The hardness of median in the synchronized bit communication model
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
The cost of fault tolerance in multi-party communication complexity
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
Space-bounded communication complexity
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
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We consider two natural extensions of the communication complexity model that are inspired by distributed computing. In both models, two parties are equipped with synchronized discrete clocks, and we assume that a bit can be sent from one party to another in one step of time. Both models allow implicit communication, by allowing the parties to choose whether to send a bit during each step. We examine trade-offs between time (total number of possible time steps elapsed) and communication (total number of bits actually sent). In the synchronized bit model, we measure the total number of bits sent between the two parties (e.g., email). We show that, in this model, communication costs can differ from the usual communication complexity by a factor roughly logarithmic in the number of time steps, and no more than such a factor. In the synchronized connection model, both parties choose whether or not to open their end of the communication channel at each time step. An exchange of bits takes place only when both ends of the channel are open (e.g., instant messaging), in which case we say that a {\em connection} has occurred. If a party does not open its end, it does not learn whether the other party opened its channel. When we restrict the number of time steps to be polynomial in the input length, and the number of connections to be polylogarithmic in the input length, the class of problems solved with this model turns out to be roughly equivalent to the communication complexity analogue of P^{NP}. Using our new model, we give what we believe to be the first lower bounds for this class, separating P^{NP} from Sigma_2 intersect Pi_2 in the communication complexity setting. Although these models are both quite natural, they have unexpected power, and lead to a refinement of problem classifications in communication complexity.