A review on algorithms for maximum clique problems
| Autor: | Qinghua Wu, Jin-Kao Hao |
|---|---|
| Přispěvatelé: | Institute of Computing Technology [Beijing] (ICT), Chinese Academy of Sciences [Changchun Branch] (CAS), Laboratoire d'Etudes et de Recherche en Informatique d'Angers (LERIA), Université d'Angers (UA) |
| Jazyk: | francouzština |
| Rok vydání: | 2015 |
| Předmět: |
Information Systems and Management
General Computer Science business.industry Maximum clique problems Management Science and Operations Research Machine learning computer.software_genre Industrial and Manufacturing Engineering Graph Clique problem Modeling and Simulation Applications Heuristics [INFO]Computer Science [cs] Artificial intelligence business computer Algorithm Exact algorithms Mathematics |
| Zdroj: | European Journal of Operational Research European Journal of Operational Research, Elsevier, 2015, 242 (3), pp.693-709. ⟨10.1016/j.ejor.2014.09.064⟩ |
| ISSN: | 0377-2217 |
| DOI: | 10.1016/j.ejor.2014.09.064⟩ |
| Popis: | International audience; The maximum clique problem (MCP) is to determine in a graph a clique (i.e., a complete subgraph) of maximum cardinality. The MCP is notable for its capability of modeling other combinatorial problems and real-world applications. As one of the most studied NP-hard problems, many algorithms are available in the literature and new methods are continually being proposed. Given that the two existing surveys on the MCP date back to 1994 and 1999 respectively, one primary goal of this paper is to provide an updated and comprehensive review on both exact and heuristic MCP algorithms, with a special focus on recent developments. To be informative, we identify the general framework followed by these algorithms and pinpoint the key ingredients that make them successful. By classifying the main search strategies and putting forward the critical elements of the most relevant clique methods, this review intends to encourage future development of more powerful methods and motivate new applications of the clique approaches. |
| Databáze: | OpenAIRE |
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