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On the elimination of inessential points in the smallest enclosing ball problem

Abstract : We consider the construction of the smallest ball B * enclosing a set Xn formed by n points in R d. We show that any probability measure on Xn, with mean c and variance matrix V , provides a lower bound b on the distance to c of any point on the boundary of B * , with b having a simple expression in terms of c and V. This inequality permits to remove inessential points from Xn, which do not participate to the definition of B * , and can be used to accelerate algorithms for the construction of B *. We show that this inequality is, in some sense, the best possible. A series of numerical examples indicates that, when d is reasonable small (d ≤ 10, say) and n is large (up to 10 5), the elimination of inessential points by a suitable two-point measure, followed by a direct (exact) solution by quadratic programming, outperforms iterative methods that compute an approximate solution by solving the dual problem.
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Contributor : Luc Pronzato <>
Submitted on : Tuesday, August 28, 2018 - 5:20:58 PM
Last modification on : Tuesday, March 30, 2021 - 9:24:24 AM
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Luc Pronzato. On the elimination of inessential points in the smallest enclosing ball problem. Optimization Methods and Software, Taylor & Francis, 2019, 34 (2), pp.225-247. ⟨10.1080/10556788.2017.1359266⟩. ⟨hal-01863587⟩



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