## Abstract

The matching problem in the online setting models the following situation: we are given a set of servers in advance, the clients arrive one at a time, and each client has edges to some of the servers. Each client must be matched to some incident server upon arrival (or left unmatched) and the algorithm is not allowed to reverse its decisions. Due to this no-reversal restriction, we are not able to guarantee an exact maximum matching in this model, only an approximate one. Therefore, it is natural to study a different setting, where the top priority is to match as many clients as possible, and changes to the matching are possible but expensive. Formally, the goal is to always maintain a maximum matching while minimizing the number of changes made to the matching (denoted the recourse). This model is called the online model with recourse, and has been studied extensively over the past few years. For the specific problem of matching, the focus has been on vertex-arrival model, where clients arrive one at a time with all their edges. A recent result of Bernstein et al. [1] gives an upper bound of O (n log^{2} n) recourse for the case of general bipartite graphs. For trees the best known bound is O(n log n) recourse, due to Bosek et al. [4]. These are nearly tight, as a lower bound of Ω(n log n) is known. In this paper, we consider the more general model where all the vertices are known in advance, but the edges of the graph are revealed one at a time. Even for the simple case where the graph is a path, there is a lower bound of Ω(n^{2}). Therefore, we instead consider the natural relaxation where the graph is worst-case, but the edges are revealed in a random order. This relaxation is motivated by the fact that in many related models, such as the streaming setting or the standard online setting without recourse, faster algorithms have been obtained for the matching problem when the input comes in a random order. Our results are as follows: Our main result is that for the case of general (non-bipartite) graphs, the problem with random edge arrivals is almost as hard as in the adversarial setting: we show a family of graphs for which the expected recourse is Ω (n^{2}/log n) . We show that for some special cases of graphs, random arrival is significantly easier. For the case of trees, we get an upper bound of O (n log^{2} n) on the expected recourse. For the case of paths, this upper bound is O (n log n). We also show that the latter bound is tight, i.e. that the expected recourse is at least Ω (n log n).

Original language | English (US) |
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Title of host publication | 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2020 |

Editors | Nitin Saxena, Sunil Simon |

Publisher | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |

ISBN (Electronic) | 9783959771740 |

DOIs | |

State | Published - Dec 2020 |

Event | 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2020 - Virtual, Goa, India Duration: Dec 14 2020 → Dec 18 2020 |

### Publication series

Name | Leibniz International Proceedings in Informatics, LIPIcs |
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Volume | 182 |

ISSN (Print) | 1868-8969 |

### Conference

Conference | 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2020 |
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Country/Territory | India |

City | Virtual, Goa |

Period | 12/14/20 → 12/18/20 |

## Keywords

- Edge-arrival
- Matchings
- Online model

## ASJC Scopus subject areas

- Software