A refinement of the gauss-lucas theorem

The classical Gauss-Lucas Theorem states that all the critical points (zeros of the derivative) of a nonconstant polynomial p lie in the convex hull H of the zeros of p. It is proved that, actually, a subdomain of H contains the critical points of p. ©1998 American Mathematical Society.

Análise do método de Gauss como substituto dos principais sistemas de amortização no estudo da ocorrência de anatocismo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A relação cartográfica e geometria diferencial de Mercator a Gauss

Pós-graduação em Educação Matemática - IGCE

Thick braneworlds and the Gibbons-Kallosh-Linde no-go theorem in the Gauss-Bonnet framework

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Soluzioni classiche e viscose dell'equazione di curvatura di Gauss-Levi

Nella presente tesi ci siamo occupati dell'equazione di curvatura di Gauss-Levi, prima introducendo le nozioni necessarie alla sua definizione, poi cercandone soluzioni viscose. A tale scopo abbiamo introdotto in generale la nozione di soluzione viscosa per operatori ellittici degeneri, dimostrandone l'esistenza grazie al Principio del Confronto e al Metodo di Perron. Abbiamo infine riportato alcuni risultati che collegano le soluzioni viscose dell'equazione di curvatura, a quelle classiche.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

Hydrogen atom in space with a compactified extra dimension and potential defined by Gauss' law

We investigate the consequences of one extra spatial dimension for the stability and energy spectrum of the non-relativistic hydrogen atom with a potential defined by Gauss' law, i.e. proportional to 1 /| x | 2 . The additional spatial dimension is considered to be either infinite or curled-up in a circle of radius R. In both cases, the energy spectrum is bounded from below for charges smaller than the same critical value and unbounded from below otherwise. As a consequence of compactification, negative energy eigenstates appear: if R is smaller than a quarter of the Bohr radius, the corresponding Hamiltonian possesses an infinite number of bound states with minimal energy extending at least to the ground state of the hydrogen atom.