Abstract
We consider the maximization \(\gamma = \max\{x^{T}\!Ax : x\in \{-1, 1\}^n\}\) for a given symmetric \(A\in\mathcal{R}^{n\times n}\). It was shown recently, using properties of zonotopes and hyperplane arrangements, that in the special case that A has a small rank, so that A = VV T where \(V\in\mathcal{R}^{n\times m}\) with m < n, then there exists a polynomial time algorithm (polynomial in n for a given m) to solve the problem \(\max\{x^TV V^Tx : x\in \{-1, 1\}^n\}\). In this paper, we use this result, as well as a spectral decomposition of A to obtain a sequence of non-increasing upper bounds on γ with no constraints on the rank of A. We also give easily computable necessary and sufficient conditions for the absence of a gap between the solution and its upper bound. Finally, we incorporate the semidefinite relaxation upper bound into our scheme and give an illustrative example.
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Halikias, G.D., Jaimoukha, I.M., Malik, U. et al. New bounds on the unconstrained quadratic integer programming problem. J Glob Optim 39, 543–554 (2007). https://doi.org/10.1007/s10898-007-9155-z
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DOI: https://doi.org/10.1007/s10898-007-9155-z