PPT-Compressed Sparse Matrix Storage
Author : lois-ondreau | Published Date : 2016-07-17
Full storage 2dimensional array nrowsncols memory 31 0 53 0 59 0 41 26 0 31 41 59 26 53 1 3 2 3 1 Sparse storage Compressed storage by columns CSC Three 1dimensional
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Compressed Sparse Matrix Storage: Transcript
Full storage 2dimensional array nrowsncols memory 31 0 53 0 59 0 41 26 0 31 41 59 26 53 1 3 2 3 1 Sparse storage Compressed storage by columns CSC Three 1dimensional arrays. Compressed gas description . Definition of a . compressed gas – . any gas, or mixture . of gases, that is . pressurized and . contained in a . cylinder or tank.. 1a. Compressed gas description . Examples of compressed gases. 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. Yang Lu. April. . 2014. Imperial College London. Outline. Introduction to Synthetic Aperture Radar (SAR) . Background of Compressed Sensing. Reconstruct Radar Image by CS methods. Introduction . to SAR . . Sparse matrix data structures, graphs. , manipulation. Xiaoye . Sherry Li. Lawrence Berkeley National . Laboratory. , . USA. xsli@. lbl.gov. crd-legacy.lbl.gov. /~. xiaoye. /G2S3. /. 4. th. Gene . Recovery. . (. Using . Sparse. . Matrices). Piotr. . Indyk. MIT. Heavy Hitters. Also called frequent elements and elephants. Define. HH. p. φ. . (. x. ) = { . i. : |x. i. | ≥ . φ. ||. x||. p. Compressed Sensing. Mobashir. . Mohammad. Aditya Kulkarni. Tobias Bertelsen. Malay Singh. Hirak. . Sarkar. Nirandika. . Wanigasekara. Yamilet Serrano . Llerena. Parvathy. . Sudhir. Introduction. Mobashir. Student : . Shenghan. TSAI. Advisor : . Hsuan. -Jung Su and . Pin-. Hsun. Lin. Date : M. ay 02, . 2014. 1. Outline. Introduction. Signal---Sparse and Compressible. . - . Sparse & Compressible. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . 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:. Matrix. •. . Binary Matrix. •. . Sparse Matrix. •. . Operations for Vectors/Matrices. •. . Graph and Adjacent Matrix. •. . Adjacent List. Matrix and Graph. •. . Matrix is a 2-dimensional . 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 ∈ . Dileep Mardham. Introduction. Sparse Direct Solvers is a fundamental tool in scientific computing. Sparse factorization can be a challenge to accelerate using GPUs. GPUs(Graphics Processing Units) can be quite good for accelerating sparse direct solvers. Yi Ma. 1,2. . Allen Yang. 3. John . Wright. 1. CVPR Tutorial, June 20, 2009. 1. Microsoft Research Asia. 3. University of California Berkeley. 2. University of Illinois . at Urbana-Champaign. John R. Gilbert (. gilbert@cs.ucsb.edu. ). www.cs.ucsb.edu/~gilbert/. cs219. Systems of linear equations:. . Ax = . b. Eigenvalues and eigenvectors:. Aw = . λw. Systems of linear equations: Ax = b.
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