PPT-CS 219 : Sparse Matrix Algorithms

Author : stefany-barnette | Published Date : 2019-06-21

John R Gilbert gilbertcsucsbedu wwwcsucsbedugilbert cs219 Systems of linear equations Ax b Eigenvalues and eigenvectors Aw λw Systems of linear equations

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CS 219 : Sparse Matrix Algorithms: Transcript

John R Gilbert gilbertcsucsbedu wwwcsucsbedugilbert cs219 Systems of linear equations Ax b Eigenvalues and eigenvectors Aw λw Systems of linear equations Ax b. Least Absolute Shrinkage via . The . CLASH. Operator. Volkan. Cevher. Laboratory. for Information . . and Inference Systems – . LIONS / EPFL. http://lions.epfl.ch . . & . Idiap. Research Institute. Aswin C Sankaranarayanan. Rice University. Richard G. . Baraniuk. Andrew E. Waters. Background subtraction in surveillance videos. s. tatic camera with foreground objects. r. ank 1 . background. s. parse. &. Miriam . Leeser. Dana Brooks. mel@coe.neu.edu brooks@ece.neu.edu. 1. This . work . is supported by: . NSF . CenSSIS. - The Center for Subsurface Sensing and Imaging. From Theory to Practice . Dina . Katabi. O. . Abari. , E. . Adalsteinsson. , A. Adam, F. . adib. , . A. . Agarwal. , . O. C. . Andronesi. , . Arvind. , A. . Chandrakasan. , F. Durand, E. . Hamed. , H. . J. Friedman, T. Hastie, R. . Tibshirani. Biostatistics, 2008. Presented by . Minhua. Chen. 1. Motivation. Mathematical Model. Mathematical Tools. Graphical LASSO. Related papers. 2. Outline. Motivation. Aditya. Chopra and Prof. Brian L. Evans. Department of Electrical and Computer Engineering. The University of Texas at Austin. 1. Introduction. Finite Impulse Response (FIR) model of transmission media. . Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. MS45: Recent Advances in Sparse and . Non-local Image Regularization - Part III of III. Tianzhu . Zhang. 1,2. , . Adel Bibi. 1. , . Bernard Ghanem. 1. 1. 2. Circulant. Primal . Formulation. 3. Dual Formulation. Fourier Domain. Time . Domain. Here, the inverse Fourier transform is for each . Dense A:. Gaussian elimination with partial pivoting (LU). Same flavor as matrix * matrix, but more complicated. Sparse A:. Gaussian elimination – Cholesky, LU, etc.. Graph algorithms. Sparse A:. Michael . Elad. The Computer Science Department. The . Technion. – Israel Institute of technology. Haifa 32000, . Israel. David L. Donoho. Statistics Department Stanford USA. Author: . Vikas. . Sindhwani. and . Amol. . Ghoting. Presenter: . Jinze. Li. Problem Introduction. we are given a collection of N data points or signals in a high-dimensional space R. D. : xi ∈ . Contents. Problem Statement. Motivation. Types . of . Algorithms. Sparse . Matrices. Methods to solve Sparse Matrices. Problem Statement. Problem Statement. The . solution . of . the linear system is the values of the unknown vector . Shi & Bo. What is sparse system. A system of linear equations is called sparse if . only a relatively small . number of . its matrix . elements . . are nonzero. It is wasteful to use general methods . Jim . Demmel. EECS & Math Departments. UC Berkeley. Why avoid communication? . Communication = moving data. Between level of memory hierarchy. Between processors over a network. Running time of an algorithm is sum of 3 terms:.

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