We study a distributed multi-agent optimization problem of minimizing the sum of convex objective functions. A new decentralized optimization algorithm is introduced, based on dual decomposition, together with the subgradient method for finding the optimal solution. The iterative algorithm is implemented on a multi-hop network and is designed to handle communication delays. The convergence of the algorithm is proved for communication networks with bounded delays. An explicit bound, which depends on the communication delays, on the convergence rate is given. A numerical comparison with a decentralized primal algorithm shows that the dual algorithm converges faster, and with less communication.

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We consider the problem of estimating the size of dynamic anonymous networks.The proposed algorithm exploits max-consensus protocols and extends a previous strategy suited for static networks. A regularization term accounts a-priori assumptions on the smoothness of the estimate, and we specifically consider quadratic regularization terms since they lead to closed-form solutions. We explicitly derive an estimation scheme tailored for peer-to-peer service networks, starting from their statistical model. Numerical experiments validate the accuracy of the algorithm and show how the strategy can be implemented using finite precision arithmetics.Problem Description dynamic network of N (t) agents each agent wants to estimate N (t) Constraints:can only use local information agents are anonymous (no global unique ID) Regularization Based Dynamic EstimationIdea: penalize implausible hypothesis ⇒ add a regularization term R to the naive approach (use a penalized log-likelihood function): Example: Peer-to-Peer Network Framework: peer-to-peer network with N max agents (either active or inactive) Assumption: birth-death process with transition probabilitiesEstimator for the 1-step memory case,

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An efficiency measure for road transportation networks with application to two case studies. An efficiency measure for road transportation networks with application to two case studies Håkan Terelius Karl Henrik JohanssonAbstract-Enabling efficient transportation is a major challenge for large cities, as the transportation need is increasing, while the environmental impact has to be minimized. In this paper, we define an efficiency measure that shows how much of the current transportation mileage that is really necessary to meet all the transportation assignments. We show that the efficiency measure can be computed efficiently as a minimum cost flow, and we apply it on two case studies. The first case demonstrate the efficiency measure on a freight transportation system, and the second case computes the measure for a large real-world data set from the New York City taxis.