Celebración de Rafael Pérez Estrada

Publicad

Exclusive Group Lasso for Structured Variable Selection

A structured variable selection problem is considered in which the covariates, divided into predefined groups, activate according to sparse patterns with few nonzero entries per group. Capitalizing on the concept of atomic norm, a composite norm can be properly designed to promote such exclusive group sparsity patterns. The resulting norm lends itself to efficient and flexible regularized optimization algorithms for support recovery, like the proximal algorithm. Moreover, an active set algorithm is proposed that builds the solution by successively including structure atoms into the estimated support. It is also shown that such an algorithm can be tailored to match more rigid structures than plain exclusive group sparsity. Asymptotic consistency analysis (with both the number of parameters as well as the number of groups growing with the observation size) establishes the effectiveness of the proposed solution in terms of signed support recovery under conventional assumptions. Finally, a set of numerical simulations further corroborates the results.Comment: 36 pages, 2 figures. Not submitted for publication. New licens

Universitat Barcelona & Lean Library. Take your library to your patrons

This webinar is the first in our series of dedicated webinars for Spanish institutions. We'll explore key findings from Lean Library's Librarian Futures report, including the role of the library in the digital age and how Lean Library can help bring librarian support and services directly to patrons, even when they begin their research journey outside of the library.The University of Barcelona who will showcase Lean Library in action and how it provides patrons with seamless access to content. We'll also explore why The University of Barcelona chose Lean Library, the challenges that this tool solves for both librarians and patrons, and effective strategies to encourage students to install the browser extension

On-line sampling methods for discovering association rules

Association rule discovery is one of the prototypical problems in data mining. In this problem, the input database is assumed to be very large and most of the algorithms are designed to minimize the number of scans of the database. Enumerating association rules is usually an expensive task due to the size of the input database. A proposed approach for reducing the running time of this process is random sampling. Of course, any implementation of an algorithm that uses sampling must solve the problem of determining which sample size is appropriate. Previous research of sampling for association rule mining has approached this problem concluding that, in general, the theoretically obtained sample size bounds are far from what is observed in practice. In this paper, we try to reduce this gap between theory and practice. We propose two on-line sampling algorithms for association rule mining. Our algorithms maintain the same theoretical guarantees of previous approaches while using a much smaller number of transactions in most of the cases. In the experiments we report, this improvement is often by an order of magnitude.Postprint (published version

Derivative-free optimization and filter methods to solve nonlinear constrained problems

In real optimization problems, usually the analytical expression of the objective function is not known, nor its derivatives, or they are complex. In these cases it becomes essential to use optimization methods where the calculation of the derivatives, or the verification of their existence, is not necessary: the Direct Search Methods or Derivative-free Methods are one solution. When the problem has constraints, penalty functions are often used. Unfortunately the choice of the penalty parameters is, frequently, very difficult, because most strategies for choosing it are heuristics strategies. As an alternative to penalty function appeared the filter methods. A filter algorithm introduces a function that aggregates the constrained violations and constructs a biobjective problem. In this problem the step is accepted if it either reduces the objective function or the constrained violation. This implies that the filter methods are less parameter dependent than a penalty function. In this work, we present a new direct search method, based on simplex methods, for general constrained optimization that combines the features of the simplex method and filter methods. This method does not compute or approximate any derivatives, penalty constants or Lagrange multipliers. The basic idea of simplex filter algorithm is to construct an initial simplex and use the simplex to drive the search. We illustrate the behavior of our algorithm through some examples. The proposed methods were implemented in Java

Derivative-free nonlinear optimization filter simplex

The filter method is a technique for solving nonlinear programming problems. The filter algorithm has two phases in each iteration. The first one reduces a measure of infeasibility, while in the second the objective function value is reduced. In real optimization problems, usually the objective function is not differentiable or its derivatives are unknown. In these cases it becomes essential to use optimization methods where the calculation of the derivatives or the verification of their existence is not necessary: direct search methods or derivative-free methods are examples of such techniques. In this work we present a new direct search method, based on simplex methods, for general constrained optimization that combines the features of simplex and filter methods. This method neither computes nor approximates derivatives, penalty constants or Lagrange multipliers

La Tumba de Keats

Juan Carlos Mestre, poeta y artista visual, nace el 15 de abril de 1957 en Villafranca de Bierzo (León). En 1982 publica su primer libro, Siete poemas escritos junto a la lluvia, al que seguirán, en 1983, La visita de Safo y Antífona del Otoño en el Valle del Bierzo, poemario con el que obtiene el Premio Adonais de poesía en 1985. En 1987, durante su estancia de varios años en Chile, publica Las páginas del fuego y, ya de regreso a España, La poesía ha caído en desgracia, libro por el que se le otorga en 1992 el Premio Jaime Gil de Biedma.Como artista visual ha expuesto su obra gráfica y pictórica en galerías de España, EE.UU., Francia, Suiza, Chile e Italia, así como editado numerosos libros de artista en colaboración con otros artistas y poetas como José María Parreño, Amancio Prada o Rafael Pérez Estrada.La tumba de Keats, su último libro, fue escrito durante su estancia en Italia como becario de la Academia de España en Roma. Con él obtuvo por unanimidad el Premio Jaén de Poesía 1999

El arca de los dones

El arca de los done