PPT-The Landscape of Sparse Ax=b Solvers

Author : test | Published Date : 2016-03-02

Direct A LU Iterative y Ay Non symmetric Symmetric positive definite More Robust Less Storage More Robust More General D Column Cholesky Factorization for j

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The Landscape of Sparse Ax=b Solvers: Transcript

Direct A LU Iterative y Ay Non symmetric Symmetric positive definite More Robust Less Storage More Robust More General D Column Cholesky Factorization for j 1 n L. Volkan . Cevher. volkan.cevher@epfl.ch. Laboratory. for Information . . and Inference Systems - . LIONS. . http://lions.epfl.ch. Linear Dimensionality Reduction. Compressive sensing. non-adaptive measurements. Teaching & Learning Conference. Jane Nolan MBE. Entrepreneur in Residence and Development Officer (Careers Service). Katie Wray. Lecturer in Enterprise (SAgE Faculty). Entrepreneurial Students. Some statistics:. Full storage:. . 2-dimensional array.. (nrows*ncols) 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 1-dimensional arrays.. 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. . 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 . Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Richard Peng. Georgia Tech. Based on . recent works . joint with:. Serban . Stan (Yale. ), . Haoran. . Xu (MIT. ),. Shen . Chen Xu (CMU. ), . Saurabh. . Sawlani. (. GaTech. ). John . Gilbert (UCSB. to Multiple Correspondence . Analysis. G. Saporta. 1. , . A. . . Bernard. 1,2. , . C. . . Guinot. 2,3. 1 . CNAM, Paris, France. 2 . CE.R.I.E.S., Neuilly sur Seine, France. 3 . Université. . François Rabelais. Michael . Elad. The Computer Science Department. The . Technion. – Israel Institute of technology. Haifa 32000, . Israel. David L. Donoho. Statistics Department Stanford USA. Guy Katz. Schloss. . Dagstuhl. , October 2016. Acknowledgements . Based on joint work with Clark Barrett, Cesare . Tinelli. , Andrew Reynolds and Liana . Hadarean. (. FMCAD’16. ). 2. Stanford . University. Preprocessing. Can . Efficiently. . Simulate. Resolution. Paul . Beame. *. . Ashish Sabharwal. . *. Computer Science and Engineering, University of Washington, Seattle, WA, USA. . Allen Institute for Artificial Intelligence, Seattle, WA, USA. 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. Reading Group Presenter:. Zhen . Hu. Cognitive Radio Institute. Friday, October 08, 2010. Authors: Carlos M. . Carvalho. , Nicholas G. Polson and James G. Scott. Outline. Introduction. Robust Shrinkage of Sparse Signals.

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