1,720,963 research outputs found
Stability of the Levinson algorithm for Toeplitz-like Systems
No full textNumerical stability of the Levinson algorithm, generalized for Toeplitz-
like systems, is studied. Arguments based on the analytic results of an
error analysis for floating point arithmetic produce an upper bound on
the norm of the residual vector, which grows exponentially with respect
to the size of the problem. The base of such an exponential function
can be small for diagonally dominant Toeplitz-like matrices. Numerical
experiments show that, for these matrices, Gaussian elimination by row
and the Levinson algorithm have residuals of the same order of magnitude.
As expected, the empirical results point out that the theoretical bound is
too pessimistic
A framework for studying the regularizing properties of Krylov subspace methods
No full textKrylov subspace iterative methods have recently received considerable
attention as regularizing techniques for solving linear systemswith a coefficient
matrix of ill-determined rank and a right-hand side vector perturbed by noise.
For many of them little is known from this point of view. In this paper,
the regularizing properties of some methods of Krylov type (CGLS, GMRES,
QMR, CGS, BiCG, Bi-CGSTAB) are examined. CGLS, for which a theoretical
analysis is available, is taken as a reference method. Tools for measuring the
regularization efficiency and the consistency with the discrepancy principle are
introduced. An extensive experimentation validates the proposed measures for
the studied methods
Stopping rules for iterative methods in nonnegatively constrained deconvolution
No full textWe consider the two-dimensional discrete nonnegatively constrained deconvolution problem, whose goal is to reconstruct an object x^@? from its image b obtained through an optical system and affected by noise. When the large size of the problem prevents regularization through a direct method, iterative methods enjoying the semi-convergence property, coupled with suitable strategies for enforcing nonnegativity, are suggested. For these methods an accurate detection of the stopping index is essential. In this paper we analyze various stopping rules and, with the aid of a large experimentation, we test their effect on three different widely used iterative regularizing methods
Performance analysis of maximum likelihood methods for regularization problems with nonnegativity constraints
Full text linkIn many numerical applications, for instance in image deconvolu- tion, the nonnegativity of the computed solution is required. When a problem of deconvolution is formulated in a statistical frame, the recorded image is seen as the realization of a random process, where the nature of the noise is taken into account. This formulation leads to the maximization of a likelihood function which depends on the sta- tistical property assumed for the noise. In this paper we revisit, under this unifying statistical approach, some iterative methods coupled with suitable strategies for enforcing nonnegativity and other ones which in- stead naturally embed nonnegativity. For all these methods we carry out a comparative study taking into account several performance in- dicators. The reconstruction e?ciency, the computational cost, the consistency with the discrepancy principle (a common technique for guessing the best regularization parameter) and the sensitivity to this choice are compared in a simulated context, by means of an extensive experimentation on both 1D and 2D problems
A coupled model for the indegree and outdegree analysis of the web
No full textWe introduce a mixed model for web graph which simultaneously describes the inlink and outlink distributions, by taking into account the interconnection of the two processes. We derive the expression for the steady-state distribution of indegrees (outdegrees) among vertices with fixed outdegree (indegree) in terms of sums of Beta functions. The experimentation on subsets of
the real web shows that the proposed distributions well reproduce the behaviour of the observed data
An inner-outer regularizing method for ill-posed problems
No full textConjugate Gradient is widely used as a regularizing technique for solving linear systems with ill-conditioned coefficient matrix and right-hand side vector perturbed by noise. It enjoys a good convergence rate and computes quickly an iterate, say xkopt, which minimizes the error with respect to the exact solution. This behavior can be a disadvantage in the regulariza-tion context, because also the high-frequency components of the noise enter quickly the computed solution, leading to a difficult detection of kopt and to a sharp increase of the error after the koptth iteration. In this paper we propose an inner-outer algorithm based on a sequence of restarted Conjugate Gradients, with the aim of overcoming this drawback. A numerical experimentation validates the effectiveness of the proposed algorithm. ©2014 American Institute of Mathematical Sciences
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
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