440 research outputs found
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
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