Batu, Tugkan 
ORCID: 0000-0003-3914-4645, Dasgupta, Sanjoy, Kumar, Ravi and Rubinfeld, Ronitt
(2002)
The complexity of approximating entropy.
In:
Proceedings of the Thiry-Fourth Annual ACM Symposium on Theory of Computing - Stoc '02.
ACM Press, New York, USA, pp. 678-687.
ISBN 1581134959
Abstract
We consider the problem of approximating the entropy of a discrete distribution under several models. If the distribution is given explicitly as an array where the i-th location is the probability of the i-th element, then linear time is both necessary and sufficient for approximating the entropy.We consider a model in which the algorithm is given access only to independent samples from the distribution. Here, we show that a &lgr;-multiplicative approximation to the entropy can be obtained in O(n(1+η)/&lgr;2 < poly(log n)) time for distributions with entropy Ω(&lgr; η), where n is the size of the domain of the distribution and η is an arbitrarily small positive constant. We show that one cannot get a multiplicative approximation to the entropy in general in this model. Even for the class of distributions to which our upper bound applies, we obtain a lower bound of Ω(nmax(1/(2&lgr;2), 2/(5&lgr;2—2)).We next consider a hybrid model in which both the explicit distribution as well as independent samples are available. Here, significantly more efficient algorithms can be achieved: a &lgr;-multiplicative approximation to the entropy can be obtained in O(&lgr;2.Finally, we consider two special families of distributions: those for which the probability of an element decreases monotonically in the label of the element, and those that are uniform over a subset of the domain. In each case, we give more efficient algorithms for approximating the entropy.
| Item Type: | Book Section |
|---|---|
| Official URL: | http://portal.acm.org/citation.cfm?doid=509907.510... |
| Additional Information: | © 2002 ACM |
| Divisions: | Mathematics |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Date Deposited: | 05 Jan 2011 12:32 |
| Last Modified: | 13 Sep 2024 15:40 |
| URI: | http://eprints.lse.ac.uk/id/eprint/31084 |
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