The communication complexity of a function f(x,y) measures the number of bits that two players, one who knows x and the other who knows y, must exchange to determine the value f(x,y). Communication complexity is a fundamental measure of complexity of functions. Lower bounds on this measure lead to lower bounds on many other measures of computational complexity. This monograph surveys lower bounds in the field of communication complexity. Our focus is on lower bounds that work by first representing the communication complexity measure in Euclidean space. That is to say, the first step in these lower bound techniques is to find a geometric complexity measure, such as rank or trace norm, that serves as a lower bound to the underlying communication complexity measure. Lower bounds on this geometric complexity measure are then found using algebraic and geometric tools.
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15 October 2009
Research Article|
October 15 2009
Lower Bounds in Communication Complexity
Troy Lee;
Troy Lee
Columbia University
, New York, NY 10027, USA
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Adi Shraibman
Adi Shraibman
Department of Mathematics,
Weizmann Institute
, Rehovot, 76100, Israel
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Online ISSN: 1551-3068
Print ISSN: 1551-305X
© 2009 T. Lee and A. Shraibman
2009
T. Lee and A. Shraibman
Licensed re-use rights only
Foundations and Trends in Theoretical Computer Science (2009) 3 (4): 263–399.
Citation
Lee T, Shraibman A (2009), "Lower Bounds in Communication Complexity". Foundations and Trends in Theoretical Computer Science, Vol. 3 No. 4 pp. 263–399, doi: https://doi.org/10.1561/0400000040
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