",
publisher = "Elsevier Science B.V.",
number = "2",

}

RIS

TY - JOUR

T1 - Non-convex mixed-integer nonlinear programming

T2 - a survey

AU - Burer, S

AU - Letchford, Adam

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - mixed-integer nonlinear programming

KW - global optimisation

U2 - 10.1016/j.sorms.2012.08.001

DO - 10.1016/j.sorms.2012.08.001

M3 - Journal article

VL - 17

SP - 97

EP - 106

JO - Surveys in Operations Research and Management Science

JF - Surveys in Operations Research and Management Science

SN - 1876-7354

IS - 2

ER -