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Generalized Nash equilibria (GNE) represent extensions of the Nash solution concept when agents have shared strategy sets. This generalization is particularly relevant when agents compete in a networked setting. In this paper, we consider such a setting and focus on a congestion game in which agents contend with shared network constraints. We make two sets of contributions: (1) Under two types of...
The present paper considers distributed consensus algorithms for agents evolving on a connected compact homogeneous (CCH) manifold. The agents track no external reference and communicate their relative state according to an interconnection graph. The paper first formalizes the consensus problem for synchronization (i.e. maximizing the consensus) and balancing (i.e. minimizing the consensus); it thereby...
We consider a class of multiuser optimization problems in which user interactions are seen through congestion cost functions or coupling constraints. Our primary emphasis lies on the convergence and error analysis of distributed algorithms in which users communicate through aggregate user information. Traditional implementations are reliant on strong convexity assumptions, require coordination across...
We consider a distributed multi-agent network system where the goal is to minimize an objective function that can be written as the sum of component functions, each of which is known partially (with stochastic errors) to a specific network agent. We propose an asynchronous algorithm that is motivated by random gossip schemes where each agent has a local Poisson clock. At each tick of its local clock,...
This paper addresses the problem of distributed rate allocation for a class of multicast networks employing linear network coding. The goal is to minimize the cost (for example, the sum rates allocated to each link in the network) while satisfying a multicast rate requirement for each destination in the network. In essence, this paper aims to achieve network capacity while ensuring that the cost of...
The Google search engine employs the so-called PageRank algorithm for ranking the search results. This algorithm quantifies the importance of each Web page based on the link structure of the Web. In this paper, we continue our recent work on distributed randomized computation of PageRank, where the pages locally determine their values by communicating with linked pages. In particular, we propose a...
The observations gathered by the individual nodes of a sensor network may be unreliable due to malfunctioning, observation noise or low battery level. Global reliability is typically recovered by collecting all the measurements in a fusion center which takes proper decisions. However, centralized networks are more vulnerable and prone to congestion around the sink nodes. To relax the congestion problem,...
We design distributed and quantized average consensus algorithms on arbitrary connected networks. By construction, quantized algorithms cannot produce a real, analog average. Instead, our algorithm reaches consensus on the quantized interval that contains the average. We prove that this consensus in reached in finite time almost surely. As a by-product of this convergence result, we show that the...
Multi-source localization is an open and challenging research problem in the energy-based wireless sensor network (WSN) of acoustic sensors. Classic maximum likelihood (ML) algorithm can not work well due to the high computation demand. An expectation maximization (EM) algorithm was proposed in our previous paper to approximate the optimal solution with lower computational complexity. However that...
In this paper we show how interference can be exploited to perform gossip computations over a larger local neighborhood, rather than only pairs of nodes. We use a recently introduced technique called computation coding to perform reliable computation over noisy multiple access channels. Since many nodes can simultaneously average in a single round, neighborhood gossip clearly converges faster than...
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