Topic
Communication complexity
About: Communication complexity is a research topic. Over the lifetime, 3870 publications have been published within this topic receiving 105832 citations.
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01 Jan 2012
TL;DR: This lecture discusses a beautiful application of Communication Complexity to come up with lower bounds in the area of property testing by introducing a new technique that seems to be able to say a lot.
Abstract: In this lecture we discuss a beautiful application of Communication Complexity to come up with lower bounds in the area of property testing. We discuss the work - Property Testing lower bounds via Communication Complexity ( [3], journal version) . This work provides testing lower bounds for many properties by introducing a new technique that seems to be able to say a lot. We begin by a brief introduction to Property Testing and then see how it relates to Communication Complexity at a high level. We then describe the general reduction technique that is employed throughout this lecture. We recollect some of the communication complexity problems and then give the reductions based on the general technique to get the testing lower bounds for a variety of properties.
33 citations
17 Aug 2020
TL;DR: In this article, the authors studied the communication complexity of unconditionally secure MPC with guaranteed output delivery over point-to-point channels for corruption threshold (t < n/2), assuming the existence of a public broadcast channel.
Abstract: We study the communication complexity of unconditionally secure MPC with guaranteed output delivery over point-to-point channels for corruption threshold \(t < n/2\), assuming the existence of a public broadcast channel. We ask the question: “is it possible to construct MPC in this setting s.t. the communication complexity per multiplication gate is linear in the number of parties?” While a number of works have focused on reducing the communication complexity in this setting, the answer to the above question has remained elusive until now. We also focus on the concrete communication complexity of evaluating each multiplication gate.
33 citations
TL;DR: The authors analyze the use of mobile agents for filtering distributed information resources and present an approach for coordinating mobile agent dissemination that minimizes communication costs.
Abstract: Reduced communication costs can motivate programmers to use mobile agent technology. The authors analyze the use of mobile agents for filtering distributed information resources and present an approach for coordinating mobile agent dissemination that minimizes communication costs.
33 citations
TL;DR: Two measures of communication complexity of dual decomposition methods are introduced and explored to identify the most efficient communication among these algorithms to demonstrate a tradeoff between fast convergence and primal feasibility.
Abstract: Dual decomposition methods are among the most prominent approaches for finding primal/dual saddle point solutions of resource allocation optimization problems. To deploy these methods in the emerging Internet of things networks, which will often have limited data rates, it is important to understand the communication overhead they require. Motivated by this, we introduce and explore two measures of communication complexity of dual decomposition methods to identify the most efficient communication among these algorithms. The first measure is $\epsilon$ -complexity, which quantifies the minimal number of bits needed to find an $\epsilon$ -accurate solution. The second measure is $b$ -complexity, which quantifies the best possible solution accuracy that can be achieved from communicating $b$ bits. We find the exact $\epsilon$ - and $b$ -complexity of a class of resource allocation problems where a single supplier allocates resources to multiple users. For both the primal and dual problems, the $\epsilon$ -complexity grows proportionally to $\log _2(1/\epsilon)$ and the $b$ -complexity proportionally to $1/2^b$ . We also introduce a variant of the $\epsilon$ - and $b$ -complexity measures where only algorithms that ensure primal feasibility of the iterates are allowed. Such algorithms are often desirable because overuse of the resources can overload the respective systems, e.g., by causing blackouts in power systems. We provide upper and lower bounds on the convergence rate of these primal feasible complexity measures. In particular, we show that the $b$ -complexity cannot converge at a faster rate than $\mathcal {O}(1/b)$ . Therefore, the results demonstrate a tradeoff between fast convergence and primal feasibility. We illustrate the result by numerical studies.
33 citations
TL;DR: A modified Dijkstra's algorithm and a precalculation technique to improve the throughput by allowing overlapped processing and the maximum-likelihood (ML) detection and a simple approximation of L2-norm calculation to reduce the computational complexity without degrading the error performance noticeably.
Abstract: The multiple-input-multiple-output (MIMO) technique is being actively adopted in recent wireless communication standards to enhance data rate. Though channel capacity is increased by adopting multiple spatial streams, the computational complexity needed to eliminate the interference hinders the implementation of a practical system. In this paper, we propose a modified Dijkstra's algorithm and a precalculation technique to improve the throughput by allowing overlapped processing. For the maximum-likelihood (ML) detection, in addition, we propose a simple approximation of L2-norm calculation to reduce the computational complexity without degrading the error performance noticeably. A MIMO symbol detector based on the proposed algorithm is implemented in a 0.18- μm CMOS process, targeting 4 × 4 16-QAM. It occupies about 0.5 mm2 with 25.1 K equivalent gates and shows a throughput of over 300 Mb/s.
33 citations