Communication complexity

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

On distributing symmetric streaming computations

Abstract: A common approach for dealing with large datasets is to stream over the input in one pass, and perform computations using sublinear resources. For truly massive datasets, however, even making a single pass over the data is prohibitive. Therefore, streaming computations must be distributed over many machines. In practice, obtaining significant speedups using distributed computation has numerous challenges including synchronization, load balancing, overcoming processor failures, and data distribution. Successful systems in practice such as Google's MapReduce and Apache's Hadoop address these problems by only allowing a certain class of highly distributable tasks defined by local computations that can be applied in any order to the input.The fundamental question that arises is: How does the class of computational tasks supported by these systems differ from the class for which streaming solutions existqWe introduce a simple algorithmic model for massive, unordered, distributed (mud) computation, as implemented by these systems. We show that in principle, mud algorithms are equivalent in power to symmetric streaming algorithms. More precisely, we show that any symmetric (order-invariant) function that can be computed by a streaming algorithm can also be computed by a mud algorithm, with comparable space and communication complexity. Our simulation uses Savitch's theorem and therefore has superpolynomial time complexity. We extend our simulation result to some natural classes of approximate and randomized streaming algorithms. We also give negative results, using communication complexity arguments to prove that extensions to private randomness, promise problems, and indeterminate functions are impossible. We also introduce an extension of the mud model to multiple keys and multiple rounds.

Faster communication in known topology radio networks

Abstract: This paper concerns the communication primitives of broadcasting (one-to-all communication) and gossiping (all-to-all communication) in known topology radio networks, i.e., where for each primitive the schedule of transmissions is precomputed in advance based on full knowledge about the size and the topology of the network. The first part of the paper examines the two communication primitives in arbitrary graphs. In particular, for the broadcast task we deliver two new results: a deterministic efficient algorithm for computing a radio schedule of length D + O(log3 n), and a randomized algorithm for computing a radio schedule of length D + O(log2 n). These results improve on the best currently known D + O(log4 n) time schedule due to Elkin and Kortsarz (Proceedings of the 16th ACM-SIAM Symposium on Discrete Algorithms, pp. 222–231, 2005). Later we propose a new (efficiently computable) deterministic schedule that uses 2D + Δlog n + O(log3 n) time units to complete the gossiping task in any radio network with size n, diameter D and max-degree Δ. Our new schedule improves and simplifies the currently best known gossiping schedule, requiring time $$O(D+\sqrt[{i+2}]{D}\Delta\log^{i+1} n)$$ , for any network with the diameter D = Ω(log i+4 n), where i is an arbitrary integer constant i ≥ 0, see Gąsieniec et al. (Proceedings of the 11th International Colloquium on Structural Information and Communication Complexity, vol. 3104, pp. 173–184, 2004). The second part of the paper focuses on radio communication in planar graphs, devising a new broadcasting schedule using fewer than 3D time slots. This result improves, for small values of D, on the currently best known D + O(log3 n) time schedule proposed by Elkin and Kortsarz (Proceedings of the 16th ACM-SIAM Symposium on Discrete Algorithms, pp. 222–231, 2005). Our new algorithm should be also seen as a separation result between planar and general graphs with small diameter due to the polylogarithmic inapproximability result for general graphs by Elkin and Kortsarz (Proceedings of the 7th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, vol. 3122, pp. 105–116, 2004; J. Algorithms 52(1), 8–25, 2004).

A Low-Complexity Design of Linear Precoding for MIMO Channels with Finite-Alphabet Inputs

Abstract: This paper investigates linear precoding scheme that maximizes mutual information for multiple-input multiple-output (MIMO) channels with finite-alphabet inputs. In contrast with recent studies, optimizing mutual information directly with extensive computational burden, this work proposes a low-complexity and high-performance design. It derives a lower bound that demands low computational effort and approximates, with a constant shift, the mutual information for various settings. Based on this bound, the precoding problem is solved efficiently. Numerical examples show the efficacy of this method for constant and fading MIMO channels. Compared to its conventional counterparts, the proposed method reduces the computational complexity without performance loss.

Limits and Applications of Group Algebras for Parameterized Problems

Abstract: The algebraic framework introduced in [Koutis, Proc. of the 35 th ICALP 2008] reduces several combinatorial problems in parameterized complexity to the problem of detecting multilinear degree-k monomials in polynomials presented as circuits. The best known (randomized) algorithm for this problem requires only O *(2 k ) time and oracle access to an arithmetic circuit, i.e. the ability to evaluate the circuit on elements from a suitable group algebra. This algorithm has been used to obtain the best known algorithms for several parameterized problems. In this paper we use communication complexity to show that the O *(2 k ) algorithm is essentially optimal within this evaluation oracle framework. On the positive side, we give new applications of the method: finding a copy of a given tree on k nodes, a spanning tree with at least k leaves, a minimum set of nodes that dominate at least t nodes, and an m -dimensional k -matching. In each case we achieve a faster algorithm than what was known. We also apply the algebraic method to problems in exact counting. Among other results, we show that a combination of dynamic programming and a variation of the algebraic method can break the trivial upper bounds for exact parameterized counting in fairly general settings.

Quantum and Classical Strong Direct Product Theorems and Optimal Time-Space Tradeoffs

Abstract: A strong direct product theorem says that if we want to compute $k$ independent instances of a function, using less than $k$ times the resources needed for one instance, then our overall success probability will be exponentially small in $k$. We establish such theorems for the classical as well as quantum query complexity of the OR-function. This implies slightly weaker direct product results for all total functions. We prove a similar result for quantum communication protocols computing $k$ instances of the disjointness function. Our direct product theorems imply a time-space tradeoff $T^2S=\Om{N^3}$ for sorting $N$ items on a quantum computer, which is optimal up to polylog factors. They also give several tight time-space and communication-space tradeoffs for the problems of Boolean matrix-vector multiplication and matrix multiplication.