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Eigenvalues and eigenvectors
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131	SIAM J. MATRIX ANAL. APPL. Vol. 27, No. 1, pp. 61–71 c 2005 Society for Industrial and Applied Mathematics Add to Reading List Source URL: users.cms.caltech.edu Language: English - Date: 2007-09-11 17:01:53 Matrices Matrix theory Linear algebra Mathematical physics Order theory Hermitian matrix Eigenvalues and eigenvectors Matrix Symmetric matrix SchurHorn theorem Tridiagonal matrix Orthogonal matrix
132	CS168: The Modern Algorithmic Toolbox Lecture #10: Tensors, and Low-Rank Tensor Recovery Tim Roughgarden & Gregory Valiant April 27, 2016 Last lecture discussed singular value decomposition (SVD), and we saw how such dec Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2016-06-04 09:49:43 Linear algebra Matrix theory Matrices Binary operations Numerical linear algebra Tensor Rank Higher-order singular value decomposition Singular value decomposition Matrix Outer product Eigenvalues and eigenvectors
133	DIAGNOSTIC IN-CLASS QUIZ: DUE WEDNESDAY OCTOBER 2: VECTORS (BASIC STUFF) MATH 196, SECTION 57 (VIPUL NAIK) Your name (print clearly in capital letters): PLEASE DO NOT DISCUSS ANY QUESTIONS. Add to Reading List Source URL: files.vipulnaik.com Language: English - Date: 2016-08-13 11:33:29 Linear algebra Vectors Abstract algebra Vector calculus Analytic geometry Euclidean vector Vector space Vector Scalar Basis Eigenvalues and eigenvectors Multivector
134	Spectral Graph Theory Lecture 15 Iterative solvers for linear equations Daniel A. Spielman Add to Reading List Source URL: www.cs.yale.edu Language: English - Date: 2012-08-24 09:50:05 Numerical linear algebra Matrix theory Linear algebra Eigenvalues and eigenvectors Singular value decomposition Modified Richardson iteration Conjugate gradient method Eigendecomposition of a matrix
135	CS168: The Modern Algorithmic Toolbox Lecture #7: Understanding and Using Principal Component Analysis (PCA) Tim Roughgarden & Gregory Valiant∗ April 18, 2016 Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2016-06-04 09:49:44 Multivariate statistics Dimension reduction Abstract algebra Linear algebra Analytic geometry Principal component analysis Dimensionality reduction Eigenvalues and eigenvectors Linear regression Euclidean vector Degrees of freedom Eigenface
136	Chapter 6 Constructs, Components, and Factor models Parsimony of description has been a goal of science since at least the famous dictum commonly attributed to William of Ockham to not multiply entities beyond necessity Add to Reading List Source URL: personality-project.org Language: English - Date: 2011-08-17 18:07:44 Covariance and correlation Factor analysis Multivariate statistics Regression analysis Linear algebra Principal component analysis Eigenvalues and eigenvectors Covariance matrix Matrix Exploratory factor analysis Singular value decomposition Multiple correlation
137	Martin Nilsson Jacobi, Chalmers Preliminary work in WP2 Two useful(?) examples maybe primarily for WP1.3: closure at different scales Add to Reading List Source URL: www.hieratic.eu Language: English Markov processes Matrix theory Graph theory Markov chain Eigenvalues and eigenvectors Matrix
138	Department of Mathematical Sciences QUALIFYING EXAMINATION FOR THE PH.D. IN MATHEMATICAL SCIENCES COMPREHENSIVE EXAMINATION FOR THE M.S. IN MATHEMATICAL SCIENCES These written examinations are given three times each year Add to Reading List Source URL: www.math.niu.edu Language: English - Date: 2016-07-11 16:50:34 Mathematics Mathematical analysis Computational mathematics Functional analysis Pure mathematics Differential equation Vector space Linear algebra Finite element method Eigenvalues and eigenvectors Calculus Book:Mathematics- An introduction
139	Searching for information hidden in multivariate data Mgr. Radoslav Harman, PhD. Department of Applied Mathematics and Statistics Faculty of Mathematics, Physics and Informatics Comenius University Bratislava Add to Reading List Source URL: www.iam.fmph.uniba.sk Language: English - Date: 2011-05-02 15:34:17 Multivariate statistics Probability distributions Covariance and correlation Stable distributions Algebra of random variables Principal component analysis Multivariate normal distribution Covariance matrix Multivariate random variable Eigenvalues and eigenvectors Vector Eigenface
140	A REDUCE package for the computation of several matrix normal forms Matt Rebbeck Konrad-Zuse-Zentrum f¨ ur Informationstechnik Berlin Takustra¨se 7 Add to Reading List Source URL: reduce-algebra.com Language: English - Date: 2008-12-30 11:47:04 Linear algebra Matrix theory Matrices Jordan normal form Matrix Frobenius normal form Triangular matrix Square matrix Eigenvalues and eigenvectors Determinant Trace Canonical form

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