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Books on the topic 'Singular decomposition'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Deift, Percy. The bidiagonal singular value decomposition and Hamiltonian mechanics. New York: Courant Institute of Mathematical Sciences, New York University, 1989.

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2

Kei, Takeuchi, Takane Yoshio, and SpringerLink (Online service), eds. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition. New York, NY: Springer Science+Business Media, LLC, 2011.

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3

Yanai, Haruo, Kei Takeuchi, and Yoshio Takane. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9887-3.

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4

Dhar, Pranab Kumar, and Tetsuya Shimamura. Advances in Audio Watermarking Based on Singular Value Decomposition. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14800-7.

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5

Gilsinn, David. Updating a turning center error model by singular value decomposition. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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6

Elsner, James B. Singular spectrum analysis: A new tool in time series analysis. New York: Plenum Press, 1996.

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7

Sen, sujit. Innovations and singular value decomposition for blind sequence detection in wireless channels. Ottawa: National Library of Canada, 1999.

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8

Torres, Rodolfo H. Boundedness results for operators with singular kernels on distribution spaces. Providence, R.I., USA: American Mathematical Society, 1991.

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9

Yao, Kung. Final report to NASA-Ames Research Center, Moffett Field, CA 94034, contract no. NAG 2-433, January 1, 1987 - March 31, 1988 on efficient load measurements using singular value decomposition. Los Angeles, CA: Laboratory for Flight Systems Research, University of California, 1989.

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10

Yao, Kung. Final report to NASA-Ames Research Center, Moffett Field, CA 94034, contract no. NAG 2-433, January 1, 1987 - March 31, 1988 on efficient load measurements using singular value decomposition. Los Angeles, CA: Laboratory for Flight Systems Research, University of California, 1989.

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11

Scroggs, Jeffrey S. A physically motivated domain decomposition for singularly perturbed equations. Hampton, Va: ICASE, 1988.

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12

Boglaev, Igor. Domain decomposition in boundary layers for a singularly perturbed parabolic problem. Palmerston North, N.Z: Faculty of Information and Mathematical Sciences, Massey University, 1997.

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13

Zuniga, Christian D. Singular Value Decomposition for Imaging Applications. SPIE, 2021. http://dx.doi.org/10.1117/3.2611523.

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14

Truncating the singular value decomposition for ill-posed problems. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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15

Sen, Sujit. Singular value decomposition techniques for multiuser detection receivers. 2004.

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16

Takeuchi, Kei, Haruo Yanai, and Yoshio Takane. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition. Springer, 2011.

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17

Dhar, Pranab Kumar, and Tetsuya Shimamura. Advances in Audio Watermarking Based on Singular Value Decomposition. Springer, 2015.

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18

(Editor), John E. Gilbert, Y. S. Han (Editor), J. A. Hogan (Editor), Joseph D. Lakey (Editor), D. Weiland (Editor), and G. Weiss (Editor), eds. Smooth Molecular Decompositions of Functions and Singular Integral Operators. American Mathematical Society, 2002.

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19

Golyandina, Nina, and Anatoly Zhigljavsky. Singular Spectrum Analysis for Time Series. Springer, 2013.

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20

Least Squares Solutions in Statistical Orbit Determination Using Singular Value Decomposition. Storming Media, 1999.

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21

National Institute of Standards and Technology (U.S.), ed. UPDATING A TURNING CENTER ERROR MODEL BY SINGULAR VALUE DECOMPOSITION... NISTIR 6722... U.S. DEPARTMENT OF COMMERCE. [S.l: s.n., 2001.

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22

Bouchaud, Jean-Philippe. Random matrix theory and (big) data analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0006.

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This chapter reviews methods from random matrix theory to extract information about a large signal matrix C (for example, a correlation matrix arising in big data problems), from its noisy observation matrix M. The chapter shows that the replica method can be used to obtain both the spectral density and the overlaps between noise-corrupted eigenvectors and the true ones, for both additive and multiplicative noise. This allows one to construct optimal rotationally invariant estimators of C based on the observation of M alone. This chapter also discusses the case of rectangular correlation matrices and the problem of random singular value decomposition.
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23

Zabrodin, Anton. Financial applications of random matrix theory: a short review. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.40.

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This article reviews some applications of random matrix theory (RMT) in the context of financial markets and econometric models, with emphasis on various theoretical results (for example, the Marčenko-Pastur spectrum and its various generalizations, random singular value decomposition, free matrices, largest eigenvalue statistics) as well as some concrete applications to portfolio optimization and out-of-sample risk estimation. The discussion begins with an overview of principal component analysis (PCA) of the correlation matrix, followed by an analysis of return statistics and portfolio theory. In particular, the article considers single asset returns, multivariate distribution of returns, risk and portfolio theory, and nonequal time correlations and more general rectangular correlation matrices. It also presents several RMT results on the bulk density of states that can be obtained using the concept of matrix freeness before concluding with a description of empirical correlation matrices of stock returns.
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24

Lattman, Eaton E., Thomas D. Grant, and Edward H. Snell. Shape Reconstructions from Small Angle Scattering Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199670871.003.0004.

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This chapter discusses recovering shape or structural information from SAXS data. Key to any such process is the ability to generate a calculated intensity from a model, and to compare this curve with the experimental one. Models for the particle scattering density can be approximated as pure homogenenous geometric shapes. More complex particle surfaces can be represented by spherical harmonics or by a set of close-packed beads. Sometimes structural information is known for components of a particle. Rigid body modeling attempts to rotate and translate structures relative to one another, such that the resulting scattering profile calculated from the model agrees with the experimental SAXS data. More advanced hybrid modelling procedures aim to incorporate as much structural information as is available, including modelling protein dynamics. Solutions may not always contain a homogeneous set of particles. A common case is the presence of two or more conformations of a single particle or a mixture of oligomeric species. The method of singular value decomposition can extract scattering for conformationally distinct species.
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25

A physically motivated domain decomposition for singularly perturbed equations. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1989.

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