We introduce new models and new information theoretic measures for the study of communication complexity in the natural peer-to-peer, multi-party, number-in-hand setting. We prove a number of properties of our new models and measures, and then, in order to exemplify their effectiveness, we use them to prove two lower bounds. The more elaborate one is a tight lower bound of $Ω(kn)$ on the multi-party peer-to-peer randomized communication complexity of the $k$-player, $n$-bit Disjointness function. The other one is a tight lower bound of $Ω(kn)$ on the multi-party peer-to-peer randomized communication complexity of the $k$-player, $n$-bit bitwise parity function. Both lower bounds hold when ${n=Ω(k)}$. The lower bound for Disjointness improves over the lower bound that can be inferred from the result of Braverman et al.~(FOCS 2013), which was proved in the coordinator model and can yield a lower bound of $Ω(kn/\log k)$ in the peer-to-peer model. To the best of our knowledge, our lower bounds are the first tight (non-trivial)lower bounds on communication complexity in the natural {\em peer-to-peer} multi-party setting. In addition to the above results for communication complexity, we also prove, using the same tools, an $Ω(n)$ lower bound on the number of random bits necessary for the (information theoretic) private computation of the $k$-player, $n$-bit Disjointness function .

In the past few years, successive improvements of the asymptotic complexity of square matrix multiplication have been obtained by developing novel methods to analyze the powers of the Coppersmith-Winograd tensor, a basic construction introduced thirty years ago. In this paper we show how to generalize this approach to make progress on the complexity of rectangular matrix multiplication as well, by developing a framework to analyze powers of tensors in an asymmetric way. By applying this methodology to the fourth power of the Coppersmith-Winograd tensor, we succeed in improving the complexity of rectangular matrix multiplication. Let α denote the maximum value such that the product of an n × nα matrix by an nα × n matrix can be computed with O(n2+∊) arithmetic operations for any ∊ > 0. By analyzing the fourth power of the Coppersmith-Winograd tensor using our methods, we obtain the new lower bound α > 0.31389, which improves the previous lower bound α > 0.30298 obtained by Le Gall (FOCS’12) from the analysis of the second power of the Coppersmith-Winograd tensor. More generally, we give faster algorithms computing the product of an n × nk matrix by an nk × n matrix for any value k ≠ 1. (In the case k = 1, we recover the bounds recently obtained for square matrix multiplication). These improvements immediately lead to improvements in the complexity of a multitude of fundamental problems for which the bottleneck is rectangular matrix multiplication, such as computing the all-pair shortest paths in directed graphs with bounded weights.