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dc.contributor.authorKosolobov, D.en
dc.contributor.authorMerkurev, O.en
dc.date.accessioned2021-08-31T14:57:41Z-
dc.date.available2021-08-31T14:57:41Z-
dc.date.issued2020-
dc.identifier.citationKosolobov D. Optimal skeleton huffman trees revisited / D. Kosolobov, O. Merkurev. — DOI 10.1007/978-3-030-50026-9_20 // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). — 2020. — Vol. 12159 LNCS. — P. 276-288.en
dc.identifier.isbn9783030500252-
dc.identifier.issn3029743-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Green3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087272570&doi=10.1007%2f978-3-030-50026-9_20&partnerID=40&md5=fd1f3061b624dbb858fd115f30f5cbf0
dc.identifier.otherhttp://arxiv.org/pdf/2001.05239m
dc.identifier.urihttp://elar.urfu.ru/handle/10995/101496-
dc.description.abstractA skeleton Huffman tree is a Huffman tree in which all disjoint maximal perfect subtrees are shrunk into leaves. Skeleton Huffman trees, besides saving storage space, are also used for faster decoding and for speeding up Huffman-shaped wavelet trees. In 2017 Klein et al. introduced an optimal skeleton tree: for given symbol frequencies, it has the least number of nodes among all optimal prefix-free code trees (not necessarily Huffman’s) with shrunk perfect subtrees. Klein et al. described a simple algorithm that, for fixed codeword lengths, finds a skeleton tree with the least number of nodes; with this algorithm one can process each set of optimal codeword lengths to find an optimal skeleton tree. However, there are exponentially many such sets in the worst case. We describe an (formula presented)-time algorithm that, given n symbol frequencies, constructs an optimal skeleton tree and its corresponding optimal code. © Springer Nature Switzerland AG 2020.en
dc.description.sponsorshipSupported by the Russian Science Foundation (RSF), project 18-71-00002.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherSpringeren
dc.relationinfo:eu-repo/grantAgreement/RSF//18-71-00002en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceLect. Notes Comput. Sci.2
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.subjectDYNAMIC PROGRAMMINGen
dc.subjectHUFFMAN TREEen
dc.subjectSKELETON TREEen
dc.subjectCODES (SYMBOLS)en
dc.subjectFORESTRYen
dc.subjectMUSCULOSKELETAL SYSTEMen
dc.subjectOPTIMAL SYSTEMSen
dc.subjectCODEWORD LENGTHen
dc.subjectHUFFMAN TREESen
dc.subjectOPTIMAL CODESen
dc.subjectPREFIX-FREE CODESen
dc.subjectSIMPLE ALGORITHMen
dc.subjectSKELETON TREEen
dc.subjectSTORAGE SPACESen
dc.subjectTIME ALGORITHMSen
dc.subjectTREES (MATHEMATICS)en
dc.titleOptimal skeleton huffman trees revisiteden
dc.typeConference Paperen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1007/978-3-030-50026-9_20-
dc.identifier.scopus85087272570-
local.contributor.employeeKosolobov, D., Ural Federal University, Ekaterinburg, Russian Federation
local.contributor.employeeMerkurev, O., Ural Federal University, Ekaterinburg, Russian Federation
local.description.firstpage276-
local.description.lastpage288-
local.volume12159 LNCS-
local.contributor.departmentUral Federal University, Ekaterinburg, Russian Federation
local.identifier.pure13387441-
local.identifier.pureb6978e04-022c-4e95-be53-0f58d4fddda8uuid
local.identifier.eid2-s2.0-85087272570-
local.fund.rsf18-71-00002-
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