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A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs

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A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs. / Galli, Laura; Letchford, Adam.
In: Optimization Letters, Vol. 8, No. 4, 04.2014, p. 1213-1224.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Galli, L & Letchford, A 2014, 'A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs', Optimization Letters, vol. 8, no. 4, pp. 1213-1224. https://doi.org/10.1007/s11590-013-0676-8

APA

Galli, L., & Letchford, A. (2014). A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs. Optimization Letters, 8(4), 1213-1224. https://doi.org/10.1007/s11590-013-0676-8

Vancouver

Galli L, Letchford A. A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs. Optimization Letters. 2014 Apr;8(4):1213-1224. Epub 2013 Jul 1. doi: 10.1007/s11590-013-0676-8

Author

Galli, Laura ; Letchford, Adam. / A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs. In: Optimization Letters. 2014 ; Vol. 8, No. 4. pp. 1213-1224.

Bibtex

@article{8ce83129c3d14c0492b702bc868297ba,
title = "A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs",
abstract = "Quadratic Convex Reformulation (QCR) is a technique that was originally proposed for quadratic 0-1 programs, and then extended to various other problems. It is used to convert non-convex instances into convex ones, in such a way that the bound obtained by solving the continuous relaxation of the reformulated instance is as strong as possible.In this paper, we focus on the case of quadratically constrained quadratic 0-1 programs. The variant of QCR previously proposed for this case involves the addition of a quadratic number of auxiliary continuous variables. We show that, in fact, at most one additional variable is needed. Some computational results are also presented.",
keywords = "Combinatorial optimisation, semidefinite programming, quadratically constrained quadratic programming",
author = "Laura Galli and Adam Letchford",
note = "The original publication is available at www.link.springer.com",
year = "2014",
month = apr,
doi = "10.1007/s11590-013-0676-8",
language = "English",
volume = "8",
pages = "1213--1224",
journal = "Optimization Letters",
issn = "1862-4472",
publisher = "Springer Verlag",
number = "4",

}

RIS

TY - JOUR

T1 - A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs

AU - Galli, Laura

AU - Letchford, Adam

N1 - The original publication is available at www.link.springer.com

PY - 2014/4

Y1 - 2014/4

N2 - Quadratic Convex Reformulation (QCR) is a technique that was originally proposed for quadratic 0-1 programs, and then extended to various other problems. It is used to convert non-convex instances into convex ones, in such a way that the bound obtained by solving the continuous relaxation of the reformulated instance is as strong as possible.In this paper, we focus on the case of quadratically constrained quadratic 0-1 programs. The variant of QCR previously proposed for this case involves the addition of a quadratic number of auxiliary continuous variables. We show that, in fact, at most one additional variable is needed. Some computational results are also presented.

AB - Quadratic Convex Reformulation (QCR) is a technique that was originally proposed for quadratic 0-1 programs, and then extended to various other problems. It is used to convert non-convex instances into convex ones, in such a way that the bound obtained by solving the continuous relaxation of the reformulated instance is as strong as possible.In this paper, we focus on the case of quadratically constrained quadratic 0-1 programs. The variant of QCR previously proposed for this case involves the addition of a quadratic number of auxiliary continuous variables. We show that, in fact, at most one additional variable is needed. Some computational results are also presented.

KW - Combinatorial optimisation

KW - semidefinite programming

KW - quadratically constrained quadratic programming

U2 - 10.1007/s11590-013-0676-8

DO - 10.1007/s11590-013-0676-8

M3 - Journal article

VL - 8

SP - 1213

EP - 1224

JO - Optimization Letters

JF - Optimization Letters

SN - 1862-4472

IS - 4

ER -