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  • We give a simple proof and a generalization of the classical result which says that the (asymptotic) approximation ratio of BestFit algorithm is 1.7. We generalize this result to a wide class of algorithms that are allowed to pack the incoming item to any bin with load larger than 1/2 (if it fits), instead to the most full bin, and at the same time this class includes the bounded-space variants of these algorithms.
  • We give a simple proof and a generalization of the classical result which says that the (asymptotic) approximation ratio of BestFit algorithm is 1.7. We generalize this result to a wide class of algorithms that are allowed to pack the incoming item to any bin with load larger than 1/2 (if it fits), instead to the most full bin, and at the same time this class includes the bounded-space variants of these algorithms. (en)
Title
  • A new analysis of best fit bin packing
  • A new analysis of best fit bin packing (en)
skos:prefLabel
  • A new analysis of best fit bin packing
  • A new analysis of best fit bin packing (en)
skos:notation
  • RIV/67985840:_____/12:00387099!RIV13-AV0-67985840
http://linked.open...avai/riv/aktivita
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  • P(IAA100190902)
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  • 120339
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  • RIV/67985840:_____/12:00387099
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  • problem complexity; approximation (en)
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http://linked.open...ontrolniKodProRIV
  • [38E963D72928]
http://linked.open...v/mistoKonaniAkce
  • Venice
http://linked.open...i/riv/mistoVydani
  • Heidelberg
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  • Fun with Algorithms
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  • Sgall, Jiří
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number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-30347-0_31
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  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-30346-3
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