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The conditional p-dispersion problem

Marilène Cherkesly () and Claudio Contardo ()
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Marilène Cherkesly: ESG UQAM
Claudio Contardo: ESG UQAM

Journal of Global Optimization, 2021, vol. 81, issue 1, No 3, 23-83

Abstract: Abstract We introduce the conditional p-dispersion problem (c-pDP), an incremental variant of the p-dispersion problem (pDP). In the c-pDP, one is given a set N of n points, a symmetric dissimilarity matrix D of dimensions $$n\times n$$ n × n , an integer $$p\ge 1$$ p ≥ 1 and a set $$Q\subseteq N$$ Q ⊆ N of cardinality $$q\ge 1$$ q ≥ 1 . The objective is to select a set $$P\subset N\setminus Q$$ P ⊂ N \ Q of cardinality p that maximizes the minimal dissimilarity between every pair of selected vertices, i.e., $$z(P\cup Q) {:}{=}\min \{D(i, j), i, j\in P\cup Q\}$$ z ( P ∪ Q ) : = min { D ( i , j ) , i , j ∈ P ∪ Q } . The set Q may model a predefined subset of preferences or hard location constraints in incremental network design. We adapt the state-of-the-art algorithm for the pDP to the c-pDP and include an ad-hoc acceleration mechanism designed to leverage the information provided by the set Q to further reduce the size of the problem instance. We perform exhaustive computational experiments and show that the proposed acceleration mechanism helps reduce the total computational time by a factor of five on average. We also assess the scalability of the algorithm and derive sensitivity analyses.

Keywords: p-Dispersion; Clustering; Conditional p-dispersion; Exact methods (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10898-020-00962-4

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