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The complexity of approximating entropy

Batu, Tugkan and Dasgupta, Sanjoy and 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, New York, USA, pp. 678-687. ISBN 1581134959

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Identification Number: 10.1145/509907.510005

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
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Sets: Departments > Mathematics
Date Deposited: 05 Jan 2011 12:32
Last Modified: 06 Jan 2011 18:03
URI: http://eprints.lse.ac.uk/id/eprint/31084

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