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Journal Articles Journal of Complexity Year : 2022

Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions

François Clément
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Carola Doerr
Luís Paquete
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Abstract

Motivated by applications in instance selection, we introduce the star discrepancy subset selection problem, which consists of finding a subset of m out of n points that minimizes the star discrepancy. First, we show that this problem is NP-hard. Then, we introduce a mixed integer linear formulation (MILP) and a combinatorial branch-and-bound (BB) algorithm for the star discrepancy subset selection problem and we evaluate both approaches against random subset selection and a greedy construction on different use-cases in dimension two and three. Our results show that the MILP and BB are efficient in dimension two for large and small m/n ratio, respectively, and for not too large n. However, the performance of both approaches decays strongly for larger dimensions and set sizes. As a side effect of our empirical comparisons we obtain point sets of discrepancy values that are much smaller than those of common low-discrepancy sequences, random point sets, and of Latin Hypercube Sampling. This suggests that subset selection could be an interesting approach for generating point sets of small discrepancy value.
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Dates and versions

hal-03520656 , version 1 (11-01-2022)

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François Clément, Carola Doerr, Luís Paquete. Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions. Journal of Complexity, 2022, 70, pp.101645. ⟨10.1016/j.jco.2022.101645⟩. ⟨hal-03520656⟩
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