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Information Complexity Is Computable

Authors Mark Braverman, Jon Schneider

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  • Communication complexity
  • convergence rate
  • information complexity

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Abstract

The information complexity of a function f is the minimum amount of information Alice and Bob need to exchange to compute the function f. In this paper we provide an algorithm for approximating the information complexity of an arbitrary function f to within any additive error epsilon > 0, thus resolving an open question as to whether information complexity is computable. 

In the process, we give the first explicit upper bound on the rate of convergence of the information complexity of f when restricted to b-bit protocols to the (unrestricted) information complexity of f.

Cite As Get BibTex

Mark Braverman and Jon Schneider. Information Complexity Is Computable. In 43rd International Colloquium on Automata, Languages, and Programming (ICALP 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 55, pp. 87:1-87:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016) https://doi.org/10.4230/LIPIcs.ICALP.2016.87

Author Details

Mark Braverman
Jon Schneider

References

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