License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2021.8
URN: urn:nbn:de:0030-drops-147013
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14701/
Go to the corresponding LIPIcs Volume Portal

Balogh, János ; Cohen, Ilan Reuven ; Epstein, Leah ; Levin, Asaf

Truly Asymptotic Lower Bounds for Online Vector Bin Packing

pdf-format:
LIPIcs-APPROX8.pdf (0.7 MB)

Abstract

In this work, we consider online d-dimensional vector bin packing. It is known that no algorithm can have a competitive ratio of o(d/log² d) in the absolute sense, although upper bounds for this problem have always been presented in the asymptotic sense. Since variants of bin packing are traditionally studied with respect to the asymptotic measure, and since the two measures are different, we focus on the asymptotic measure and prove new lower bounds of the asymptotic competitive ratio. The existing lower bounds prior to this work were known to be smaller than 3, even for very large d. Here, we significantly improved on the best known lower bounds of the asymptotic competitive ratio (and as a byproduct, on the absolute competitive ratio) for online vector packing of vectors with d ≥ 3 dimensions, for every dimension d. To obtain these results, we use several different constructions, one of which is an adaptive construction with a lower bound of Ω(√d). Our main result is that the lower bound of Ω(d/log² d) on the competitive ratio holds also in the asymptotic sense. This result holds also against randomized algorithms, and requires a careful adaptation of constructions for online coloring, rather than simple black-box reductions.

BibTeX - Entry

@InProceedings{balogh_et_al:LIPIcs.APPROX/RANDOM.2021.8,
  author =	{Balogh, J\'{a}nos and Cohen, Ilan Reuven and Epstein, Leah and Levin, Asaf},
  title =	{{Truly Asymptotic Lower Bounds for Online Vector Bin Packing}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)},
  pages =	{8:1--8:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-207-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{207},
  editor =	{Wootters, Mary and Sanit\`{a}, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14701},
  URN =		{urn:nbn:de:0030-drops-147013},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2021.8},
  annote =	{Keywords: Bin packing, online algorithms, approximation algorithms, vector packing}
}

Keywords: Bin packing, online algorithms, approximation algorithms, vector packing
Collection: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)
Issue Date: 2021
Date of publication: 15.09.2021

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI