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Libros sobre el tema "Subspaces methods"

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Publicado: 7 de julio de 2024

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1

Demmel, James Weldon. Three methods for refining estimates of invariant subspaces. New York: Courant Institute of Mathematical Sciences, New York University, 1985.

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2

Watkins, David S. The matrix eigenvalue problem: GR and Krylov subspace methods. Philadelphia: Society for Industrial and Applied Mathematics, 2007.

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3

Mats, Viberg y Stoica Petre 1949-, eds. Subspace methods. Amsterdam: Elsevier, 1996.

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4

Katayama, Tohru. Subspace methods for system identification. London: Springer, 2005.

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5

Katayama, Tohru. Subspace Methods for System Identification. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-158-x.

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6

Saad, Y. Krylov subspace methods on supercomputers. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1988.

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7

Sogabe, Tomohiro. Krylov Subspace Methods for Linear Systems. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8532-4.

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8

Heeger, David J. Subspace methods for recovering rigid motion. Toronto, Ont: University of Toronto, 1990.

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9

Jepson, Allan D. Linear subspace methods for recovering translational direction. Toronto: University of Toronto, Dept. of Computer Science, 1992.

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10

F, Chan Tony y Research Institute for Advanced Computer Science (U.S.), eds. Preserving symmetry in preconditioned Krylov subspace methods. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1996.

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11

F, Chan Tony y Research Institute for Advanced Computer Science (U.S.), eds. Preserving symmetry in preconditioned Krylov subspace methods. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1996.

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12

F, Chan Tony y Research Institute for Advanced Computer Science (U.S.), eds. Preserving symmetry in preconditioned Krylov subspace methods. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1996.

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13

Chen, Yen-Wei y Lakhmi C. Jain, eds. Subspace Methods for Pattern Recognition in Intelligent Environment. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54851-2.

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14

Research Institute for Advanced Computer Science (U.S.), ed. Krylov subspace methods for complex non-Hermitian linear systems. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1991.

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15

Saad, Y. Overview of Krylov subspace methods with applications to control problems. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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16

Amini, S. Preconditioned Krylov subspace methods for boundary element solution of the Helmholtz equation. Salford: University of Salford Department of Mathematics and Computer Science, 1995.

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17

United States. National Aeronautics and Space Administration., ed. Subspace based signal analysis of partially polarized light reflected by plant canopies. [Washington, DC: National Aeronautics and Space Administration, 1996.

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18

Branch, Mary Ann. A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1995.

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19

Sidi, Avram. Application of vector-valued rational approximations to the matrix Eigenvalue problem and connections with Krylov subspace methods. [Washington, DC: National Aeronautics and Space Administration, 1992.

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20

United States. National Aeronautics and Space Administration., ed. Application of vector-valued rational approximations to the matrix Eigenvalue problem and connections with Krylov subspace methods. [Washington, DC: National Aeronautics and Space Administration, 1992.

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21

Katayama, Tohru. Subspace Methods for System Identification. Springer London, Limited, 2010.

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22

National Aeronautics and Space Administration (NASA) Staff. Krylov Subspace Methods on Supercomputers. Independently Published, 2018.

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23

Katayama, Tohru. Subspace Methods for System Identification. Springer London, Limited, 2006.

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24

Liesen, Jörg y Zdenek Strakos. Krylov Subspace Methods: Principles and Analysis. Oxford University Press, 2015.

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25

Krylov Subspace Methods Principles And Analysis. Oxford University Press, 2013.

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26

Liesen, Jan, Jörg Liesen y Zdenek Strakos. Krylov Subspace Methods: Principles and Analysis. Oxford University Press, 2012.

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27

Liesen, Jörg y Zdenek Strakos. Krylov Subspace Methods: Principles and Analysis. Oxford University Press, Incorporated, 2012.

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28

Lukas, Andre. The Oxford Linear Algebra for Scientists. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780198844914.001.0001.

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Abstract This book provides a introduction into linear algebra which covers the mathematical set-up as well as applications to science. After the introductory material on sets, functions, groups and fields, the basic features of vector spaces are developed, including linear independence, bases, dimension, vector subspaces and linear maps. Practical methods for calculating with dot, cross and triple products are introduced early on. The theory of linear maps and their relation to matrices is developed in detail, culminating in the rank theorem. Algorithmic methods bases on row reduction and determinants are discussed an applied to computing the rank and the inverse of matrices and to solve systems of linear equations. Eigenvalues and eigenvectors and the application to diagonalising linear maps, as well as scalar products and unitary linear maps are covered in detail. Advanced topics included are the Jordon normal form, normal linear maps, the singular value decomposition, bi-linear and sesqui-linear forms, duality and tensors. The book also included short expositions of diverse scientific applications of linear algebra, including to internet search, classical mechanics, graph theory, cryptography, coding theory, data compression, special relativity, quantum mechanics and quantum computing.
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29

Preserving symmetry in preconditioned Krylov subspace methods. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1996.

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30

Simoncini, Valeria. Krtlov Subspace Methods for Linear Systems - Tools. Princeton University Press, 2009.

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31

Jain, Lakhmi C. y Yen-Wei Chen. Subspace Methods for Pattern Recognition in Intelligent Environment. Springer, 2014.

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32

Jain, Lakhmi C. y Yen-Wei Chen. Subspace Methods for Pattern Recognition in Intelligent Environment. Springer London, Limited, 2014.

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33

Jain, Lakhmi C. y Yen-Wei Chen. Subspace Methods for Pattern Recognition in Intelligent Environment. Springer, 2016.

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34

Ramakrishnan, S., ed. Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods. InTech, 2016. http://dx.doi.org/10.5772/61471.

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35

Sogabe, Tomohiro. Krylov Subspace Methods for Linear Systems: Principles of Algorithms. Springer, 2023.

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36

Farahbakhsh, Iman. Krylov Subspace Methods with Application in Incompressible Fluid Flow Solvers. Wiley & Sons, Limited, John, 2020.

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37

Farahbakhsh, Iman. Krylov Subspace Methods with Application in Incompressible Fluid Flow Solvers. Wiley & Sons, Limited, John, 2020.

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38

Farahbakhsh, Iman. Krylov Subspace Methods with Application in Incompressible Fluid Flow Solvers. Wiley & Sons, Incorporated, John, 2020.

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39

Farahbakhsh, Iman. Krylov Subspace Methods with Application in Incompressible Fluid Flow Solvers. Wiley & Sons, Incorporated, John, 2020.

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40

Application of vector-valued rational approximations to the matrix Eigenvalue problem and connections with Krylov subspace methods. [Washington, DC: National Aeronautics and Space Administration, 1992.

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41

Application of vector-valued rational approximations to the matrix Eigenvalue problem and connections with Krylov subspace methods. [Washington, DC: National Aeronautics and Space Administration, 1992.

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42

Starr, Jason, Brendan Hassett, Ravi Vakil y James McKernan. A Celebration of Algebraic Geometry (Clay Mathematics Proceedings). American Mathematical Society, 2013.

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