1,370 research outputs found
Distributed Averaging via Lifted Markov Chains
Motivated by applications of distributed linear estimation, distributed
control and distributed optimization, we consider the question of designing
linear iterative algorithms for computing the average of numbers in a network.
Specifically, our interest is in designing such an algorithm with the fastest
rate of convergence given the topological constraints of the network. As the
main result of this paper, we design an algorithm with the fastest possible
rate of convergence using a non-reversible Markov chain on the given network
graph. We construct such a Markov chain by transforming the standard Markov
chain, which is obtained using the Metropolis-Hastings method. We call this
novel transformation pseudo-lifting. We apply our method to graphs with
geometry, or graphs with doubling dimension. Specifically, the convergence time
of our algorithm (equivalently, the mixing time of our Markov chain) is
proportional to the diameter of the network graph and hence optimal. As a
byproduct, our result provides the fastest mixing Markov chain given the
network topological constraints, and should naturally find their applications
in the context of distributed optimization, estimation and control
Location-Aided Fast Distributed Consensus in Wireless Networks
Existing works on distributed consensus explore linear iterations based on
reversible Markov chains, which contribute to the slow convergence of the
algorithms. It has been observed that by overcoming the diffusive behavior of
reversible chains, certain nonreversible chains lifted from reversible ones mix
substantially faster than the original chains. In this paper, we investigate
the idea of accelerating distributed consensus via lifting Markov chains, and
propose a class of Location-Aided Distributed Averaging (LADA) algorithms for
wireless networks, where nodes' coarse location information is used to
construct nonreversible chains that facilitate distributed computing and
cooperative processing. First, two general pseudo-algorithms are presented to
illustrate the notion of distributed averaging through chain-lifting. These
pseudo-algorithms are then respectively instantiated through one LADA algorithm
on grid networks, and one on general wireless networks. For a grid
network, the proposed LADA algorithm achieves an -averaging time of
. Based on this algorithm, in a wireless network with
transmission range , an -averaging time of
can be attained through a centralized algorithm.
Subsequently, we present a fully-distributed LADA algorithm for wireless
networks, which utilizes only the direction information of neighbors to
construct nonreversible chains. It is shown that this distributed LADA
algorithm achieves the same scaling law in averaging time as the centralized
scheme. Finally, we propose a cluster-based LADA (C-LADA) algorithm, which,
requiring no central coordination, provides the additional benefit of reduced
message complexity compared with the distributed LADA algorithm.Comment: 44 pages, 14 figures. Submitted to IEEE Transactions on Information
Theor
Accelerated Consensus via Min-Sum Splitting
We apply the Min-Sum message-passing protocol to solve the consensus problem
in distributed optimization. We show that while the ordinary Min-Sum algorithm
does not converge, a modified version of it known as Splitting yields
convergence to the problem solution. We prove that a proper choice of the
tuning parameters allows Min-Sum Splitting to yield subdiffusive accelerated
convergence rates, matching the rates obtained by shift-register methods. The
acceleration scheme embodied by Min-Sum Splitting for the consensus problem
bears similarities with lifted Markov chains techniques and with multi-step
first order methods in convex optimization
Simulation of quantum walks and fast mixing with classical processes
We compare discrete-time quantum walks on graphs to their natural classical equivalents, which we argue are lifted Markov chains (LMCs), that is, classical Markov chains with added memory. We show that LMCs can simulate the mixing behavior of any quantum walk, under a commonly satisfied invariance condition. This allows us to answer an open question on how the graph topology ultimately bounds a quantum walk's mixing performance, and that of any stochastic local evolution. The results highlight that speedups in mixing and transport phenomena are not necessarily diagnostic of quantum effects, although superdiffusive spreading is more prominent with quantum walks. The general simulating LMC construction may lead to large memory, yet we show that for the main graphs under study (i.e., lattices) this memory can be brought down to the same size employed in the quantum walks proposed in the literature
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Greedy Gossip with Eavesdropping
This paper presents greedy gossip with eavesdropping (GGE), a novel
randomized gossip algorithm for distributed computation of the average
consensus problem. In gossip algorithms, nodes in the network randomly
communicate with their neighbors and exchange information iteratively. The
algorithms are simple and decentralized, making them attractive for wireless
network applications. In general, gossip algorithms are robust to unreliable
wireless conditions and time varying network topologies. In this paper we
introduce GGE and demonstrate that greedy updates lead to rapid convergence. We
do not require nodes to have any location information. Instead, greedy updates
are made possible by exploiting the broadcast nature of wireless
communications. During the operation of GGE, when a node decides to gossip,
instead of choosing one of its neighbors at random, it makes a greedy
selection, choosing the node which has the value most different from its own.
In order to make this selection, nodes need to know their neighbors' values.
Therefore, we assume that all transmissions are wireless broadcasts and nodes
keep track of their neighbors' values by eavesdropping on their communications.
We show that the convergence of GGE is guaranteed for connected network
topologies. We also study the rates of convergence and illustrate, through
theoretical bounds and numerical simulations, that GGE consistently outperforms
randomized gossip and performs comparably to geographic gossip on
moderate-sized random geometric graph topologies.Comment: 25 pages, 7 figure
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