January 31, 2012 Time: 11:44am chapter1.tex

First Page		Document Content
Date: 2012-01-31 13:06:47 Mathematical physics Linear algebra Matrix theory Computational science Eigenvalues and eigenvectors Singular value decomposition Numerical analysis Computational fluid dynamics Mathematical model Mathematics Algebra Physics		January 31, 2012 Time: 11:44am chapter1.tex Add to Reading List Source URL: press.princeton.edu Download Document from Source Website File Size: 538,38 KB Share Document on Facebook

	Stat 991: Multivariate Analysis, Dimensionality Reduction, and Spectral Methods Lecture: 1 The Singular Value Decomposition Instructor: Sham Kakade DocID: 1vbS6 - View Document
	Sampling Algorithms to Update Truncated SVD Ichitaro Yamazaki, Stanimire Tomov, and Jack Dongarra University of Tennessee, Knoxville, Tennessee, U.S.A. Abstract— A truncated singular value decomposition (SVD) is a powe DocID: 1udU9 - View Document
	Orthogonal Matrices and the Singular Value Decomposition Carlo Tomasi The first Section below extends to m × n matrices the results on orthogonality and projection we have previously seen for vectors. The Sections there DocID: 1tr0F - View Document
	Using Singular Value Decomposition to Parameterize State-Dependent Model Errors Christopher M. Danforth∗ Department of Mathematics and Statistics, University of Vermont Burlington, VTEugenia Kalnay DocID: 1tjbW - View Document
	CS168: The Modern Algorithmic Toolbox Lecture #9: The Singular Value Decomposition (SVD) and Low-Rank Matrix Approximations Tim Roughgarden & Gregory Valiant∗ April 25, 2016 DocID: 1rHEk - View Document