We study the distributed simulation problem where @math players aim to generate sequences of random coin flips where some subsets of the players share an independent common coin which can be tossed multiple times, and there is a publicly seen blackboard through which the players communicate with each other. We provide a tight representation of the optimal communication rates via linear programming, and more importantly, propose explicit algorithms for the optimal distributed simulation for a wide class of hypergraphs. In particular, the optimal communication rate in complete hypergraphs is still achievable in sparser hypergraphs containing a path-connected cycle-free cluster of topologically connected components. Some key steps in analyzing the upper bounds rely on two different definitions of connectivity in hypergraphs, which may be of independent interest.
We also review some literature on the communication complexity. First introduced in @cite_22 , the blackboard communication protocol serves as an elegant mathematical framework for the study of communication complexity. A series of research are devoted to the lower bounds in communication complexity, where the is the prominent tool for all the deterministic @cite_14 @cite_32 , nondeterministic @cite_25 and randomized communication complexities @cite_42 @cite_6 @cite_43 . We refer to @cite_26 for a survey of these methods. Another closely-related problem is distributed inference under communication constraints @cite_12 , where distributed simulation of private public randomness is useful for distributed learning and property testing @cite_1 @cite_44 . To establish lower bounds on the communication complexity in distributed inference, the copy-paste property of the blackboard communication model typically plays an important role @cite_31 @cite_9 . However, our technique to establish the lower bound is different, where only the sequential nature of the blackboard communication protocol is used in the proof of Theorem (cf. Lemma ), which may be of independent interest.
@cite_14 @cite_26 @cite_22 @cite_9 @cite_42 @cite_1 @cite_32 @cite_6 @cite_44 @cite_43 @cite_31 @cite_25 @cite_12
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In this paper we prove lower bounds on randomized multiparty communication complexity, mainly in the message-passing model, where messages are sent player-to-player. Some of our results apply to the blackboard model, where each message is written on a blackboard for all players to see. We introduce a new technique for proving such bounds, called symmetrization, which is natural, intuitive, and often easy to use. For example, for the problem where each of @math players gets a bit-vector of length @math , and the goal is to compute the coordinatewise XOR of these vectors, we prove a tight lower bound of @math in the blackboard model. For the same problem with AND instead of XOR, we prove a lower bound of roughly @math in the message-passing model (assuming @math ) and @math in the blackboard model. We also prove lower bounds for bitwise majority, for a graph-connectivity problem, and for other problems; the technique seems applicable to a wide range of other problems as well. A... This paper provides the first general technique for proving information lower bounds on two-party unbounded-rounds communication problems. We show that the discrepancy lower bound, which applies to randomized communication complexity, also applies to information complexity. More precisely, if the discrepancy of a two-party function @math with respect to a distribution @math is @math , then any two party randomized protocol computing @math must reveal at least @math bits of information to the participants. As a corollary, we obtain that any two-party protocol for computing a random function on @math must reveal @math bits of information to the participants. In addition, we prove that the discrepancy of the Greater-Than function is @math , which provides an alternative proof to the recent proof of Viola Viola11 of the @math lower bound on the communication complexity of this well-studied function and, combined with our main result, proves the tight @math lower bound on its information complexity. The proof of our main result develops a new simulation procedure that may be of an independent interest. In a very recent breakthrough work of kerenidis2012lower , this simulation procedure was the main building block for proving that almost all known lower bound techniques for communication complexity (and not just discrepancy) apply to information complexity. We study a model of communication complexity that encompasses many well-studied problems, including classical and quantum communication complexity, the complexity of simulating distributions arising from bipartite measurements of shared quantum states, and XOR games. In this model, Alice gets an input x, Bob gets an input y, and their goal is to each produce an output a, b distributed according to some pre-specified joint distribution p(a, b|x, y). Our results apply to any non-signaling distribution, that is, those where Alice's marginal distribution does not depend on Bob's input, and vice versa. By taking a geometric view of the non-signaling distributions, we introduce a simple new technique based on affine combinations of lower-complexity distributions, and we give the first general technique to apply to all these settings, with elementary proofs and very intuitive interpretations. Specifically, we introduce two complexity measures, one which gives lower bounds on classical communication, and one for quantum communication. These measures can be expressed as convex optimization problems. We show that the dual formulations have a striking interpretation, since they coincide with maximum violations of Bell and Tsirelson inequalities. The dual expressions are closely related to the winning probability of XOR games. Despite their apparent simplicity, these lower bounds subsume many known communication complexity lower bound methods, most notably the recent lower bounds of Linial and Shraibman for the special case of Boolean functions. We show that as in the case of Boolean functions, the gap between the quantum and classical lower bounds is at most linear in the size of the support of the distribution, and does not depend on the size of the inputs. This translates into a bound on the gap between maximal Bell and Tsirelson inequality violations, which was previously known only for the case of distributions with Boolean outcomes and uniform marginals. It also allows us to show that for some distributions, information theoretic methods are necessary to prove strong lower bounds. Finally, we give an exponential upper bound on quantum and classical communication complexity in the simultaneous messages model, for any non-signaling distribution. One consequence of this is a simple proof that any quantum distribution can be approximated with a constant number of bits of communication. Communication is a bottleneck in many distributed computations. In VLSI, communication constraints dictate lower bounds on the performance of chips. The two-processor information transfer model measures the communication requirements to compute functions. We study the unbounded error probabilistic version of this model. Because of its weak notion of correct output, we believe that this model measures the “intrinsic” communication complexity of functions. We present exact characterizations of the unbounded error communication complexity in terms of arrangements of hyperplanes and approximations of matrices. These characterizations establish the connection with certain classical problems in combinatorial geometry which are concerned with the configurations of points in d-dimensional real space. With the help of these characterizations, we obtain some upper and lower bounds on communication complexity. The upper bounds which we obtained for the functions—equality and verification of Hamming distance—are considerably better than their counterparts in the deterministic, the nondeterministic, and the bounded error probabilistic models. We also exhibit a function which has log n complexity. We present a counting argument to show that most functions have linear complexity. Further, we apply the logarithmic lower bound on communication complexity to obtain an Ω(n log n) bound on the time of 1-tape unbounded error probabilistic Turing machines. We believe that this is the first nontrivial lower bound obtained for such machines.