Truncated Newton-Based Multigrid Algorithm for Centroidal Voronoi Diagram Calculation
In a variety of modern applications there arises a need to tessellate the domain into representative regions, called Voronoi cells. A particular type of such tessellations, called centroidal Voronoi tessellations or CVTs, are in big demand due to their ...
Z. Di, M. Emelianenko, S. Nash
semanticscholar +1 more source
The smallest singular value anomaly: The reasons behind sharp anomaly
Let AA be an arbitrary matrix in which the number of rows, mm, is considerably larger than the number of columns, nn. Let the submatrix Ai,i=1,…,m{A}_{i},\hspace{0.33em}i=1,\ldots ,m, be composed from the first ii rows of AA, and let βi{\beta }_{i ...
Dax Achiya
doaj +1 more source
A Numerical Approach to solve Three-Parameter Matrix Eigenvalue Problems by Kronecker Product Method
In this work it is intended to discuss three-parameter matrix eigenvalue problems and its numerical aspects. The problem is reduced into its corresponding one-parameter problems in tensor product space.
N. Bora, A. Baruah
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We draw a parallel between the properties of the spectrum Λ0(A) and the pseudospectrum Λǫ(A), for a square matrix A and ǫ > 0. This paper presents several definitions and properties of the pseudospectrum as a function of the given matrix, ǫ > 0, and the ...
M. Ahues, M. Hama
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Computing the smallest singular triplets of a large matrix
In this paper we present a new type of restarted Krylov methods for calculating the smallest singular triplets of a large sparse matrix, A. The new framework avoids the Lanczos bidiagonalization process and the use of polynomial filtering.
Achiya Dax
doaj
COVID-19 pandemic and lockdown: what has changed in common home accidents such as foreign bodies and corrosive injuries? [PDF]
Balcı Ö+8 more
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All-at-once multigrid approaches for one-dimensional space-fractional diffusion equations. [PDF]
Donatelli M+3 more
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An eigenvalue localization set for tensors and its applications. [PDF]
Zhao J, Sang C.
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An upper bound for the Z-spectral radius of adjacency tensors. [PDF]
Wu ZY, He J, Liu YM, Tian JK.
europepmc +1 more source
Computable upper error bounds for Krylov approximations to matrix exponentials and associated φ -functions. [PDF]
Jawecki T, Auzinger W, Koch O.
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