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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Contributeur : Luc Pronzato <>
Soumis le : mardi 28 août 2018 - 17:20:58
Dernière modification le : lundi 5 novembre 2018 - 15:52:02
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Luc Pronzato. On the elimination of inessential points in the smallest enclosing ball problem. Optimization Methods and Software, Taylor & Francis, In press, 〈http://www.tandfonline.com/eprint/Ee6xQ2f8DQmawDarI6zK/full〉. 〈10.1080/10556788.2017.1359266〉. 〈hal-01863587〉



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