487 research outputs found
Ergodic Randomized Algorithms and Dynamics over Networks
Algorithms and dynamics over networks often involve randomization, and
randomization may result in oscillating dynamics which fail to converge in a
deterministic sense. In this paper, we observe this undesired feature in three
applications, in which the dynamics is the randomized asynchronous counterpart
of a well-behaved synchronous one. These three applications are network
localization, PageRank computation, and opinion dynamics. Motivated by their
formal similarity, we show the following general fact, under the assumptions of
independence across time and linearities of the updates: if the expected
dynamics is stable and converges to the same limit of the original synchronous
dynamics, then the oscillations are ergodic and the desired limit can be
locally recovered via time-averaging.Comment: 11 pages; submitted for publication. revised version with fixed
technical flaw and updated reference
Belief Consensus Algorithms for Fast Distributed Target Tracking in Wireless Sensor Networks
In distributed target tracking for wireless sensor networks, agreement on the
target state can be achieved by the construction and maintenance of a
communication path, in order to exchange information regarding local likelihood
functions. Such an approach lacks robustness to failures and is not easily
applicable to ad-hoc networks. To address this, several methods have been
proposed that allow agreement on the global likelihood through fully
distributed belief consensus (BC) algorithms, operating on local likelihoods in
distributed particle filtering (DPF). However, a unified comparison of the
convergence speed and communication cost has not been performed. In this paper,
we provide such a comparison and propose a novel BC algorithm based on belief
propagation (BP). According to our study, DPF based on metropolis belief
consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus
is the fastest in tree graphs. Moreover, we found that BC-based DPF methods
have lower communication overhead than data flooding when the network is
sufficiently sparse
Distributed Estimation with Information-Seeking Control in Agent Network
We introduce a distributed, cooperative framework and method for Bayesian
estimation and control in decentralized agent networks. Our framework combines
joint estimation of time-varying global and local states with
information-seeking control optimizing the behavior of the agents. It is suited
to nonlinear and non-Gaussian problems and, in particular, to location-aware
networks. For cooperative estimation, a combination of belief propagation
message passing and consensus is used. For cooperative control, the negative
posterior joint entropy of all states is maximized via a gradient ascent. The
estimation layer provides the control layer with probabilistic information in
the form of sample representations of probability distributions. Simulation
results demonstrate intelligent behavior of the agents and excellent estimation
performance for a simultaneous self-localization and target tracking problem.
In a cooperative localization scenario with only one anchor, mobile agents can
localize themselves after a short time with an accuracy that is higher than the
accuracy of the performed distance measurements.Comment: 17 pages, 10 figure
Distributed Learning from Interactions in Social Networks
We consider a network scenario in which agents can evaluate each other
according to a score graph that models some interactions. The goal is to design
a distributed protocol, run by the agents, that allows them to learn their
unknown state among a finite set of possible values. We propose a Bayesian
framework in which scores and states are associated to probabilistic events
with unknown parameters and hyperparameters, respectively. We show that each
agent can learn its state by means of a local Bayesian classifier and a
(centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter
that combines plain ML and Empirical Bayes approaches. By using tools from
graphical models, which allow us to gain insight on conditional dependencies of
scores and states, we provide a relaxed probabilistic model that ultimately
leads to a parameter-hyperparameter estimator amenable to distributed
computation. To highlight the appropriateness of the proposed relaxation, we
demonstrate the distributed estimators on a social interaction set-up for user
profiling.Comment: This submission is a shorter work (for conference publication) of a
more comprehensive paper, already submitted as arXiv:1706.04081 (under review
for journal publication). In this short submission only one social set-up is
considered and only one of the relaxed estimators is proposed. Moreover, the
exhaustive analysis, carried out in the longer manuscript, is completely
missing in this versio
Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms with Directed Gossip Communication
We study distributed optimization in networked systems, where nodes cooperate
to find the optimal quantity of common interest, x=x^\star. The objective
function of the corresponding optimization problem is the sum of private (known
only by a node,) convex, nodes' objectives and each node imposes a private
convex constraint on the allowed values of x. We solve this problem for generic
connected network topologies with asymmetric random link failures with a novel
distributed, decentralized algorithm. We refer to this algorithm as AL-G
(augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented
Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast
gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual
function. Dual variables are updated by the standard method of multipliers, at
a slow time scale. To update the primal variables, we propose a novel,
Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses
unidirectional gossip communication, only between immediate neighbors in the
network and is resilient to random link failures. For networks with reliable
communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian
broadcast gossiping) algorithm reduces communication, computation and data
storage cost. We prove convergence for all proposed algorithms and demonstrate
by simulations the effectiveness on two applications: l_1-regularized logistic
regression for classification and cooperative spectrum sensing for cognitive
radio networks.Comment: 28 pages, journal; revise
Symmetrization for Quantum Networks: a continuous-time approach
In this paper we propose a continuous-time, dissipative Markov dynamics that
asymptotically drives a network of n-dimensional quantum systems to the set of
states that are invariant under the action of the subsystem permutation group.
The Lindblad-type generator of the dynamics is built with two-body subsystem
swap operators, thus satisfying locality constraints, and preserve symmetric
observables. The potential use of the proposed generator in combination with
local control and measurement actions is illustrated with two applications: the
generation of a global pure state and the estimation of the network size.Comment: submitted to MTNS 201
- …