• DOI:10.1145/2746539.2746572
• Corpus ID: 15361448

Exponential Separation of Information and Communication for Boolean Functions

@article{Ganor2014ExponentialSO,
  title={Exponential Separation of Information and Communication for Boolean Functions},
  author={Anat Ganor and Gillat Kol and Ran Raz},
  journal={Proceedings of the forty-seventh annual ACM symposium on Theory of Computing},
  year={2014}
}

• A. Ganor, Gillat Kol, R. Raz
• Published 14 June 2015
• Computer Science
• Proceedings of the forty-seventh annual ACM symposium on Theory of Computing

We show an exponential gap between communication complexity and information complexity for boolean functions, by giving an explicit example of a partial function with information complexity ≤ O(k), and distributional communication complexity ≥ 2k. This shows that a communication protocol for a partial boolean function cannot always be compressed to its internal information. By a result of Braverman [Bra12], our gap is the largest possible. By a result of Braverman and Rao [BR11], our example shows a gap between communication complexity and amortized communication complexity, implying that a tight direct sum result for distributional communication complexity of boolean functions cannot hold, answering a long standing open problem. Our techniques build on [GKR14], that proved a similar result for relations with very long outputs (double exponentially long in k). In addition to the stronger result, the current work gives a simpler proof, benefiting from the short output length of boolean functions. Another (conceptual) contribution of our work is the relative discrepancy method, a new rectangle-based method for proving communication complexity lower bounds for boolean functions, powerful enough to separate information complexity and communication complexity.