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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2008.03.048 Byline: Cristiano Cervellera, Luca Caviglione Keywords: OR in telecommunications; Peer-to-peer systems; Mixed-integer nonlinear programming Abstract: This paper introduces a framework for the optimization of a peer-to-peer (p2p) based content replication system, aiming at actively exploiting the presence of a centralized component that represents a recent trend in content delivery architectures. To this purpose, we formalize a real-time mixed-integer nonlinear programming problem over a discrete time dynamic system, and propose a hybrid random/nonlinear programming scheme that allows to find good solutions while remaining computationally feasible. Two performance indexes, representing different objectives of the content replication process (e.g., speed vs. improved resistance against node failures), are discussed. Simulative tests are presented to prove the effectiveness of the proposed solution, with respect to typical strategies adopted by existing systems. Author Affiliation: Istituto di Studi sui Sistemi Intelligenti per l'Automazione, Consiglio Nazionale delle Ricerche Via de Marini 6, 16149 Genova, Italy Article History: Received 14 May 2007; Accepted 31 March 2008

In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find close approximations to ML estimator functions for the unknown parameters of any given density. The method relies on the choice of a proper neural network and on the deterministic generation of samples of observations of the likelihood function, thus avoiding the problem of generating samples with the unknown density. Under mild assumptions, consistency and convergence with favorable rates to the true ML estimator function can be proved. Simulation results are provided to show the good behavior of the algorithm compared to the corresponding exact solutions. Index Terms--Deterministic learning (DL), discrepancy, maximum-likelihood estimation (MLE), variation.

A methodology to design state estimators for a class of nonlinear continuous-time dynamic systems that is based on neural networks and nonlinear programming is proposed. The estimator has the structure of a Luenberger observer with a linear gain and a parameterized (in general, nonlinear) function, whose argument is an innovation term representing the difference between the current measurement and its prediction. The problem of the estimator design consists in finding the values of the gain and of the parameters that guarantee the asymptotic stability of the estimation error. Toward this end, if a neural network is used to take on this function, the parameters (i.e., the neural weights) are chosen, together with the gain, by constraining the derivative of a quadratic Lyapunov function for the estimation error to be negative definite on a given compact set. It is proved that it is sufficient to impose the negative definiteness of such a derivative only on a suitably dense grid of sampling points. The gain is determined by solving a Lyapunov equation. The neural weights are searched for via nonlinear programming by minimizing a cost penalizing grid-point constraints that are not satisfied. Techniques based on low-discrepancy sequences are applied to deal with a small number of sampling points, and, hence, to reduce the computational burden required to optimize the parameters. Numerical results are reported and comparisons with those obtained by the extended Kalman filter are made. Index Terms--Feedforward neural networks, Lyapunov function, offline optimization, penalty function, quasi-random sequences, state observer.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.01.022 Byline: Cristiano Cervellera (a), Victoria C.P. Chen (b), Aihong Wen (b) Keywords: Dynamic programming; Large-scale optimization; Applied probability; Neural networks; Natural resources Abstract: A numerical solution to a 30-dimensional water reservoir network optimization problem, based on stochastic dynamic programming, is presented. In such problems the amount of water to be released from each reservoir is chosen to minimize a nonlinear cost (or maximize benefit) function while satisfying proper constraints. Experimental results show how dimensionality issues, given by the large number of basins and realistic modeling of the stochastic inflows, can be mitigated by employing neural approximators for the value functions, and efficient discretizations of the state space, such as orthogonal arrays, Latin hypercube designs and low-discrepancy sequences. Author Affiliation: (a) Institute of Intelligent Systems for Automation, ISSIA-CNR National Research Council of Italy, Genova Branch, Via De Marini 6, Genova 16149, Italy (b) Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Campus Box 19017, Arlington, TX 76019-0017, United States

Stochastic dynamic programming models are developed, to solve 30-dimensional water reservoir network optimization problem.