Cluster Approach to the Domains Formation

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dc.creatorLitinskii, Leonid B.
dc.date2008-03-26
dc.date.accessioned2026-07-07T09:28:38Z
dc.date.available2026-07-07T09:28:38Z
dc.descriptionAs a rule, a quadratic functional depending on a great number of binary variables has a lot of local minima. One of approaches allowing one to find in averaged deeper local minima is aggregation of binary variables into larger blocks/domains. To minimize the functional one has to change the states of aggregated variables (domains). In the present publication we discuss methods of domains formation. It is shown that the best results are obtained when domains are formed by variables that are strongly connected with each other.
dc.description11 pages, 5 figures, PDF-file
dc.identifierhttps://arxiv.org/abs/0803.3746
dc.identifierhttp://arxiv.org/abs/0803.3746
dc.identifierOptical Memory & Neural Networks (Information Optics), 2007, v.16(3) pp.144-153
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/157498
dc.subjectNeural and Evolutionary Computing
dc.subjectData Structures and Algorithms
dc.titleCluster Approach to the Domains Formation
dc.typetext

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