Abstract
In the bottleneck hyperplane clustering problem, given n points in \(\mathbb{R}^{d}\) and an integer k with 1≤k≤n, we wish to determine k hyperplanes and assign each point to a hyperplane so as to minimize the maximum Euclidean distance between each point and its assigned hyperplane. This mixed-integer nonlinear problem has several interesting applications but is computationally challenging due, among others, to the nonconvexity arising from the ℓ 2-norm. After comparing several linear approximations to deal with the ℓ 2-norm constraint, we propose a two-phase heuristic. First, an approximate solution is obtained by exploiting the ℓ ∞-approximation and the problem geometry, and then it is converted into an ℓ 2-approximate solution. Computational experiments on realistic randomly generated instances and instances arising from piecewise affine maps show that our heuristic provides good quality solutions in a reasonable amount of time.
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Amaldi, E., Dhyani, K. & Liberti, L. A two-phase heuristic for the bottleneck k-hyperplane clustering problem. Comput Optim Appl 56, 619–633 (2013). https://doi.org/10.1007/s10589-013-9567-2
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DOI: https://doi.org/10.1007/s10589-013-9567-2