Lattice preconditioning for the real relaxation branch-and-bound approach for integer least squares problems

Miguel Anjos (), Xiao-Wen Chang () and Wen-Yang Ku ()

Journal of Global Optimization, 2014, vol. 59, issue 2, 227-242

Abstract: The integer least squares problem is an important problem that arises in numerous applications. We propose a real relaxation-based branch-and-bound (RRBB) method for this problem. First, we define a quantity called the distance to integrality, propose it as a measure of the number of nodes in the RRBB enumeration tree, and provide computational evidence that the size of the RRBB tree is proportional to this distance. Since we cannot know the distance to integrality a priori, we prove that the norm of the Moore–Penrose generalized inverse of the matrix of coefficients is a key factor for bounding this distance, and then we propose a preconditioning method to reduce this norm using lattice reduction techniques. We also propose a set of valid box constraints that help accelerate the RRBB method. Our computational results show that the proposed preconditioning significantly reduces the size of the RRBB enumeration tree, that the preconditioning combined with the proposed set of box constraints can significantly reduce the computational time of RRBB, and that the resulting RRBB method can outperform the Schnorr and Eucher method, a widely used method for solving integer least squares problems, on some types of problem data. Copyright Springer Science+Business Media New York 2014

Keywords: Integer least squares; Branch-and-bound methods; Lattice reduction; Preconditioning; Box constraints (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10898-014-0148-4

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