| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Fed Univ Espirito Santo UFES, Ctr Agrarian Sci, BR-29500000 Alegre, ES - Brazil
[2] Natl Inst Space Res INPE, Lab Comp & Appl Math, BR-12227010 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | International Transactions in Operational Research; v. 18, n. 2, p. 257-270, MAR 2011. |
| Web of Science Citations: | 4 |
| Abstract | |
The unconstrained binary quadratic programming problem (QP) is a classical non-linear problem of optimizing a quadratic objective by a suitable choice of binary decision variables. This paper proposes new Lagrangean decompositions to find bounds for QP. The methods presented treat a mixed binary linear version (LQP) of QP with constraints represented by a graph. This graph is partitioned into clusters of vertices forming a dual problem that is solved by a subgradient algorithm. The subproblems formed by the generated subgraphs are solved by CPLEX. Computational experiments consider a data set formed by several difficult instances with different features. The results show the efficiency of the proposed methods over traditional Lagrangean relaxations and other methods found in the literature. (AU) | |