413 research outputs found
K shortest paths in stochastic time-dependent networks
A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. In some particular cases, the shortest origin-destination path must nevertheless be chosen a priori, since time-adaptive choices are not allowed. Unfortunately, finding the a priori shortest path is NP-hard, while the best time-adaptive strategy can be found in polynomial time. In this paper, we propose a solution method for the a priori shortest path problem, and we show that it can be easily adapted to the ranking of the first K shortest paths. Moreover, we present a computational comparison of time-adaptive and a priori route choices, pointing out the effect of travel time and cost distributions. The reported results show that, under realistic distributions, our solution methods are effectiveShortest paths; K shortest paths; stochastic time-dependent networks; routing; directed hypergraphs
Bicriterion a priori route choice in stochastic time-dependent networks.
In recent years there has been a growing interest in using stochastic time-dependent (STD) networks as a modelling tool for a number of applications within such areas as transportation and telecommunications. It is known that an optimal routing policy does not necessarily correspond to a path, but rather to a time-adaptive strategy. In some applications, however, it makes good sense to require that the routing policy corresponds to a loopless path in the network, that is, the time-adaptive aspect disappears and a priori route choice is considered. In this paper we consider bicriterion a priori route choice in STD networks, i.e. the problem of finding the set of efficient paths. Both expectation and min-max criteria are considered and a solution method based on the two-phase approach is devised. Experimental results reveal that the full set of efficient solutions can be determined on rather large test instances, which is in contrast to previously reported results for the time-adaptive caseStochastic time-dependent networks; Bicriterion shortest path; A priori route choice; Two-phase method
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Asynchronous iterative solution for dominant eigenvectors with applications in performance modelling and PageRank
Imperial Users onl
Whether and Where to Code in the Wireless Relay Channel
The throughput benefits of random linear network codes have been studied
extensively for wirelined and wireless erasure networks. It is often assumed
that all nodes within a network perform coding operations. In
energy-constrained systems, however, coding subgraphs should be chosen to
control the number of coding nodes while maintaining throughput. In this paper,
we explore the strategic use of network coding in the wireless packet erasure
relay channel according to both throughput and energy metrics. In the relay
channel, a single source communicates to a single sink through the aid of a
half-duplex relay. The fluid flow model is used to describe the case where both
the source and the relay are coding, and Markov chain models are proposed to
describe packet evolution if only the source or only the relay is coding. In
addition to transmission energy, we take into account coding and reception
energies. We show that coding at the relay alone while operating in a rateless
fashion is neither throughput nor energy efficient. Given a set of system
parameters, our analysis determines the optimal amount of time the relay should
participate in the transmission, and where coding should be performed.Comment: 11 pages, 12 figures, to be published in the IEEE JSAC Special Issue
on Theories and Methods for Advanced Wireless Relay
Biased landscapes for random Constraint Satisfaction Problems
The typical complexity of Constraint Satisfaction Problems (CSPs) can be
investigated by means of random ensembles of instances. The latter exhibit many
threshold phenomena besides their satisfiability phase transition, in
particular a clustering or dynamic phase transition (related to the tree
reconstruction problem) at which their typical solutions shatter into
disconnected components. In this paper we study the evolution of this
phenomenon under a bias that breaks the uniformity among solutions of one CSP
instance, concentrating on the bicoloring of k-uniform random hypergraphs. We
show that for small k the clustering transition can be delayed in this way to
higher density of constraints, and that this strategy has a positive impact on
the performances of Simulated Annealing algorithms. We characterize the modest
gain that can be expected in the large k limit from the simple implementation
of the biasing idea studied here. This paper contains also a contribution of a
more methodological nature, made of a review and extension of the methods to
determine numerically the discontinuous dynamic transition threshold.Comment: 32 pages, 16 figure
Towards hypergraph cognitive networks as feature-rich models of knowledge
Semantic networks provide a useful tool to understand how related concepts
are retrieved from memory. However, most current network approaches use
pairwise links to represent memory recall patterns. Pairwise connections
neglect higher-order associations, i.e. relationships between more than two
concepts at a time. These higher-order interactions might covariate with (and
thus contain information about) how similar concepts are along psycholinguistic
dimensions like arousal, valence, familiarity, gender and others. We overcome
these limits by introducing feature-rich cognitive hypergraphs as quantitative
models of human memory where: (i) concepts recalled together can all engage in
hyperlinks involving also more than two concepts at once (cognitive hypergraph
aspect), and (ii) each concept is endowed with a vector of psycholinguistic
features (feature-rich aspect). We build hypergraphs from word association data
and use evaluation methods from machine learning features to predict concept
concreteness. Since concepts with similar concreteness tend to cluster together
in human memory, we expect to be able to leverage this structure. Using word
association data from the Small World of Words dataset, we compared a pairwise
network and a hypergraph with N=3586 concepts/nodes. Interpretable artificial
intelligence models trained on (1) psycholinguistic features only, (2)
pairwise-based feature aggregations, and on (3) hypergraph-based aggregations
show significant differences between pairwise and hypergraph links.
Specifically, our results show that higher-order and feature-rich hypergraph
models contain richer information than pairwise networks leading to improved
prediction of word concreteness. The relation with previous studies about
conceptual clustering and compartmentalisation in associative knowledge and
human memory are discussed
Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs
Interactive partially observable Markov decision processes (I-POMDP) provide
a formal framework for planning for a self-interested agent in multiagent
settings. An agent operating in a multiagent environment must deliberate about
the actions that other agents may take and the effect these actions have on the
environment and the rewards it receives. Traditional I-POMDPs model this
dependence on the actions of other agents using joint action and model spaces.
Therefore, the solution complexity grows exponentially with the number of
agents thereby complicating scalability. In this paper, we model and extend
anonymity and context-specific independence -- problem structures often present
in agent populations -- for computational gain. We empirically demonstrate the
efficiency from exploiting these problem structures by solving a new multiagent
problem involving more than 1,000 agents.Comment: 8 page article plus two page appendix containing proofs in
Proceedings of 25th International Conference on Autonomous Planning and
Scheduling, 201
A distributed control strategy for reactive power compensation in smart microgrids
We consider the problem of optimal reactive power compensation for the
minimization of power distribution losses in a smart microgrid. We first
propose an approximate model for the power distribution network, which allows
us to cast the problem into the class of convex quadratic, linearly
constrained, optimization problems. We then consider the specific problem of
commanding the microgenerators connected to the microgrid, in order to achieve
the optimal injection of reactive power. For this task, we design a randomized,
gossip-like optimization algorithm. We show how a distributed approach is
possible, where microgenerators need to have only a partial knowledge of the
problem parameters and of the state, and can perform only local measurements.
For the proposed algorithm, we provide conditions for convergence together with
an analytic characterization of the convergence speed. The analysis shows that,
in radial networks, the best performance can be achieved when we command
cooperation among units that are neighbors in the electric topology. Numerical
simulations are included to validate the proposed model and to confirm the
analytic results about the performance of the proposed algorithm
- …