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Online maximum matching with recourse

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Abstract

We study the online maximum matching problem in a model in which the edges are associated with a known recourse parameter k. An online algorithm for this problem has to maintain a valid matching while edges of the underlying graph are presented one after the other. At any moment the algorithm can decide to include an edge into the matching or to exclude it, under the restriction that at most k such actions per edge take place, where k is typically a small constant. This problem was introduced and studied in the context of general online packing problems with recourse by Avitabile et al. (Inf Process Lett 113(3):81–86, 2013), whereas the special case \(k=2\) was studied by Boyar et al. (Proceedings of the 15th workshop on algorithms and data structures (WADS), pp 217–228, 2017). In the first part of this paper we consider the edge arrival model, in which an arriving edge never disappears from the graph. Here, we first show an improved analysis on the performance of the algorithm AMP of Avitabile et al., by exploiting the structure of the matching problem. In addition, we show that the greedy algorithm has competitive ratio 3/2 for every even k and ratio 2 for every odd k. Moreover, we present and analyze an improvement of the greedy algorithm which we call L-Greedy, and we show that for small values of k it outperforms the algorithm AMP. In terms of lower bounds, we show that no deterministic algorithm better than \(1+1/(k-1)\) exists, improving upon the known lower bound of \(1+1/k\). The second part of the paper is devoted to the edge arrival/departure model, which is the fully dynamic variant of online matching with recourse. The analysis of L-Greedy and AMP carry through in this model; moreover we show a lower bound of \((k^2-3k+6) / (k^2-4k+7)\) for all even \(k \ge 4\). For \(k\in \{2,3\}\), the competitive ratio is 3/2.

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Notes

  1. For consistency with other recourse models, we define the initial default state of an edge as rejected, and therefore rejecting a newly arriving edge does not count as decision modification. See also Sect. 1.3.1.

  2. We emphasize that in our work we consider the maximum cardinality matching problem; some previous work (with or without recourse) has considered the generalized weighted matching problem, in which each edge has a weight and the objective is to maximize the weight of edges in the matching.

  3. We can assume, without loss of generality, that this is the only viable choice for the online algorithm. This is because the only way an algorithm can transform a given matching \(M_1\) to a matching \(M_2\) is via a sequence which can only consist of the following: (i) augmenting paths; (ii) alternating cycles; and (iii) alternating, or even “decreasing” paths (i.e., paths such that if the algorithm applies them, then the cardinality of the matching remains the same, or decreases, respectively). This is a well-known result from matching theory. In principle an online algorithm, say A, could apply paths and cycles in cases (ii) and (iii), but such an algorithm can be converted to another algorithm \(A'\) which is at least as good as A in terms of matching size, and which maintains edges of smaller types than A.

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Correspondence to Spyros Angelopoulos.

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Supported by ANR OATA (ANR-15-CE40-0015), DIM RFSI DACM and Labex Mathématique Hadamard. Preliminary version appeared in the Proceedings of the 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS), 2018.

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Angelopoulos, S., Dürr, C. & Jin, S. Online maximum matching with recourse. J Comb Optim 40, 974–1007 (2020). https://doi.org/10.1007/s10878-020-00641-w

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