Communication Memento: Memoryless Communication Complexity

We study the communication complexity of computing functions $F:\{0,1\}^n\times \{0,1\}^n \rightarrow \{0,1\}$ in the memoryless communication model. Here, Alice is given $x\in \{0,1\}^n$, Bob is given $y\in \{0,1\}^n$ and their goal is to compute F(x,y) subject to the following constraint: at every round, Alice receives a message from Bob and her reply to Bob solely depends on the message received and her input x; the same applies to Bob. The cost of computing F in this model is the maximum number of bits exchanged in any round between Alice and Bob (on the worst case input x,y). In this paper, we also consider variants of our memoryless model wherein one party is allowed to have memory, the parties are allowed to communicate quantum bits, only one player is allowed to send messages. We show that our memoryless communication model capture the garden-hose model of computation by Buhrman et al. (ITCS'13), space bounded communication complexity by Brody et al. (ITCS'13) and the overlay communication complexity by Papakonstantinou et al. (CCC'14). Thus the memoryless communication complexity model provides a unified framework to study space-bounded communication models. We establish the following: (1) We show that the memoryless communication complexity of F equals the logarithm of the size of the smallest bipartite branching program computing F (up to a factor 2); (2) We show that memoryless communication complexity equals garden-hose complexity; (3) We exhibit various exponential separations between these memoryless communication models. We end with an intriguing open question: can we find an explicit function F and universal constant c>1 for which the memoryless communication complexity is at least $c \log n$? Note that $c\geq 2+\varepsilon$ would imply a $\Omega(n^{2+\varepsilon})$ lower bound for general formula size, improving upon the best lower bound by Ne\v{c}iporuk in 1966.Comment: 30 Pages; several improvements to the presentation

Tradeoffs between communication and space

This paper initiates the study of communication complexity when the processors have limited work space. The following tradeoffs between number C of communications steps and space S are proved: (1) For multiplying two n×n matrices in the arithmetic model with two-way communication, CS = Θ(n3). (2) For convolution of two degree n polynomials in the arithmetic model with two-way communication, CS = Θ(n2). (3) For multiplying an n × n matrix by an n-vector in the Boolean model with one-way communication, CS = Θ(n2).link_to_subscribed_fulltex