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Decomposition and Linearization for 0-1 Quadratic Programming

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Abstract

This paper presents a general decomposition method to compute bounds for constrained 0-1 quadratic programming. The best decomposition is found by using a Lagrangian decomposition of the problem. Moreover, in its simplest version this method is proved to give at least the bound obtained by the LP-relaxation of a non-trivial linearization. To illustrate this point, some computational results are given for the 0-1 quadratic knapsack problem.

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Elloumi, S., Faye, A. & Soutif, E. Decomposition and Linearization for 0-1 Quadratic Programming. Annals of Operations Research 99, 79–93 (2000). https://doi.org/10.1023/A:1019236832495

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