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Selecting a subset of diverse points based on the squared euclidean distance


In this paper we consider two closely related problems of selecting a diverse subset of points with respect to squared Euclidean distance. Given a set of points in Euclidean space, the first problem is to find a subset of a specified size M maximizing the sum of squared Euclidean distances between the chosen points. The second problem asks for a minimum cardinality subset of points, given a constraint on the sum of squared Euclidean distances between them. We consider the computational complexity of both problems and propose exact dynamic programming algorithms in the case of integer input data. If the dimension of the Euclidean space is bounded by a constant, these algorithms have a pseudo-polynomial time complexity. We also develop an FPTAS for the special case of the first problem, where the dimension of the Euclidean space is bounded by a constant.

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The authors thank Yulia Kovalenko for her helpful comments. The study presented in Section 3 was supported by the RFBR grant 19-01-00308, the study presented Section 6 was carried out within the state task of Sobolev Institute of Mathematics SB RAS.

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Correspondence to Anton V. Eremeev.

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Eremeev, A.V., Kel’manov, A.V., Kovalyov, M.Y. et al. Selecting a subset of diverse points based on the squared euclidean distance. Ann Math Artif Intell (2021).

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  • Euclidean space
  • Subset of points
  • Given size
  • Maximum variance
  • Strong NP-hardness
  • Integer instance
  • Exact algorithm
  • Fixed space dimension
  • Pseudo-polynomial time

Mathematics Subject Classification (2010)

  • 62H30
  • 90C09
  • 68W25