A decomposition procedure based on approximate newton directions

The efficient solution of large-scale linear and nonlinear optimization problems may require exploiting any special structure in them in an efficient manner. We describe and analyze some cases in which this special structure can be used with very little cost to obtain search directions from decomposed subproblems. We also study how to correct these directions using (decomposable) preconditioned conjugate gradient methods to ensure local convergence in all cases. The choice of appropriate preconditioners results in a natural manner from the structure in the problem. Finally, we conduct computational experiments to compare the resulting procedures with direct methods, as well as to study the impact of different preconditioner choices

On the relationship between bilevel decomposition algorithms and direct interior-point methods

Engineers have been using bilevel decomposition algorithms to solve certain nonconvex large-scale optimization problems arising in engineering design projects. These algorithms transform the large-scale problem into a bilevel program with one upperlevel problem (the master problem) and several lower-level problems (the subproblems). Unfortunately, there is analytical and numerical evidence that some of these commonly used bilevel decomposition algorithms may fail to converge even when the starting point is very close to the minimizer. In this paper, we establish a relationship between a particular bilevel decomposition algorithm, which only performs one iteration of an interior-point method when solving the subproblems, and a direct interior-point method, which solves the problem in its original (integrated) form. Using this relationship, we formally prove that the bilevel decomposition algorithm converges locally at a superlinear rate. The relevance of our analysis is that it bridges the gap between the incipient local convergence theory of bilevel decomposition algorithms and the mature theory of direct interior-point methods

Portfolio selection with proportional transaction costs and predictability

We consider the portfolio optimization problem for a multiperiod investor who seeks to maximize her utility of consumption facing multiple risky assets and proportional transaction costs in the presence of return predictability. Due to the curse of dimensionality, this problem is very difficult to solve even numerically. In this paper, we propose several feasible policies that are based on optimizing quadratic programs. These proposed feasible policies can be easily computed even for many risky assets. We show how to compute upper bounds and use them to study how the losses associated with using the approximate policies depend on different problem parameters.Acknowledgements: the authors acknowledge financial support from the Spanish Government Project MTM2013-4490

Robust and sparse estimation of high-dimensional precision matrices via bivariate outlier detection

Robust estimation of Gaussian Graphical models in the high-dimensional setting is becoming increasingly important since large and real data may contain outlying observations. These outliers can lead to drastically wrong inference on the intrinsic graph structure. Several procedures apply univariate transformations to make the data Gaussian distributed. However, these transformations do not work well under the presence of structural bivariate outliers. We propose a robust precision matrix estimator under the cellwise contamination mechanism that is robust against structural bivariate outliers. This estimator exploits robust pairwise weighted correlation coefficient estimates, where the weights are computed by the Mahalanobis distance with respect to an affine equivariant robust correlation coefficient estimator. We show that the convergence rate of the proposed estimator is the same as the correlation coefficient used to compute the Mahalanobis distance. We conduct numerical simulation under different contamination settings to compare the graph recovery performance of different robust estimators. Finally, the proposed method is then applied to the classification of tumors using gene expression data. We show that our procedure can effectively recover the true graph under cellwise data contamination.Acknowledgements: the authors acknowledge financial support from the Spanish Ministry of Education and Science, research project MTM2013-44902-P

Retail competition with switching consumers in electricity markets

The ongoing transformations of power systems worldwide pose important challenges,both economic and technical, for their appropriate planning and operation. A key approach to improve the efficiency of these systems is through demand-side management, i.e., to promote the active involvement of consumers in the system. In particular, the current trend it to conceive systems where electricity consumers can vary their load according to real-time price incentives, offered by retailing companies.Under this setting, retail competition plays an important role as inadequate prices orservices may entail consumers switching to a rival retailer. In this work we consider a game theoretical model where asymmetric retailers compete in prices to increase their profits by accounting for the utility function of consumers. Consumer preferences for retailers are uncertain and distributed within a Hotelling line. We analytically characterize the equilibrium of a retailer duopoly, establishing its existence and uniqueness conditions. Furthermore, sensitivities of the equilibrium prices with respect to relevant model parameters are also provided. The duopoly model is extended to a multiple retailer case for which we perform an empirical analysis via numerical simulations. Results indicate that, depending on the retailer costs, loyalty rewards and initial market shares, the resulting equilibrium can range from complete competition to one in which a retailer have a leading or even a dominant position in the market, decreasing the consumers' utility significantly. Moreover, the retailer network configuration also plays an important role in the competitiveness of the systemAcknowledgements: The authors gratefully acknowledge financial support from the Spanish government through project MTM2013-

Ranking Edges and Model Selection in High-Dimensional Graphs

In this article we present an approach to rank edges in a network modeled through a Gaussian Graphical Model. We obtain a path of precision matrices such that, in each step of the procedure, an edge is added. We also guarantee that the matrices along the path are symmetric and positive definite. To select the edges, we estimate the covariates that have the largest absolute correlation with a node conditional to the set of edges estimated in previous iterations. Simulation studies show that the procedure is able to detect true edges until the sparsity level of the population network is recovered. Moreover, it can add efficiently true edges in the first iterations avoiding to enter false ones. We show that the top-rank edges are associated with the largest partial correlated variables. Finally, we compare the graph recovery performance with that of Glasso under different settings.The research of Ginette Lafit and Francisco J. Nogales is supported by the Spanish Government through project MTM2013-44902-

Comparing univariate and multivariate models to forecast portfolio Value-at-Risk

This article compares multivariate and univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to forecast portfolio value-at-risk (VaR). We provide a comprehensive look at the problem by considering realistic models and diversified portfolios containing a large number of assets, using both simulated and real data. Moreover, we rank the models by implementing statistical tests of comparative predictive ability. We conclude that multivariate models ou tperform their univariate counterparts on an out-of-sample basis. In particular, among the models considered in this article, the dynamic conditional correlation model with Student's t errors seems to be the most appropriate specification when implemented to estimate the VaR of the real portfolios analyzedA. A. P. S. acknowledges financial support from research Projects CNPq Universal 481719/2011-3 and UFSC-Funpesquisa 2010/2011 from the Brazilian Government. F. J. N. is supported by the Spanish Government through Project MTM2010-16519. E. R. is supported by the Spanish Government ECO2009-0810

Hierarchical clustering for smart meter electricity loads based on quantile autocovariances

In order to improve the efficiency and sustainability of electricity systems, most countries worldwide are deploying advanced metering infrastructures, and in particular household smart meters, in the residential sector. This technology is able to record electricity load time series at a very high frequency rates, information that can be exploited to develop new clustering models to group individual households by similar consumptions patterns. To this end, in this work we propose three hierarchical clustering methodologies that allow capturing different characteristics of the time series. These are based on a set of “dissimilarity” measures computed over different features: quantile auto-covariances, and simple and partial autocorrelations. The main advantage is that they allow summarizing each time series in a few representative features so that they are computationally efficient, robust against outliers, easy to automatize, and scalable to hundreds of thousands of smart meters series. We evaluate the performance of each clustering model in a real-world smart meter dataset with thousands of half-hourly time series. The results show how the obtained clusters identify relevant consumption behaviors of households and capture part of their geo-demographic segmentation. Moreover, we apply a supervised classification procedure to explore which features are more relevant to define each cluster.This work was supported in part by the Spanish Government through Project under Grant MTM2017-88979-P, and in part by the Fundación Iberdrola through “Ayudas a la Investigación en Energía y Medio Ambiente 2018.” The work of Andrés M. Alonso was supported in part by the Spanish Government through Project under Grant ECO2015-66593-P. Paper no. TSG-01702-2019

EducaFarma 4.0

Memoria ID-0264. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2015-2016

El fenómeno del dopaje desde la perspectiva de las Ciencias Sociales Odile

En este libro se recoge una selección de las comunicaciones presentadas en el IV Congreso Internacional ‘Deporte, Dopaje y Sociedad’ que se celebró en Madrid del 26 de febrero al 1 de marzo de 2014 y que fue organizado conjuntamente por la Universidad Politécnica de Madrid y la Agencia Española de Protección de la Salud en el Deporte. Los textos están escritos en español, francés e inglés y abordan el estudio del fenómeno del dopaje desde el ámbito especifico de las Ciencias Humanas y Sociales a través de disciplinas como Historia, Derecho, Sociología, Psicología, Economía, Ciencias de la Información y otras disciplinas relacionadas