Improving a Lagrangian decomposition for the unconstrained binary quadratic programming problem

This paper presents a new alternative of Lagrangian decomposition based on column generation technique to solve the unconstrained binary quadratic programming problem. We use a mixed binary linear version of the original quadratic problem with constraints represented by a graph. This graph is partit...

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Bibliographic Details
Published in:Computers & operations research Vol. 39; no. 7; pp. 1577 - 1581
Main Authors: Mauri, Geraldo Regis, Lorena, Luiz Antonio Nogueira
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.07.2012
Elsevier
Pergamon Press Inc
Subjects:
Clusters
Column generation
Decomposition
Forming
Graph theory
Graphs
Lagrange multiplier
Lagrangian decomposition
Mathematical analysis
Mathematical models
Operations research
Quadratic programming
Studies
Quadratic programming
Lagrangian decomposition
Column generation
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary:This paper presents a new alternative of Lagrangian decomposition based on column generation technique to solve the unconstrained binary quadratic programming problem. We use a mixed binary linear version of the original quadratic problem with constraints represented by a graph. This graph is partitioned into clusters of vertices forming subproblems whose solutions use the dual variables obtained by a coordinator problem. Computational experiments consider a set of difficult instances and the results are compared against other methods reported recently in the literature.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2011.09.008