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We combine model-based methods and distributed stochastic approximation to propose a fully distributed algorithm for nonconvex optimization, with good empirical performance and convergence guarantees. Neither the expression of the objective nor its gradient are known. Instead, the objective is like a “black-box”, in which the agents input candidate solutions and evaluate the output. Without central...
We propose two distributed algorithms, one for solving the weight-balance problem and another for solving the bistochastic matrix formation problem, in a distributed system whose components (nodes) can exchange information via interconnection links (edges) that form an arbitrary, possibly directed, strongly connected communication topology (digraph). Both distributed algorithms achieve their goal...
In this paper, we propose a distributed algorithm for optimal routing in wireless multi-hop networks. We build our approach on a recently proposed model for stochastic routing, whereby each node selects a neighbor to forward a packet according to a given probability distribution. Our solution relies on dual decomposition techniques with regularization, that can significantly improve on the slow convergence...
We consider that a set of distributed agents desire to reach consensus on the average of their initial state values, while communicating with neighboring agents through a shared medium. This communication medium allows only one agent to transmit unidirectionally at a given time, which is true, e.g., in wireless networks. We address scenarios where the choice of agents that transmit and receive messages...
Network utility maximization provides an important method for network architecture and distributed algorithm design. This method is often used in previous work as a determining utility model, but in practice the utility model of users in networks is stochastic. In this paper, stochastic network utility maximization is transform into determining model by optimizing time averages, then it is easy to...
We propose distributed algorithms to automatically deploy a group of robotic agents and provide coverage of a discretized environment represented by a graph. The classic Lloyd approach to coverage optimization involves separate centering and partitioning steps and converges to the set of centroidal Voronoi partitions. In this work we present a novel graph coverage algorithm which achieves better performance...
This paper is concerned with the distributed averaging problem over a given undirected graph. To enable every vertex to compute the average of the initial numbers sitting on the vertices of the graph, the policy is to pick an edge at random and update the values on its ending vertices based on some rules, but only in terms of the quantized data being exchanged between them. Our recent paper showed...
The paper studies the effect of noise on the asymptotic properties of high dimensional consensus (HDC). HDC offers a unified framework to study a broad class of distributed algorithms with applications to average consensus, leader-follower dynamics in multi-agent networks and distributed sensor localization. We show that under a broad range of perturbations, including inter-sensor communication noise,...
This paper treats the problem of distributed planning in general-sum stochastic games with communication when the model is known. Our main contribution is a novel, game theoretic approach to the problem of distributed equilibrium computation and selection. We show theoretically and via experiments that our approach, when adopted by all agents, facilitates an efficient distributed equilibrium computation...
Stability and delay constraints have significant impact on the design and operation of wireless sensor networks. In this paper, we propose a closed architecture for data sampling in wireless sensor networks. Examples show that the proposed scheme outperforms the traditional layered scheme, both in terms of stable operating region as well as the end-to-end delays. We then propose a distributed routing...
The implementation of distributed network utility maximization (NUM) algorithms hinges heavily on information feedback through message passing among network elements. In practical systems the feedback is often obtained using error-prone measurement mechanisms and suffers from random errors. There has been little work in this direction, and by and large the impact of noisy feedback remains unclear...
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