PDF-Chapter Eigenvalues and Eigenvectors

1 Eigenvalues and the Characteristic Equation Given a matrix if 611 where is a scalar and is a nonzero vector is called an eigenvalue of and an eigenvector It is important here that an eigenvector should be a nonzero vector For the zero vector. Positive de64257nite matrices ar e even bet ter Symmetric matrices A symmetric matrix is one for which A T If a matrix has some special pr operty eg its a Markov matrix its eigenvalues and eigenvectors ar e likely to have special pr operties as we 41 No 1 pp 135147 The Discrete Cosine Transform Gilbert Strang Abstract Each discrete cosine transform DCT uses real basis vectors whose components are cosines In the DCT4 for example the th component of is cos These basis vectors are orthogonal a 1 Introduction to Eigenvalues Linear equations come from steady state problems Eigenvalues have their greatest importance in dynamic problems The solution of dt is changing with time growing or decaying or oscillating We cant 64257nd it by eliminat De64257nition 2 Computation and Properties 3 Chains brPage 3br Generalized Eigenvectors Math 240 De64257nition Computation and Properties Chains Motivation Defective matrices cannot be diagonalized because they do not possess enough eigenvectors to Consider the state space system with the A matrix given by a 65000 05000 65000 65000 05000 55000 55000 55000 05000 05000 05000 65000 05000 05000 55000 05000 For this example consider when B is b1 0 1 0 2 1 2 3 4 3 3 2 3 Check the controllability o Review of properties of vibration and buckling modes. What is nice about them?. Sensitivities of eigenvalues are really cheap! . Sensitivities of . eigevectors. . . Why bother getting them? . Think of where you want your car to have the least vibrations. (Non-Commuting). . Random Symmetric Matrices? :. . A "Quantum Information" Inspired Answer. . Alan Edelman. Ramis. . Movassagh. July 14, 2011. FOCM. Random Matrices. Example Result. p=1 .  classical probability. Dimensionality Reduction. Linear Methods. . 2.1 Introduction. Dimensionality reduction. the process of finding a suitable lower dimensional space in which to represent the original data. Goal:. Explore high-dimensional data. Autar. Kaw. Humberto . Isaza. http://nm.MathForCollege.com. Transforming Numerical Methods Education for STEM Undergraduates. Eigenvalues and Eigenvectors. http://nm.MathForCollege.com. Objectives. BY. YAN RU LIN. SCOTT HENDERSON. NIRUPAMA GOPALASWAMI. GROUP 4. 11.1 EIGENVALUES & EIGENVECTORS. Definition. An . eigenvector. of a . n . x . n. matrix . A. is a nonzero vector . x. such that . and . eigenvectors. Births. Deaths. Population. . increase. Population. . increase. = . Births. – . deaths. t. Equilibrium. N: . population. . size. b: . birthrate. d: . deathrate. The. net . EIGEN … THINGS. (values, vectors, spaces … ). CONVENTION: . From now on, unless otherwise spec-. ified. , all matrices shall be square, i.e. . . . Another, less simple example:. . What are these . Prepared by Vince Zaccone. For Campus Learning Assistance Services at UCSB. Prepared by Vince Zaccone. For Campus Learning Assistance Services at UCSB. Consider the equation . , where A is an . nxn. Bamshad Mobasher. DePaul University. Principal Component Analysis. PCA is a widely used data . compression and dimensionality reduction technique. PCA takes a data matrix, . A. , of . n. objects by .