Communication complexity

About: Communication complexity is a research topic. Over the lifetime, 3870 publications have been published within this topic receiving 105832 citations.

On the Hardness of Approximating the Network Coding Capacity

Abstract: This work addresses the computational complexity of achieving the capacity of a general network coding instance. It has been shown [Lehman and Lehman, SODA 2005] that determining the “scalar linear” capacity of a general network coding instance is NP-hard. In this paper we address the notion of approximation in the context of both linear and nonlinear network coding. Loosely speaking, we show that given an instance of the general network coding problem of capacity C , constructing a code of rate αC for any universal (i.e., independent of the size of the instance) constant α ≤ 1 is “hard”. Specifically, finding such network codes would solve a long standing open problem in the field of graph coloring. Our results refer to scalar linear, vector linear, and nonlinear encoding functions and are the first results that address the computational complexity of achieving the network coding capacity in both the vector linear and general network coding scenarios. In addition, we consider the problem of determining the (scalar) linear capacity of a planar network coding instance (i.e., an instance in which the underlying graph is planar). We show that even for planar networks this problem remains NP-hard.

Boolean Circuits, Tensor Ranks, and Communication Complexity

Abstract: We investigate two methods for proving lower bounds on the size of small-depth circuits, namely the approaches based on multiparty communication games and algebraic characterizations extending the concepts of the tensor rank and rigidity of matrices. Our methods are combinatorial, but we think that our main contribution concerns the algebraic concepts used in this area (tensor ranks and rigidity). Our main results are following. (i) An $o(n)$-bit protocol for a communication game for computing shifts, which also gives an upper bound of $o(n^2)$ on the contact rank of the tensor of multiplication of polynomials; this disproves some earlier conjectures. A related probabilistic construction gives an $o(n)$ upper bound for computing all permutations and an $O(n\log\log n)$ upper bound on the communication complexity of pointer jumping with permutations. (ii) A lower bound on certain restricted circuits of depth 2 which are related to the problem of proving a superlinear lower bound on the size of logarithmic-depth circuits; this bound has interpretations both as a lower bound on the rigidity of the tensor of multiplication of polynomials and as a lower bound on the communication needed to compute the shift function in a restricted model. (iii) An upper bound on Boolean circuits of depth 2 for computing shifts and, more generally, all permutations; this shows that such circuits are more efficient than the model based on sending bits along vertex-disjoint paths.

The Partition Bound for Classical Communication Complexity and Query Complexity

Abstract: We describe new lower bounds for randomized communication complexity and query complexity which we call the partition bounds. They are expressed as the optimum value of linear programs. For communication complexity we show that the partition bound is stronger than both the rectangle/corruption bound and the \gamma_2/generalized discrepancy bounds. In the model of query complexity we show that the partition bound is stronger than the approximate polynomial degree and classical adversary bounds. We also exhibit an example where the partition bound is quadratically larger than polynomial degree and classical adversary bounds.

Efficient Replication of Large Data Objects

Abstract: We present a new distributed data replication algorithm tailored especially for large-scale read/write data objects such as files. The algorithm guarantees atomic data consistency, while incurring low latency costs. The key idea of the algorithm is to maintain copies of the data objects separately from information about the locations of up-to-date copies. Because it performs most of its work using only the location information, our algorithm needs to access only a few copies of the actual data; specifically, only one copy during a read and only f+1 copies during a write, where f is an assumed upper bound on the number of copies that can fail. These bounds are optimal. The algorithm works in an asynchronous message-passing environment. It does not use additional mechanisms such as group communication or distributed locking. It is suitable for implementation in WANs as well as LANs. We also present two lower bounds on the costs of data replication. The first lower bound is on the number of low-level writes required during a read operation on the data. The second bound is on the minimum space complexity of a class of efficient replication algorithms. These lower bounds suggest that some of the techniques used in our algorithm are necessary. They are also of independent interest.

Communication Complexity: and Applications

Abstract: Communication complexity is the mathematical study of scenarios where several parties need to communicate to achieve a common goal, a situation that naturally appears during computation. This introduction presents the most recent developments in an accessible form, providing the language to unify several disjoint research subareas. Written as a guide for a graduate course on communication complexity, it will interest a broad audience in computer science, from advanced undergraduates to researchers in areas ranging from theory to algorithm design to distributed computing. The first part presents basic theory in a clear and illustrative way, offering beginners an entry into the field. The second part describes applications including circuit complexity, proof complexity, streaming algorithms, extension complexity of polytopes, and distributed computing. Proofs throughout the text use ideas from a wide range of mathematics, including geometry, algebra, and probability. Each chapter contains numerous examples, figures, and exercises to aid understanding.