1,720,961 research outputs found
Convergent Inexact Penalty Decomposition Methods for Cardinality-Constrained Problems
Full text linkIn this manuscript, we consider the problem of minimizing a smooth function with cardinality constraint, i.e., the constraint requiring that the [InlineEquation not available: see fulltext.]-norm of the vector of variables cannot exceed a given threshold value. A well-known approach of the literature is represented by the class of penalty decomposition methods, where a sequence of penalty subproblems, depending on the original variables and new variables, are inexactly solved by a two-block decomposition method. The inner iterates of the decomposition method require to perform exact minimizations with respect to the two blocks of variables. The computation of the global minimum with respect to the original variables may be prohibitive in the case of nonconvex objective function. In order to overcome this nontrivial issue, we propose a modified penalty decomposition method, where the exact minimizations with respect to the original variables are replaced by suitable line searches along gradient-related directions. We also present a derivative-free penalty decomposition algorithm for black-box optimization. We state convergence results of the proposed methods, and we report the results of preliminary computational experiments
A study on sequential minimal optimization methods for standard quadratic problems
No full textIn this work, we consider the relevant class of Standard Quadratic Programming problems and we propose a simple and quick decomposition algorithm, which sequentially updates, at each iteration, two variables chosen by a suitable selection rule. The main features of the algorithm are the following: (1) the two variables are updated by solving a subproblem that, although nonconvex, can be analytically solved; (2) the adopted selection rule guarantees convergence towards stationary points of the problem. Then, the proposed Sequential Minimal Optimization algorithm, which optimizes the smallest possible sub-problem at each step, can be used as efficient local solver within a global optimization strategy. We performed extensive computational experiments and the obtained results show that the proposed decomposition algorithm, equipped with a simple multi-start strategy, is a valuable alternative to the state-of-the-art algorithms for Standard Quadratic Optimization Problems
A Unifying Framework for Sparsity-Constrained Optimization
Full text linkIn this paper, we consider the optimization problem of minimizing a continuously differentiable function subject to both convex constraints and sparsity constraints. By exploiting a mixed-integer reformulation from the literature, we define a necessary optimality condition based on a tailored neighborhood that allows to take into account potential changes of the support set. We then propose an algorithmic framework to tackle the considered class of problems and prove its convergence to points satisfying the newly introduced concept of stationarity. We further show that, by suitably choosing the neighborhood, other well-known optimality conditions from the literature can be recovered at the limit points of the sequence produced by the algorithm. Finally, we analyze the computational impact of the neighborhood size within our framework and in the comparison with some state-of-the-art algorithms, namely, the Penalty Decomposition method and the Greedy Sparse-Simplex method. The algorithms have been tested using a benchmark related to sparse logistic regression problems
A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems
Full text linkIn this work we present a novel way to solve the sub-problems that originate when using decomposition algorithms to train Support Vector Machines (SVMs). State-of-the-art Sequential Minimization Optimization (SMO) solvers reduce the original problem to a sequence of sub-problems of two variables for which the solution is analytical. Although considering more than two variables at a time usually results in a lower number of iterations needed to train an SVM model, solving the sub-problem becomes much harder and the overall computational gains are limited, if any. We propose to apply the two-variables decomposition method to solve the sub-problems themselves and experimentally show that it is a viable and efficient way to deal with sub-problems of up to 50 variables. As a second contribution we explore different ways to select the working set and its size, combining first-order and second-order working set selection rules together with a strategy for exploiting cached elements of the Hessian matrix. An extensive numerical comparison shows that the method performs considerably better than state-of-the-art software
An Alternating Augmented Lagrangian method for constrained nonconvex optimization
No full textWe consider the problem of minimizing a smooth nonconvex function over a structured convex feasible set, that is, defined by two sets of constraints that are easy to treat when considered separately. In order to exploit the structure of the problem, we define an equivalent formulation by duplicating the variables and we consider the augmented Lagrangian of this latter formulation. Following the idea of the Alternating Direction Method of Multipliers (ADMM), we propose an algorithm where a two-blocks decomposition method is embedded within an augmented Lagrangian framework. The peculiarities of the proposed algorithm are the following: (1) the computation of the exact solution of a possibly nonconvex subproblem is not required; (2) the penalty parameter is iteratively updated once an approximated stationary point of the augmented Lagrangian is determined. Global convergence results are stated under mild assumptions and without requiring convexity of the objective function. Although the primary aim of the paper is theoretical, we perform numerical experiments on a nonconvex problem arising in machine learning, and the obtained results show the practical advantages of the proposed approach with respect to classical ADMM
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
