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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

One the most attractive features of urban traffic network models is the possibility of running hypothetical scenarios to evaluate the impact of strategic and tactical decisions. In order to provide statistically meaningful results, the simulation runs should be able to capture the complex multivariate distributions characterizing the involved variables, and a key factor is the correct modeling of possible statistical dependence among the generated inputs used to define the desired scenarios. Here we introduce a data-driven method for scenario generation based on the statistical concept of copula models, through which the marginals of single input parameters can be chosen freely without altering the joint multivariate dependence structure of the inputs. This approach is particularly suited to running what-if scenarios, in which the marginal distributions of the inputs are changed, while retaining the general joint dependence scheme. The method exploits only a finite set of measures from the network and copes with arbitrary sets of input parameters without requiring any assumption on the kind of traffic model or the shape of the involved multivariate distributions. Simulation tests involving different scenarios show that the proposed method is able to capture complex multivariate distributions of the simulation outcomes and yield reliable inferences in what-if analyses, significantly better than in the case the joint dependence is ignored. • Statistically-sound what-if scenario generation for urban traffic models. • Data-driven generation through copula models trained on available data. • The method can be applied to any traffic model and arbitrary sets of input parameters. • Joint dependence of generated inputs is preserved, beyond simple correlation analysis. • Simulation results are provided for both a large-scale and a microscopic model. [ABSTRACT FROM AUTHOR]

Abstract: A new efficient technique for estimating probability densities from data through the application of the approximate global maximum likelihood (AGML) approach is proposed. It employs a composition of kernel functions to estimate the correct behavior of parameters involved in the expression of the unknown probability density. Convergence to the optimal solution is guaranteed by a deterministic learning framework when low discrepancy sequences are used to generate the centers of the kernels. Trials on mixture of Gaussians show that the proposed semi-local technique is able to efficiently approximate the maximum likelihood solution even in complex situations where implementations based on standard neural networks require an excessive computational cost. [Copyright &y& Elsevier]

• A method to learn a nonparametric model of a dataset through Voronoi binary trees. • Models built according to a distance measure to preserve the original distribution. • The Voronoi models can be used either for sampling or generation of new data. • Applicable to different tasks, like batch selection, imbalanced sets, reconstruction. • Simulation tests show the good performance of the method in various learning tasks. We propose a method based on recursive binary Voronoi trees to learn a nonparametric model of the distribution underlying a given dataset. The obtained model can be used as a general tool both to extract good samples from the original dataset (e.g., for batch selection, bagging, or sample size reduction) or to generate new synthetic ones, also in a conditional fashion (e.g., to deal with imbalanced sets or to reconstruct corrupted points). In order to ensure that the distribution of the new sets, either sampled or generated, follows closely that of the original dataset, we design all the procedures according to a specific measure of distance between distributions. The use of binary recursive Voronoi structures enables the proposed algorithms to be simple, efficient and able to adapt to the shape of the original dataset. Simulation tests showcase the good performance and flexibility of the approach in various learning contexts. [ABSTRACT FROM AUTHOR]