Area aggregation and time-scale modeling for sparse nonlinear networks.
ABSTRACT Model reduction and aggregation are of key importance for simulation and analysis of large-scale systems, such as molecular dynamics, large swarms of robotic vehicles, and animal aggregations. We study a nonlinear network which exhibits areas of internally dense and externally sparse interconnections. The densely connected nodes in these areas synchronize in the fast time-scale, and behave as aggregate nodes that dominate the slow dynamics of the network. We first derive a singular perturbation model which makes this time-scale separation explicit and, next, prove the validity of the reduced-model approximation on the infinite time interval.
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ABSTRACT: The PageRank algorithm employed at Google assigns a measure of importance to each web page for rankings in search results. In our recent papers, we have proposed a distributed randomized approach for this algorithm, where web pages are treated as agents computing their own PageRank by communicating with linked pages. This paper builds upon this approach to reduce the computation and communication loads for the algorithms. In particular, we develop a method to systematically aggregate the web pages into groups by exploiting the sparsity inherent in the web. For each group, an aggregated PageRank value is computed, which can then be distributed among the group members. We provide a distributed update scheme for the aggregated PageRank along with an analysis on its convergence properties. The method is especially motivated by results on singular perturbation techniques for large-scale Markov chains and multi-agent consensus.IEEE Transactions on Automatic Control 03/2012; · 2.72 Impact Factor
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ABSTRACT: We adjust the node and edge weightings of graphs using convex optimization to impose bounds on their Laplacian spectra. First, we derive necessary and sufficient conditions that characterize the feasibility of spectral bounds given positive node and edge weightings. Synthesizing these conditions leads naturally to algorithms that exploit convexity to achieve several eigenvalue bounds simultaneously. The algorithms we propose apply to many graph design problems as well as multi-agent systems control. Finally, we suggest efficient ways to accommodate larger graphs, and show that dual formulations lead to substantial improvement in the size of graphs that can be addressed.IEEE Transactions on Automatic Control 01/2012; 57(7):1872-1877. · 2.72 Impact Factor
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ABSTRACT: The small gain condition is sufficient for input-to-state stability (ISS) of interconnected systems. However, verification of the small gain condition requires large amount of computations in the case of a large size of the system. To facilitate this procedure we aggregate the subsystems and the gains between the subsystems that belong to certain interconnection patterns (motifs) using three heuristic rules. These rules are based on three motifs: sequentially connected nodes, nodes connected in parallel and almost disconnected subgraphs. Aggregation of these motifs keeps the main structure of the mutual influences between the subsystems in the network. Furthermore, fulfillment of the reduced small gain condition implies ISS of the large network. Thus such reduction allows to decrease the number of computations needed to verify the small gain condition. Finally, an ISS-Lyapunov function for the large network can be constructed using the reduced small gain condition. Applications of these rules is illustrated on an example.International Journal of Robust and Nonlinear Control 06/2012; · 1.90 Impact Factor