Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIT.2009.2034824
Title: The Communication complexity of correlation
Authors: Harsha, P.
Jain, R. 
McAllester, D.
Radhakrishnan, J.
Keywords: Communication complexity
Direct sum
Mutual information
Rejection sampling
Relative entropy
Issue Date: 2010
Source: Harsha, P., Jain, R., McAllester, D., Radhakrishnan, J. (2010). The Communication complexity of correlation. IEEE Transactions on Information Theory 56 (1) : 438-449. ScholarBank@NUS Repository. https://doi.org/10.1109/TIT.2009.2034824
Abstract: Let X and Y be finite nonempty sets and (X, Y) a pair of random variables taking values in X × Y. We consider communication protocols between two parties, ALICE and BOB, for generating X and Y. ALICE is provided an x ε X generated according to the distribution of X, and is required to send a message to BOB in order to enable him to generate y ε Y, whose distribution is the same as that of Y|X=x. Both parties have access to a shared random string generated in advance. Let T[X : Y] be the minimum (over all protocols) of the expected number of bits ALICE needs to transmit to achieve this. We show that I[X : Y] ≤ T[X : Y] ≤ I[X : Y] + 2 log2, (I[X : Y] + 1) + O(1). We also consider the worst case communication required for this problem, where we seek to minimize the average number of bits ALICE must transmit for the worst case x ε X. We show that the communication required in this case is related to the capacity C(E) of the channel E, derived from (X, Y ), that maps x ε X to the distribution of Y|X=x . We also show that the required communication T(E) satisfies C(E) ≤T(E) ≤ C(E) + 2log2(C(E) + 1) + O(1). Using the first result, we derive a direct-sum theorem in communication complexity that substantially improves the previous such result shown by Jain, Radhakrishnan, and Sen [In Proc. 30th International Colloquium of Automata, Languages and Programming (ICALP), ser. Lecture Notes in Computer Science, vol. 2719.2003, pp. 300-315]. These results are obtained by employing a rejection sampling procedure that relates the relative entropy between two distributions to the communication complexity of generating one distribution from the other. © 2009 IEEE.
Source Title: IEEE Transactions on Information Theory
URI: http://scholarbank.nus.edu.sg/handle/10635/39040
ISSN: 00189448
DOI: 10.1109/TIT.2009.2034824
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