The Unconstrained Binary Quadratic Programming Problem - PQ is a classical non-linear problem of optimizing a quadratic objective by suitable choice of binary decisions variables. This paper proposes new alternatives of Lagrangean decomposition to find bounds for PQ. The presented methods treat a mixed binary linear version (PQL) of PQ with constraints represented by a graph. This graph is partitioned in clusters of vertices forming a dual problem that is solved by a subgradient algorithm. The subproblems formed by the generated subgraphs are solved by the CPLEX. Computational experiments consider with a data set formed by several difficult instances with different characteristics. The results show the efficiency of the proposed methods over traditional Lagrangean relaxations and other methods found in literature.
quadratic programming; Lagrangean decomposition; bounds
