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Exploiting planarity in separation routines for the symmetric travelling salesman problem

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Exploiting planarity in separation routines for the symmetric travelling salesman problem. / Letchford, A N; Pearson, N.
In: Discrete Optimization, Vol. 5, No. 2, 2008, p. 220-230.

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

Harvard

Letchford, AN & Pearson, N 2008, 'Exploiting planarity in separation routines for the symmetric travelling salesman problem', Discrete Optimization, vol. 5, no. 2, pp. 220-230. https://doi.org/10.1016/j.disopt.2007.05.002

APA

Letchford, A. N., & Pearson, N. (2008). Exploiting planarity in separation routines for the symmetric travelling salesman problem. Discrete Optimization, 5(2), 220-230. https://doi.org/10.1016/j.disopt.2007.05.002

Vancouver

Letchford AN, Pearson N. Exploiting planarity in separation routines for the symmetric travelling salesman problem. Discrete Optimization. 2008;5(2):220-230. doi: 10.1016/j.disopt.2007.05.002

Author

Letchford, A N ; Pearson, N. / Exploiting planarity in separation routines for the symmetric travelling salesman problem. In: Discrete Optimization. 2008 ; Vol. 5, No. 2. pp. 220-230.

Bibtex

@article{5b99df2c3ce547bfbe36fea9e95c0a17,
title = "Exploiting planarity in separation routines for the symmetric travelling salesman problem",
abstract = "At present, the most successful approach to solving large-scale instances of the Symmetric Traveling Salesman Problem to optimality is branch-and-cut. The success of branch-and-cut is due in large part to the availability of effective separation procedures; that is, routines for identifying violated linear constraints. For two particular classes of constraints, known as comb and domino-parity constraints, it has been shown that separation becomes easier when the underlying graph is planar. We continue this line of research by showing how to exploit planarity in the separation of three other classes of constraints: subtour elimination, 2-matching and simple domino-parity constraints.",
keywords = "traveling salesman problem, planar graphs, cutting planes",
author = "Letchford, {A N} and N Pearson",
year = "2008",
doi = "10.1016/j.disopt.2007.05.002",
language = "English",
volume = "5",
pages = "220--230",
journal = "Discrete Optimization",
issn = "1572-5286",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Exploiting planarity in separation routines for the symmetric travelling salesman problem

AU - Letchford, A N

AU - Pearson, N

PY - 2008

Y1 - 2008

N2 - At present, the most successful approach to solving large-scale instances of the Symmetric Traveling Salesman Problem to optimality is branch-and-cut. The success of branch-and-cut is due in large part to the availability of effective separation procedures; that is, routines for identifying violated linear constraints. For two particular classes of constraints, known as comb and domino-parity constraints, it has been shown that separation becomes easier when the underlying graph is planar. We continue this line of research by showing how to exploit planarity in the separation of three other classes of constraints: subtour elimination, 2-matching and simple domino-parity constraints.

AB - At present, the most successful approach to solving large-scale instances of the Symmetric Traveling Salesman Problem to optimality is branch-and-cut. The success of branch-and-cut is due in large part to the availability of effective separation procedures; that is, routines for identifying violated linear constraints. For two particular classes of constraints, known as comb and domino-parity constraints, it has been shown that separation becomes easier when the underlying graph is planar. We continue this line of research by showing how to exploit planarity in the separation of three other classes of constraints: subtour elimination, 2-matching and simple domino-parity constraints.

KW - traveling salesman problem

KW - planar graphs

KW - cutting planes

U2 - 10.1016/j.disopt.2007.05.002

DO - 10.1016/j.disopt.2007.05.002

M3 - Journal article

VL - 5

SP - 220

EP - 230

JO - Discrete Optimization

JF - Discrete Optimization

SN - 1572-5286

IS - 2

ER -