172 research outputs found
On resilient control of dynamical flow networks
Resilience has become a key aspect in the design of contemporary
infrastructure networks. This comes as a result of ever-increasing loads,
limited physical capacity, and fast-growing levels of interconnectedness and
complexity due to the recent technological advancements. The problem has
motivated a considerable amount of research within the last few years,
particularly focused on the dynamical aspects of network flows, complementing
more classical static network flow optimization approaches. In this tutorial
paper, a class of single-commodity first-order models of dynamical flow
networks is considered. A few results recently appeared in the literature and
dealing with stability and robustness of dynamical flow networks are gathered
and originally presented in a unified framework. In particular, (differential)
stability properties of monotone dynamical flow networks are treated in some
detail, and the notion of margin of resilience is introduced as a quantitative
measure of their robustness. While emphasizing methodological aspects --
including structural properties, such as monotonicity, that enable tractability
and scalability -- over the specific applications, connections to
well-established road traffic flow models are made.Comment: accepted for publication in Annual Reviews in Control, 201
Scaling limits for continuous opinion dynamics systems
Scaling limits are analyzed for stochastic continuous opinion dynamics
systems, also known as gossip models. In such models, agents update their
vector-valued opinion to a convex combination (possibly agent- and
opinion-dependent) of their current value and that of another observed agent.
It is shown that, in the limit of large agent population size, the empirical
opinion density concentrates, at an exponential probability rate, around the
solution of a probability-measure-valued ordinary differential equation
describing the system's mean-field dynamics. Properties of the associated
initial value problem are studied. The asymptotic behavior of the solution is
analyzed for bounded-confidence opinion dynamics, and in the presence of an
heterogeneous influential environment.Comment: Published at http://dx.doi.org/10.1214/10-AAP739 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Robustness of large-scale stochastic matrices to localized perturbations
Upper bounds are derived on the total variation distance between the
invariant distributions of two stochastic matrices differing on a subset W of
rows. Such bounds depend on three parameters: the mixing time and the minimal
expected hitting time on W for the Markov chain associated to one of the
matrices; and the escape time from W for the Markov chain associated to the
other matrix. These results, obtained through coupling techniques, prove
particularly useful in scenarios where W is a small subset of the state space,
even if the difference between the two matrices is not small in any norm.
Several applications to large-scale network problems are discussed, including
robustness of Google's PageRank algorithm, distributed averaging and consensus
algorithms, and interacting particle systems.Comment: 12 pages, 4 figure
From local averaging to emergent global behaviors: the fundamental role of network interconnections
Distributed averaging is one of the simplest and most studied network
dynamics. Its applications range from cooperative inference in sensor networks,
to robot formation, to opinion dynamics. A number of fundamental results and
examples scattered through the literature are gathered here and originally
presented, emphasizing the deep interplay between the network interconnection
structure and the emergent global behavior.Comment: 10 page
On stochastic imitation dynamics in large-scale networks
We consider a broad class of stochastic imitation dynamics over networks,
encompassing several well known learning models such as the replicator
dynamics. In the considered models, players have no global information about
the game structure: they only know their own current utility and the one of
neighbor players contacted through pairwise interactions in a network. In
response to this information, players update their state according to some
stochastic rules. For potential population games and complete interaction
networks, we prove convergence and long-lasting permanence close to the
evolutionary stable strategies of the game. These results refine and extend the
ones known for deterministic imitation dynamics as they account for new
emerging behaviors including meta-stability of the equilibria. Finally, we
discuss extensions of our results beyond the fully mixed case, studying
imitation dynamics where agents interact on complex communication networks.Comment: Extended version of conference paper accepted at ECC 201
On resilient control of dynamical flow networks
Resilience has become a key aspect in the design of contemporary infrastructure networks. This comes as a result of ever-increasing loads, limited physical capacity, and fast-growing levels of interconnectedness and complexity due to the recent technological advancements. The problem has motivated a considerable amount of research within the last few years, particularly focused on the dynamical aspects of network flows, complementing more classical static network flow optimization approaches.In this tutorial paper, a class of single-commodity first-order models of dynamical flow networks is considered. A few results recently appeared in the literature and dealing with stability and robustness of dynamical flow networks are gathered and originally presented in a unified framework. In particular, (differential) stability properties of monotone dynamical flow networks are treated in some detail, and the notion of margin of resilience is introduced as a quantitative measure of their robustness. While emphasizing methodological aspects -including structural properties, such as monotonicity, that enable tractability and scalability- over the specific applications, connections to well-established road traffic flow models are made
On imitation dynamics in potential population games
Imitation dynamics for population games are studied and their asymptotic
properties analyzed. In the considered class of imitation dynamics - that
encompass the replicator equation as well as other models previously considered
in evolutionary biology - players have no global information about the game
structure, and all they know is their own current utility and the one of fellow
players contacted through pairwise interactions. For potential population
games, global asymptotic stability of the set of Nash equilibria of the
sub-game restricted to the support of the initial population configuration is
proved. These results strengthen (from local to global asymptotic stability)
existing ones and generalize them to a broader class of dynamics. The developed
techniques highlight a certain structure of the problem and suggest possible
generalizations from the fully mixed population case to imitation dynamics
whereby agents interact on complex communication networks.Comment: 7 pages, 3 figures. Accepted at CDC 201
Convexity and Robustness of Dynamic Traffic Assignment and Freeway Network Control
We study the use of the System Optimum (SO) Dynamic Traffic Assignment (DTA)
problem to design optimal traffic flow controls for freeway networks as modeled
by the Cell Transmission Model, using variable speed limit, ramp metering, and
routing. We consider two optimal control problems: the DTA problem, where
turning ratios are part of the control inputs, and the Freeway Network Control
(FNC), where turning ratios are instead assigned exogenous parameters. It is
known that relaxation of the supply and demand constraints in the cell-based
formulations of the DTA problem results in a linear program. However, solutions
to the relaxed problem can be infeasible with respect to traffic dynamics.
Previous work has shown that such solutions can be made feasible by proper
choice of ramp metering and variable speed limit control for specific traffic
networks. We extend this procedure to arbitrary networks and provide insight
into the structure and robustness of the proposed optimal controllers. For a
network consisting only of ordinary, merge, and diverge junctions, where the
cells have linear demand functions and affine supply functions with identical
slopes, and the cost is the total traffic volume, we show, using the maximum
principle, that variable speed limits are not needed in order to achieve
optimality in the FNC problem, and ramp metering is sufficient. We also prove
bounds on perturbation of the controlled system trajectory in terms of
perturbations in initial traffic volume and exogenous inflows. These bounds,
which leverage monotonicity properties of the controlled trajectory, are shown
to be in close agreement with numerical simulation results
Robust Network Routing under Cascading Failures
We propose a dynamical model for cascading failures in single-commodity
network flows. In the proposed model, the network state consists of flows and
activation status of the links. Network dynamics is determined by a, possibly
state-dependent and adversarial, disturbance process that reduces flow capacity
on the links, and routing policies at the nodes that have access to the network
state, but are oblivious to the presence of disturbance. Under the proposed
dynamics, a link becomes irreversibly inactive either due to overload condition
on itself or on all of its immediate downstream links. The coupling between
link activation and flow dynamics implies that links to become inactive
successively are not necessarily adjacent to each other, and hence the pattern
of cascading failure under our model is qualitatively different than standard
cascade models. The magnitude of a disturbance process is defined as the sum of
cumulative capacity reductions across time and links of the network, and the
margin of resilience of the network is defined as the infimum over the
magnitude of all disturbance processes under which the links at the origin node
become inactive. We propose an algorithm to compute an upper bound on the
margin of resilience for the setting where the routing policy only has access
to information about the local state of the network. For the limiting case when
the routing policies update their action as fast as network dynamics, we
identify sufficient conditions on network parameters under which the upper
bound is tight under an appropriate routing policy. Our analysis relies on
making connections between network parameters and monotonicity in network state
evolution under proposed dynamics
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