Round Complexity of Common Randomness Generation: The Amortized Setting

Abstract

In this work we study the effect of rounds of interaction on the common randomness generation (CRG) problem. In the CRG problem, two parties, Alice and Bob, receive samples X i and Y i , respectively, where (X i , Y i ) are drawn jointly from a source distribution µ. The two parties wish to agree on a common random key consisting of many bits of randomness, by exchanging messages that depend on each party's respective input and the previous messages. In this work we study the amortized version of the problem, i.e., the number of bits of communication needed per random bit output by Alice and Bob, in the limit as the number of bits generated tends to infinity. The amortized version of the CRG problem has been extensively studied in the information theory literature, though very little was known about the effect of interaction on this problem. Recently Bafna et al. (SODA 2019) considered the non-amortized version of the problem (so here the goal of the interaction is to generate a fixed number of random bits): they gave a family of sources µ r,n parameterized by r, n ∈ N, such that with r + 2 rounds of communication one can generate n bits of common randomness with this source with O(r log n) communication, whereas with roughly r/2 rounds the communication complexity is Ω(n/ poly log n). Note in particular that their source is designed with the target number of bits in mind and hence the result does not apply to the amortized setting.In this work we strengthen the work of Bafna et al. in two ways: First we show that the results extend to the classical amortized setting. We also reduce the gap between the round complexity in the upper and lower bounds to an additive constant. Specifically we show that for every pair r, n ∈ N the (amortized) communication complexity to generate Ω(n) bits of common randomness from the source µ r,n using r + 2 rounds of communication is O(r log n) whereas the amortized communication required to generate the same amount of randomness from r rounds is Ω( √ n). Our techniques exploit known connections between information complexity and CRG, and the main novelty is our ability to analyze the information complexity of protocols getting inputs from the source µ r,n .

Multi-Pass Graph Streaming Lower Bounds for Cycle Counting, MAX-CUT, Matching Size, and Other Problems

“…There are however several differences between a typical pointer chasing problem (see, e.g. [7,27,50,52,79,87] for many different variants) and our problem. Most important among these is that the strong promise in the input effectively means there is no single particular pointer that the players need to chase-all they need to do is to figure out P (v) for some v ∈ V after communicating the messages (this is on top of apparent issues such as players being able to chase pointers from "both ends" and the like).…”