Online pattern matching in sparse matrices and contact maps Robert Frasera∗ a David R. Cheriton School of Computer Science, University of Waterloo,

Source URL: www.cs.umanitoba.ca

Tutorial: Sparse Recovery Using Sparse Matrices Piotr Indyk MIT Problem Formulation

Source URL: conference.itcs.tsinghua.edu.cn

Tutorial: Sparse Recovery Using Sparse Matrices Piotr Indyk MIT Problem Formulation

Source URL: people.csail.mit.edu

Deriving E
cient Parallel Implementations of Algorithms Operating on General Sparse Matrices using Program Transformation Stephen Fitzpatrick

Source URL: www.kestrel.edu

Sparse Recovery Using Sparse (Random) Matrices Piotr Indyk MIT Joint work with: Radu Berinde, Anna Gilbert, Howard Karloff, Martin Strauss and Milan Ruzic

Source URL: people.csail.mit.edu

Sparse estimation of large covariance matrices via a nested Lasso penalty

Source URL: dept.stat.lsa.umich.edu

1 Robust Support Recovery Using Sparse Compressive Sensing Matrices ∗

Source URL: www.ece.umn.edu

Positive-Definite ℓ1-Penalized Estimation of Large Covariance Matrices Lingzhou XUE, Shiqian MA, and Hui ZOU The thresholding covariance estimator has nice asymptotic properties for estimating sparse large covariance m

Source URL: users.stat.umn.edu

Communication Optimal Parallel Multiplication of Sparse Random Matrices∗ Grey Ballard Aydın Buluç

Source URL: gauss.cs.ucsb.edu

• Multiplication
• Matrix theory
• Numerical linear algebra
• Matrix multiplication algorithm
• Analysis of algorithms
• Computational complexity theory
• Matrix multiplication
• Matrix
• Parallel computing
• Multiplication algorithm
• Algorithm
• Time complexity

AN ANALOG SUB-LINEAR TIME SPARSE SIGNAL ACQUISITION FRAMEWORK BASED ON STRUCTURED MATRICES Juhwan Yoo, Amin Khajehnejad, Babak Hassibi, and Azita Emami-Neyestanak Department of Electrical Engineering, California Institut

Source URL: www.mics.caltech.edu