196,055 research outputs found
A cubic regularization algorithm for unconstrained optimization using line search and nonmonotone techniques
No full textIn recent years, cubic regularization algorithms for unconstrained optimization have been defined as alternatives to trust-region and line search schemes. These regularization techniques are based on the strategy of computing an (approximate) global minimizer of a cubic overestimator of the objective function. In this work we focus on the adaptive regularization algorithm using cubics (ARC) proposed in Cartis et al. [Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results, Mathematical Programming A 127 (2011), pp. 245–295]. Our purpose is to design a modified version of ARC in order to improve the computational efficiency preserving global convergence properties. The basic idea is to suitably combine a Goldstein-type line search and a nonmonotone accepting criterion with the aim of advantageously exploiting the possible good descent properties of the trial step computed as (approximate) minimizer of the cubic model. Global convergence properties of the proposed nonmonotone ARC algorithm are proved. Numerical experiments are performed and the obtained results clearly show satisfactory performance of the new algorithm when compared to the basic ARC algorithm
Estimation issues in multivariate panel data
No full textatent variable models are a powerful tool in various research fields
when the constructs of interest are not directly observable. However, the likelihood-based model estimation can be problematic when dealing with many latent variables and/or random effects since the integrals involved in the likelihood function do not have analytical solutions. In the literature, several approaches have been proposed to
overcome this issue. Among them, the pairwise likelihood method and the dimension-wise quadrature have emerged as effective solutions that produce estimators with desirable properties. In this study, we compare a weighted version of the pairwise likelihood method with the dimension-wise quadrature for a latent variable model for binary longitudinal data by means of a simulation study
Poliendocrinopatie autoimmuni: esordio contemporaneo di diabete mellito di tipo 1 e morbo do Basedow: caso clinico
No full textOn the use of iterative methods in cubic regularization for unconstrained optimization
No full textIn this paper we consider the problem of minimizing a smooth function by using the adaptive cubic regularized (ARC) framework. We focus on the computation of the trial step as a suitable approximate minimizer of the cubic model and discuss the use of matrix-free iterative methods. Our approach is alternative to the implementation proposed in the original version of ARC, involving a linear algebra phase, but preserves the same worst-case complexity count. Further we introduce a new stopping criterion in order to properly manage the “over-solving” issue arising whenever the cubic model is not an adequate model of the true objective function. Numerical experiments conducted by using a nonmonotone gradient method as inexact solver are presented. The obtained results clearly show the effectiveness of the new variant of ARC algorithm
Comparison Between Different Estimation Methods of Factor Models for Longitudinal Ordinal Data
No full textLatent variable models represent a useful tool in different fields of research in which the constructs of interest are not directly observable. In presence of many latent variables/random effects, problems related to the integration of the likelihood function can arise since analytical solutions do not exist. In literature, different remedies have been proposed to overcome these problems. Among these, the composite likelihoods method and, more recently, the dimension-wise quadrature have been shown to produce estimators with desirable properties. We compare the performance of the two methods in the case of longitudinal ordinal data through a simulation study and an empirical application. Both the methods perform similarly, but the dimension-wise quadrature results less computational demanding. Indeed, for the specific model under investigation, it involves integrals of smaller dimensions than those involved in the computation of the pairwise likelihood, with a better performance than the latter in terms of accuracy of the estimates
Misurare la cultura della sicurezza: primo adattamento italiano del Safety Attitude Questionnaire (SAQ)
No full textApproximate likelihood inference via dimension reduction for multidimensional binary panel data
No full text- …
