Results 181 to 190 of about 3,994 (221)
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Adaptive detectors in the Krylov subspace
Science China Information Sciences, 2014The validity of the application of the Krylov subspace techniques in adaptive filtering and detection is investigated. A new verification of the equivalence of two well-known methods in the Krylov subspace, namely the multistage Wiener filters (MWF) and the auxiliary-vector filtering (AVF), is given in this paper.
Weijian Liu 0001 +4 more
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On Moment Methods in Krylov Subspaces
Doklady Mathematics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A Krylov Subspace Method for Information Retrieval
SIAM Journal on Matrix Analysis and Applications, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Katarina Blom, Axel Ruhe
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Projection Methods in Krylov Subspaces
Journal of Mathematical Sciences, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Spectral Variants of Krylov Subspace Methods
Numerical Algorithms, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Brígida Molina, Marcos Raydan
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Analysis of Augmented Krylov Subspace Methods
SIAM Journal on Matrix Analysis and Applications, 1997``Augmented Krylov methods'' are studied theoretically. These methods for solving a linear system are projection methods in which the subspace of projection is of the form \(K = K_{m} + W\), where \(K_{m}\) is the standard Krylov subspace, which is augmented by another subspace \(W\). The subspace \(W\) can be chosen in different ways.
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2020
In this chapter we consider the computation of bases for the Krylov subspace \(\mathcal{K}_k(A,r_0)\). We could have used another notation for the vector on which the Krylov subspace is constructed, but in the next chapters, the Krylov subspaces we have to deal with will be based on the initial residual vector \(r_0=b-Ax_0\).
Gérard Meurant, Jurjen Duintjer Tebbens
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In this chapter we consider the computation of bases for the Krylov subspace \(\mathcal{K}_k(A,r_0)\). We could have used another notation for the vector on which the Krylov subspace is constructed, but in the next chapters, the Krylov subspaces we have to deal with will be based on the initial residual vector \(r_0=b-Ax_0\).
Gérard Meurant, Jurjen Duintjer Tebbens
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On the Convergence of Restarted Krylov Subspace Methods
SIAM Journal on Matrix Analysis and Applications, 2000The paper is concerned with investigation of convergence of Krylov subspace iterative method for the solution of large nonsymmetric linear systems. Restarted methods terminate the process after a fixed number of iterations and then repeat the procedure using the residual of the current approximate solution as new initial vector.
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Application of Krylov subspaces to SPECT imaging
International Journal of Imaging Systems and Technology, 2002AbstractThe application of the conjugate gradient (CG) algorithm to the problem of data reconstruction in SPECT imaging indicates that most of the useful information is already contained in Krylov subspaces of small dimension, ranging from 9 (two‐dimensional case) to 15 (three‐dimensional case).
CALVINI P., BERTERO, MARIO
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Error-Minimizing Krylov Subspace Methods
SIAM Journal on Scientific Computing, 1994This paper first introduces generalized conjugate gradient methods which specialize to error minimizing procedures as well as to residual minimizing methods. General minimum error methods are then introduced, and the two method classes are compared.
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