Parameterized Dynamic Cluster Editing

Luo, Jiaqi; Molter, Hendrik; Nichterlein, André; Niedermeier, Rolf

doi:10.1007/s00453-020-00746-y

Cited by 15 publications

(10 citation statements)

References 44 publications

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“…Finally, let us briefly mention that the motivation for our incremental scenario for stable matching is related to similar scenarios in the context of clustering (Charikar et al 2004;Luo et al 2018), coloring (Hartung and Niedermeier 2013), other dynamic versions of parameterized problems (Abu-Khzam et al 2015;Krithika, Sahu, and Tale 2018), and reoptimization (Böckenhauer et al 2018;Schieber et al 2018).…”

Section: Related Workmentioning

confidence: 99%

Adapting Stable Matchings to Evolving Preferences

Bredereck

Knop

Luo

et al. 2020

AAAI

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Adaptivity to changing environments and constraints is key to success in modern society. We address this by proposing “incrementalized versions” of Stable Marriage and Stable Roommates. That is, we try to answer the following question: for both problems, what is the computational cost of adapting an existing stable matching after some of the preferences of the agents have changed. While doing so, we also model the constraint that the new stable matching shall be not too different from the old one. After formalizing these incremental versions, we provide a fairly comprehensive picture of the computational complexity landscape of Incremental Stable Marriage and Incremental Stable Roommates. To this end, we exploit the parameters “degree of change” both in the input (difference between old and new preference profile) and in the output (difference between old and new stable matching). We obtain both hardness and tractability results, in particular showing a fixed-parameter tractability result with respect to the parameter “distance between old and new stable matching”.

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Section: Related Workmentioning

confidence: 99%

Adapting Stable Matchings to Evolving Preferences

Bredereck

Knop

Luo

et al. 2020

AAAI

Self Cite

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“…It would be interesting to study the application of this turbo-charging method to similar graph partitioning problems. In fact, a dynamic version of the Cluster Editing problem (CE) was recently introduced and studied in [19]. It would also be interesting to extend this work to the multi-parameterized version introduced in [1] and to possibly apply our enhanced turbo-charging method to such graph partitioning problems.…”

Section: Discussionmentioning

confidence: 99%

A Dynamically Turbo-Charged Greedy Heuristic for Graph Coloring

Abu-Khzam,

Chahine

2018

Preprint

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We introduce a dynamic version of the graph coloring problem and prove its fixed-parameter tractability with respect to the editparameter. This is used to present a turbo-charged heuristic for the problem that works by combining the turbo-charging technique with other standard heuristic tools, including greedy coloring. The recently introduced turbo-charging idea is further enhanced in this paper by introducing a dynamic version of the so called moment of regret and rollback points. Experiments comparing the presented turbo-charging algorithm to other (non-turbo-charged) heuristics demonstrate its effectiveness. Our algorithm often produced results that were either exact or better than all the other available heuristics.

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“…Finally, let us briefly mention that the motivation for our incremental scenario for stable matching is related to similar scenarios in the context of clustering [6,22], coloring [18], other dynamic versions of parameterized problems [1,21], and reoptimization [4,30].…”

Section: Related Workmentioning

confidence: 99%

Adapting Stable Matchings to Evolving Preferences

Bredereck¹,

Knop²,

Luo³

et al. 2019

Preprint

Self Cite

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Adaptivity to changing environments and constraints is key to success in modern society. We address this view by proposing "incrementalized versions" of Stable Marriage and Stable Roommates. That is, we try to answer the following question: for both problems, what is the cost of adapting an existing stable matching after some of the preferences of the agents have changed. While doing so, we also model the constraint that the new stable matching shall be close to the old one. After formalizing these incremental versions, we provide a fairly comprehensive picture of the computational complexity landscape of Incremental Stable Marriage and Incremental Stable Roommates. To this end, we exploit the parameters "degree of change" both in the input (difference between old and new preference profile) and in the output (difference between old and new stable matching). We obtain both hardness and tractability results, among the latter being one of the few fixed-parameter tractability results (exploiting the parameter "distance between old and new stable matching") in the context of computationally hard stable matching problems. * Supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement numbers 677651. † Supported by the DFG, project MaMu (NI 369/19). ‡ Supported by CAS-DAAD Joint Fellowship Program for Doctoral Students of UCAS. Work done while with TU Berlin. 1 The naming "Incremental" is inspired by work in the context of clustering and information retrieval [6].

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Parameterized Dynamic Cluster Editing

Cited by 15 publications

References 44 publications

Adapting Stable Matchings to Evolving Preferences

Adapting Stable Matchings to Evolving Preferences

A Dynamically Turbo-Charged Greedy Heuristic for Graph Coloring

Adapting Stable Matchings to Evolving Preferences

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