77,227 research outputs found
Accurate computation of singular values and eigenvalues of symmetric matrices
We give the review of recent results in relative perturbation theory
for eigenvalue and singular value problems and highly accurate
algorithms which compute eigenvalues and singular values to the highest possible relative accuracy
Physical behavior of eigenvalues and singular values in matrix decompositions
An apposite as well as realistic treatment of eigenvalue and singular value problems are potentially of interest to a wide variety of people, including among others, design engineers, theoretical physicists, classical applied mathematics and numerical analysis who yearn to carry out research in the matrix field. In real world, it is extensively used but at sometimes scantily understood. This paper focuses on building a solid perception of eigenvalue as well as singular value with their substantial meanings. The main goals of this paper are to present an intuitive experience of both eigenvalue and singular value in matrix decompositions throughout a discussion by largely building on ideas from linear algebra and will be proficiently to gain a better perceptive of their physical meanings from graphical representation
A Self-learning Algebraic Multigrid Method for Extremal Singular Triplets and Eigenpairs
A self-learning algebraic multigrid method for dominant and minimal singular
triplets and eigenpairs is described. The method consists of two multilevel
phases. In the first, multiplicative phase (setup phase), tentative singular
triplets are calculated along with a multigrid hierarchy of interpolation
operators that approximately fit the tentative singular vectors in a collective
and self-learning manner, using multiplicative update formulas. In the second,
additive phase (solve phase), the tentative singular triplets are improved up
to the desired accuracy by using an additive correction scheme with fixed
interpolation operators, combined with a Ritz update. A suitable generalization
of the singular value decomposition is formulated that applies to the coarse
levels of the multilevel cycles. The proposed algorithm combines and extends
two existing multigrid approaches for symmetric positive definite eigenvalue
problems to the case of dominant and minimal singular triplets. Numerical tests
on model problems from different areas show that the algorithm converges to
high accuracy in a modest number of iterations, and is flexible enough to deal
with a variety of problems due to its self-learning properties.Comment: 29 page
A note on the spectral analysis of matrix sequences via GLT momentary symbols: from all-at-once solution of parabolic problems to distributed fractional order matrices
The first focus of this paper is the characterization of the spectrum and the
singular values of the coefficient matrix stemming from the discretization with
space-time grid for a parabolic diffusion problem and from the approximation of
distributed order fractional equations. For this purpose we will use the
classical GLT theory and the new concept of GLT momentary symbols. The first
permits to describe the singular value or eigenvalue asymptotic distribution of
the sequence of the coefficient matrices, the latter permits to derive a
function, which describes the singular value or eigenvalue distribution of the
matrix of the sequence, even for small matrix-sizes but under given
assumptions. The note is concluded with a list of open problems, including the
use of our machinery in the study of iteration matrices, especially those
concerning multigrid-type techniques
A note on the spectral analysis of matrix sequences via GLT momentary symbols: from all-at-once solution of parabolic problems to distributed fractional order matrices
The first focus of this paper is the characterization of the spectrum and the singular values of the coefficient matrix stemming from the discretization of a parabolic diffusion problem using a space-time grid and secondly from the approximation of distributed-order fractional equations. For this purpose we use the classical GLT theory and the new concept of GLT momentary symbols. The first permits us to describe the singular value or eigenvalue asymptotic distribution of the sequence of the coefficient matrices. The latter permits us to derive a function that describes the singular value or eigenvalue distribution of the matrix of the sequence, even for small matrix sizes, but under given assumptions. The paper is concluded with a list of open problems, including the use of our machinery in the study of iteration matrices, especially those concerning multigrid-type techniques
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces
Reproducing kernel Hilbert spaces (RKHSs) play an important role in many
statistics and machine learning applications ranging from support vector
machines to Gaussian processes and kernel embeddings of distributions.
Operators acting on such spaces are, for instance, required to embed
conditional probability distributions in order to implement the kernel Bayes
rule and build sequential data models. It was recently shown that transfer
operators such as the Perron-Frobenius or Koopman operator can also be
approximated in a similar fashion using covariance and cross-covariance
operators and that eigenfunctions of these operators can be obtained by solving
associated matrix eigenvalue problems. The goal of this paper is to provide a
solid functional analytic foundation for the eigenvalue decomposition of RKHS
operators and to extend the approach to the singular value decomposition. The
results are illustrated with simple guiding examples
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