eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2016-08-18
37:1
37:16
10.4230/LIPIcs.ESA.2016.37
article
On the Power of Advice and Randomization for Online Bipartite Matching
Dürr, Christoph
Konrad, Christian
Renault, Marc
While randomized online algorithms have access to a sequence of uniform random bits, deterministic online algorithms with advice have access to a sequence of advice bits, i.e., bits that are set by an all-powerful oracle prior to the processing of the request sequence. Advice bits are at least as helpful as random bits, but how helpful are they? In this work, we investigate the power of advice bits and random bits for online maximum bipartite matching (MBM).
The well-known Karp-Vazirani-Vazirani algorithm [Karp, Vazirani and Vazirani 90] is an optimal randomized (1-1/e)-competitive algorithm for MBM that requires access to Theta(n log n) uniform random bits. We show that Omega(log(1/epsilon) n) advice bits are necessary and O(1/epsilon^5 n) sufficient in order to obtain a (1-epsilon)-competitive deterministic advice algorithm. Furthermore, for a large natural class of deterministic advice algorithms, we prove that Omega(log log log n) advice bits are required in order to improve on the 1/2-competitiveness of the best deterministic online algorithm, while it is known that O(log n) bits are sufficient [Böckenhauer, Komm, Královic and Královic 2011].
Last, we give a randomized online algorithm that uses cn random bits, for integers c >= 1, and a competitive ratio that approaches 1-1/e very quickly as c is increasing. For example if c = 10, then the difference between 1-1/e and the achieved competitive ratio is less than 0.0002.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol057-esa2016/LIPIcs.ESA.2016.37/LIPIcs.ESA.2016.37.pdf
On-line algorithms
Bipartite matching
Randomization