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Randomized numerical linear algebra - RandNLA, for short - concerns the use of randomization as a resource to develop improved algorithms for large-scale linear algebra computations.
“Optimal Sketching for Kronecker Product Regression and Low Rank Approximation”, 2019
Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun and David. Woodruff · 1909
Earlier work this paper cites.
James Demmel, Laura Grigori and Alexander Rusciano · 1910
Earlier work this paper cites.
M.. Iwen, D. Needell, E. Rebrova and A. Zare · 1912
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“Theory of reproducing kernels”
Nachman Aronszajn · 1950
Earlier work this paper cites.
“Methods of conjugate gradients for solving linear systems”
Magnus Hestenes and Eduard Stiefel · 1952
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“A Note on the Generation of Random Normal Deviates”
G… Box and Mervin. Muller · 1958
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“Chebyshev semi-iterative methods, successive overrelaxation iterative methods, and second order Richardson iterative methods”
Gene. Golub and Richard. Varga · 1961
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“Compatibility of approximate solution of linear equations with given error bounds for coefficients and right-hand sides”
W. Oettli and W. Prager · 1964
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“Linear least squares solutions by householder transformations”
Peter Businger and Gene. Golub · 1965
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“Cramming more components onto integrated circuits”
Gordon Moore · 1965
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“Extensions and applications of the Householder algorithm for solving linear least squares problems”
Richard. Hanson and Charles. Lawson · 1969
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“A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines”
George. Kimeldorf and Grace Wahba · 1970
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“Design of ion-implanted MOSFET’s with very small physical dimensions”
R.H. Dennard, F.H. Gaensslen, V.L. Rideout, E. Bassous and A.R. LeBlanc · 1974
Earlier work this paper cites.
“Some stable methods for calculating inertia and solving symmetric linear systems”
James. Bunch and Linda Kaufman · 1977
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“Research, Development, and LINPACK”
G.W. Stewart · 1977
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“LINPACK users guide”
Jack Dongarra, Cleve Moler, James Bunch and Gilbert Stewart · 1979
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“Basic Linear Algebra Subprograms for Fortran Usage”
C.. Lawson, R.. Hanson, D.. Kincaid and F.. Krogh · 1979
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“A Limit Theorem for the Norm of Random Matrices”
Stuart Geman · 1980
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“The Efficient Generation of Random Orthogonal Matrices with an Application to Condition Estimators”
G.. Stewart · 1980
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“LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares”
Christopher. Paige and Michael. Saunders · 1982
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“Estimating Extremal Eigenvalues and Condition Numbers of Matrices”
John. Dixon · 1983
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“Extensions of Lipshitz mapping into Hilbert space”
W.B. Johnson and J. Lindenstrauss · 1984
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“The Smallest Eigenvalue of a Large Dimensional Wishart Matrix”
Jack. Silverstein · 1985
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“Prospectus for the development of a linear algebra library for high-performance computers” LAPACK Working Note 01, https://netlib.org/lapack/lawns/, 1987
J. Demmel, J. Dongarra, J. Du, A. Greenbaum, S. Hammarling and D. Sorensen · 1987
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“Un algorithme simple et rapid pour la validation croisee géenéralisée sur des probléms de grande taille”, 1987
Didier Girard · 1987
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“Sensitivity Analysis in Linear Regression”
S. Chatterjee and A.S. Hadi · 1988
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“An Extended Set of FORTRAN Basic Linear Algebra Subprograms”
Jack. Dongarra, Jeremy Du, Sven Hammarling and Richard. Hanson · 1988
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“A fast ‘Monte-Carlo cross-validation’ procedure for large least squares problems with noisy data”
A Girard · 1989
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“A Set of Level 3 Basic Linear Algebra Subprograms”
J.. Dongarra, Jeremy Du, Sven Hammarling and I.. Duff · 1990
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“A stochastic estimator of the trace of the influence matrix for Laplacian smoothing splines”
M.F. Hutchinson · 1990
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“Stopping Criteria for Iterative Solvers”
Mario Arioli, Iain Duff and Daniel Ruiz · 1992
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“The Componentwise Distance to the Nearest Singular Matrix”
James Demmel · 1992
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“Estimating the Largest Eigenvalue by the Power and Lanczos Algorithms with a Random Start”
J. Kuczyński and H. Woźniakowski · 1992
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“Generation of random matrices with orthonormal columns and multivariate normal variates with given sample mean and covariance”
Kim-hung Li · 1992
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“An updating algorithm for subspace tracking”
G.W. Stewart · 1992
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“Updating a Rank-Revealing ULV Decomposition”
G.W. Stewart · 1993
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“An introduction to the bootstrap”
Bradley Efron and Robert Tibshirani · 1994
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“A Proposal for a Set of Parallel Basic Linear Algebra Subprograms”
Jaeyoung Choi, Jack Dongarra, Susan Ostrouchov, Antoine Petitet, David. Walker and R. Whaley · 1995
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“A Rank–One Reduction Formula and Its Applications to Matrix Factorizations”
Moody. Chu, Robert. Funderlic and Gene. Golub · 1995
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“A Divide-and-Conquer Algorithm for the Bidiagonal SVD”
Ming Gu and Stanley. Eisenstat · 1995
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“Pseudo-skeleton approximations of matrices”
S.. Goreĭnov, N.. Zamarashkin and E.. Tyrtyshnikov · 1995
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“Random Butterfly Transformations with Applications in Computational Linear Algebra”, 1995
D.. Parker · 1995
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“Some large-scale matrix computation problems”
Zhaojun Bai, Gark Fahey and Gene Golub · 1996
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“Numerical Methods for Least Squares Problems”
Åke Björck · 1996
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“ScaLAPACK: A portable linear algebra library for distributed memory computers—Design issues and performance”
Jaeyoung Choi, James Demmel, Inderjiit Dhillon, Jack Dongarra, Susan Ostrouchov, Antoine Petitet, Ken Stanley, David Walker and R Whaley · 1996
Earlier work this paper cites.
“Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization”
Ming Gu and Stanley. Eisenstat · 1996
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“Matrix Analysis”
Rajendra Bhatia · 1997
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“A theory of pseudoskeleton approximations”
S.A. Goreinov, E.E. Tyrtyshnikov and N.L. Zamarashkin · 1997
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“Iterative refinement for linear systems and LAPACK”
Nicholas. Higham · 1997
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“Accurate Symmetric Indefinite Linear Equation Solvers”
Cleve Ashcraft, Roger. Grimes and John. Lewis · 1998
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“Approximate nearest neighbors: towards removing the curse of dimensionality”
P. Indyk and R. Motwani · 1998
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“LAPACK Users’ Guide”
E. Anderson, Z. Bai, C. Bischof, L.. Blackford, J. Demmel, J. Dongarra, J. Croz, A. Greenbaum, S. Hammarling, A. McKenney and D. Sorensen · 1999
Earlier work this paper cites.
“UTV tools: Matlab templates for rank-revealing UTV decompositions”
Ricardo Fierro, Per Hansen and Peterøren Hansen · 1999
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“The QLP Approximation to the Singular Value Decomposition”
G.. Stewart · 1999
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“The Ziggurat Method for Generating Random Variables”
George Marsaglia and Wai Tsang · 2000
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“On the existence and computation of rank-revealing LU factorizations” Special Issue: Conference celebrating the 60th birthday of Robert J. Plemmons
C.-T. Pan · 2000
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“Using the Nyström Method to Speed Up Kernel Machines”
Christopher Williams and Matthias Seeger · 2000
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“Error norm estimation and stopping criteria in preconditioned conjugate gradient iterations”
Owe Axelsson and Igor Kaporin · 2001
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“On the Complexity of Computing Error Bounds”
J. Demmel, B. Diament and G. Malajovich · 2001
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“Accuracy and Stability of Numerical Algorithms”
Nicholas. Higham · 2002
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“On error estimation in the conjugate gradient method and why it works in finite precision computations”
Zdeněk Strakoš and Petr Tichý · 2002
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“Pass-Efficient Randomized LU Algorithms for Computing Low-Rank Matrix Approximation”, 2020
Bolong Zhang and Michael Mascagni · 2002
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“Database-friendly random projections: Johnson-Lindenstrauss with binary coins”
D. Achlioptas · 2003
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“An elementary proof of a theorem of Johnson and Lindenstrauss”
S. Dasgupta and A. Gupta · 2003
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“Fast Monte-Carlo Algorithms for Finding Low-Rank Approximations”
A. Frieze, R. Kannan and S. Vempala · 2004
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“Numerical solution of saddle point problems”
Michele Benzi, Gene. Golub and Jörg Liesen · 2005
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“On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning”
P. Drineas and M.. Mahoney · 2005
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“FastMap, MetricMap, and Landmark MDS are all Nyström Algorithms”
J.C. Platt · 2005
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“Error Estimation in Preconditioned Conjugate Gradients” Extends a related 2002 paper by the same authors
Zdeněk Strakoš and Petr Tichý · 2005
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“Approximate Nearest Neighbors and the Fast Johnson-Lindenstrauss Transform”
Nir Ailon and Bernard Chazelle · 2006
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“Error Bounds from Extra-Precise Iterative Refinement”
James Demmel, Yozo Hida, William Kahan, Xiaoye. Li, Sonil Mukherjee and E. Riedy · 2006
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“Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication”
P. Drineas, R. Kannan and M.. Mahoney · 2006
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“Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix”
P. Drineas, R. Kannan and M.. Mahoney · 2006
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“Sampling Algorithms for ℓ 2 \ell_{2} Regression and Applications”
P. Drineas, M.. Mahoney and S. Muthukrishnan · 2006
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“Matrix approximation and projective clustering via volume sampling”
Amit Deshpande, Luis Rademacher, Santosh Vempala and Grant Wang · 2006
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“Adaptive Sampling and Fast Low-Rank Matrix Approximation”
Amit Deshpande and Santosh Vempala · 2006
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“Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition” v3 released in 2022, 2020
Brett. Larsen and Tamara. Kolda · 2006
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“Improved Approximation Algorithms for Large Matrices via Random Projections”
Tamas Sarlos · 2006
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“A 30 Year Retrospective on Dennard’s MOSFET Scaling Paper”
Mark Bohr · 2007
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“Fast linear algebra is stable”
James Demmel, Ioana Dumitriu and Olga Holtz · 2007
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“Randomized algorithms for the low-rank approximation of matrices”
Edo Liberty, Franco Woolfe, Per Martinsson, Vladimir Rokhlin and Mark Tygert · 2007
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“How to generate random matrices from the classical compact groups”
Francesco Mezzadri · 2007
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Addison-Wesley Professional, 2007
“GPU Gems 3” · 2007
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“Random Features for Large-Scale Kernel Machines”
Ali Rahimi and Benjamin Recht · 2007
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“A fast randomized algorithm for the approximation of matrices”
Franco Woolfe, Edo Liberty, Vladimir Rokhlin and Mark Tygert · 2007
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“On the Failure of Rank-Revealing QR Factorization Software – A Case Study”
Zlatko Drmač and Zvonimir Bujanović · 2008
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“Relative-Error CUR Matrix Decompositions” This is a longer journal version of two conference papers from 2006
Petros Drineas, Michael. Mahoney and S. Muthukrishnan · 2008
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“Functions of Matrices”
Nicholas. Higham · 2008
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“A fast randomized algorithm for overdetermined linear least-squares regression”
V Rokhlin and M Tygert · 2008
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“A Randomized Kaczmarz Algorithm with Exponential Convergence”
Thomas Strohmer and Roman Vershynin · 2008
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“The Fast Johnson–Lindenstrauss Transform and Approximate Nearest Neighbors”
Nir Ailon and Bernard Chazelle · 2009
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“Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects”
Emmanuel Agullo, Jim Demmel, Jack Dongarra, Bilel Hadri, Jakub Kurzak, Julien Langou, Hatem Ltaief, Piotr Luszczek and Stanimire Tomov · 2009
Earlier work this paper cites.
“An Improved Approximation Algorithm for the Column Subset Selection Problem”
C. Boutsidis, M.. Mahoney and P. Drineas · 2009
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“On selecting a maximum volume sub-matrix of a matrix and related problems”
Ali Çivril and Malik Magdon-Ismail · 2009
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“Numerical Linear Algebra in the Streaming Model”
Kenneth. Clarkson and David. Woodruff · 2009
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“Numerical Matrix Analysis: Linear Systems and Least Squares”
Ilse.. Ipsen · 2009
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“Tensor Decompositions and Applications”
Tamara. Kolda and Brett. Bader · 2009
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“Ensemble Nyström Method”
S. Kumar, M. Mohri and A. Talwalkar · 2009
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“Sampling Techniques for the Nyström Method”
S. Kumar, M. Mohri and A. Talwalkar · 2009
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“Accelerated dense random projections”, 2009
Edo Liberty · 2009
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“CUR matrix decompositions for improved data analysis”
Michael Mahoney and Petros Drineas · 2009
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“Fast and stable randomized low-rank matrix approximation”, 2020
Yuji Nakatsukasa · 2009
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“A Fast and Efficient Algorithm for Low-Rank Approximation of a Matrix”
Nam. Nguyen, Thong. Do and Trac. Tran · 2009
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“Towards dense linear algebra for hybrid GPU accelerated manycore systems”
Stanimire Tomov, Jack Dongarra and Marc Baboulin · 2009
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“Blendenpik: Supercharging LAPACK’s Least-Squares Solver”
Haim Avron, Petar Maymounkov and Sivan Toledo · 2010
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“A Sparse Johnson-Lindenstrauss Transform”
Anirban Dasgupta, Ravi Kumar and Tamás Sarlos · 2010
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“Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving”, 2010
P. Drineas and M.W. Mahoney · 2010
Earlier work this paper cites.
“Matrices, Moments and Quadrature with Applications”
Gene. Golub and Gérard Meurant · 2010
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“The Price of Privately Releasing Contingency Tables and the Spectra of Random Matrices with Correlated Rows”
Shiva Kasiviswanathan, Mark Rudelson, Adam Smith and Jonathan Ullman · 2010
Earlier work this paper cites.
“Making Large-Scale Nyström Approximation Possible”
M. Li, J.T. Kwok and B.-L. Lu · 2010
Earlier work this paper cites.
“Accelerating GPU Kernels for Dense Linear Algebra”
Rajib Nath, Stanimire Tomov and Jack Dongarra · 2010
Cited alongside, same era.
“A Randomized Algorithm for Principal Component Analysis”
Vladimir Rokhlin, Arthur Szlam and Mark Tygert · 2010
Cited alongside, same era.
“Numerical Methods for Electronic Structure Calculations of Materials”
Y. Saad, J.R. Chelikowsky and S.M. Shontz · 2010
Cited alongside, same era.
“Faster Least Squares Approximation”
P. Drineas, M.. Mahoney, S. Muthukrishnan and T. Sarlós · 2011
Cited alongside, same era.
“LSMR: An Iterative Algorithm for Sparse Least-Squares Problems”
David-Lung Fong and Michael Saunders · 2011
Cited alongside, same era.
“CALU: a communication optimal LU factorization algorithm”
L. Grigori, J. Demmel and H. Xiang · 2011
Cited alongside, same era.
Per-Gunnar Martinsson · 2018
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“Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch”
Osman Malik and Stephen Becker · 2018
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“On fast leverage score sampling and optimal learning”
Alessandro Rudi, Daniele Calandriello, Luigi Carratino and Lorenzo Rosasco · 2018
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“Reproducible BLAS: Make Addition Associative Again!”
J. Riedy, J. Demmel and P. Ahrens · 2018
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“Tensor Random Projection for Low Memory Dimension Reduction”
Yiming Sun, Yang Guo, Joel. Tropp and Madeleine Udell · 2018
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“Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions”
N. Halko, P.. Martinsson and J.. Tropp · 2011
Cited alongside, same era.
“Randomized algorithms for matrices and data”, Foundations and Trends in Machine Learning
M.. Mahoney · 2011
Cited alongside, same era.
“Single-pass randomized QLP decomposition for low-rank approximation”, 2020
Huan Ren and Zheng-Jian Bai · 2011
Cited alongside, same era.
“Parallel random numbers: As easy as 1, 2, 3”
John. Salmon, Mark. Moraes, Ron. Dror and David. Shaw · 2011
Cited alongside, same era.
“Improved analysis of the subsampled randomized Hadamard transform”
Joel. Tropp · 2011
Cited alongside, same era.
“Fast approximation of matrix coherence and statistical leverage”
P. Drineas, M. Magdon-Ismail, M.. Mahoney and D.. Woodruff · 2012
Cited alongside, same era.
“Extreme Heterogeneity 2018 – Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity” https://www.osti.gov/servlets/purl/1473756 , 2018
Jeffrey. Vetter, Ron Brightwell, Maya Gokhale, Pat McCormick, Rob Ross, John Shalf, Katie Antypas, David Donofrio, Travis Humble, Catherine Schuman, Brian Van, Shinjae Yoo, Alex Aiken, David Bernholdt, Suren Byna, Kirk Cameron, Frank Cappello, Barbara Chapman, Andrew Chien, Mary Hall, Rebecca Hartman-Baker, Zhiling Lan, Michael Lang, John Leidel, Sherry Li, Robert Lucas, John Mellor-Crummey, Paul Peltz., Thomas Peterka, Michelle Strout and Jeremiah Wilke · 2018
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“High-dimensional probability: An introduction with applications in data science”
Roman Vershynin · 2018
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“Sketched ridge regression: Optimization perspective, statistical perspective, and model averaging”
Shusen Wang, Alex Gittens and Michael Mahoney · 2018
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“Weighted SGD for Lp Regression with Randomized Preconditioning”
J. Yang, Y.-L. Chow, C. Re and M.. Mahoney · 2018
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“Efficient Randomized Algorithms for the Fixed-Precision Low-Rank Matrix Approximation”
Wenjian Yu, Yu Gu and Yaohang Li · 2018
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“Inexact Non-Convex Newton-Type Methods”, 2018
Z. Yao, P. Xu, F. Roosta-Khorasani and M.. Mahoney · 2018
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“Pass-Efficient Randomized Algorithms for Low-Rank Matrix Approximation Using Any Number of Views”
Elvar. Bjarkason · 2019
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“Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression”
Michał Dereziński, Kenneth Clarkson, Michael Mahoney and Manfred Warmuth · 2019
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“Exact sampling of determinantal point processes with sublinear time preprocessing”
Michał Dereziński, Daniele Calandriello and Michal Valko · 2019
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“Fast determinantal point processes via distortion-free intermediate sampling”
Michał Dereziński · 2019
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“PLASMA”
Jack Dongarra, Mark Gates, Azzam Haidar, Jakub Kurzak, Piotr Luszczek, Panruo Wu, Ichitaro Yamazaki, Asim Yarkhan, Maksims Abalenkovs, Negin Bagherpour, Sven Hammarling, Jakub Šístek, David Stevens, Mawussi Zounon and Samuel. Relton · 2019
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“Compressed dynamic mode decomposition for background modeling”
N Erichson, Steven Brunton and J Kutz · 2019
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“Randomized dynamic mode decomposition”
N Erichson, Lionel Mathelin, J Kutz and Steven Brunton · 2019
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“Randomized Matrix Decompositions Using R”
N. Erichson, Sergey Voronin, Steven. Brunton and J. Kutz · 2019
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“Flip-flop spectrum-revealing QR factorization and its applications to singular value decomposition”
Yuehua Feng, Jianwei Xiao and Ming Gu · 2019
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“Robust and Accurate Stopping Criteria for Adaptive Randomized Sampling in Matrix-Free Hierarchically Semiseparable Construction”
Christopher Gorman, Gustavo Chávez, Pieter Ghysels, Théo Mary, François-Henry Rouet and Xiaoye Li · 2019
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“IEEE Standard for Floating-Point Arithmetic”
IEEE · 2019
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“Notes for oneMKL Vector Statistics”, 2019, pp. 120
Intel · 2019
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“Faster least squares optimization”
Jonathan Lacotte and Mert Pilanci · 2019
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“RandUTV: A Blocked Randomized Algorithm for Computing a Rank-Revealing UTV Factorization”
P.. Martinsson, G. Quintana-Ortí and N. Heavner · 2019
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“Iterative Hessian Sketch with Momentum”
Ibrahim Ozaslan, Mert Pilanci and Orhan Arikan · 2019
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“High-performance sampling of generic determinantal point processes”
Jack Poulson · 2019
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“Sub-Sampled Newton Methods”
F. Roosta-Khorasani and M.. Mahoney · 2019
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“Matrix Concentration & Computational Linear Algebra” Lecture notes for a course at École Normale Supérieure, Paris, 2019
Joel. Tropp · 2019
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“Scalable diagnostics for global atmospheric chemistry using Ristretto library (version 1.0)”
Meghana Velegar, N Erichson, Christoph Keller and J Kutz · 2019
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“Isotropy and log-concave polynomials: Accelerated sampling and high-precision counting of matroid bases”
Nima Anari and Michał Dereziński · 2020
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“Algorithms for efficient reproducible floating point summation”
P. Ahren, J. Demmel and H.-D. Nguyen · 2020
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“Oblivious Sketching of High-Degree Polynomial Kernels”
Thomas Ahle, Michael Kapralov, Jakob Knudsen, Rasmus Pagh, Ameya Velingker, David Woodruff and Amir Zandieh · 2020
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“Sampling from a k-DPP without looking at all items”
Daniele Calandriello, Michał Dereziński and Michal Valko · 2020
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“Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs”
Agniva Chowdhury, Palma London, Haim Avron and Petros Drineas · 2020
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“Structured Random Sketching for PDE Inverse Problems”
Ke Chen, Qin Li, Kit Newton and Stephen. Wright · 2020
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“Prospectus for the Next LAPACK and ScaLAPACK Libraries: Basic ALgebra LIbraries for Sustainable Technology with Interdisciplinary Collaboration (BALLISTIC)” http://www.netlib.org/lapack/lawnspdf/lawn297.pdf , 2020
James Demmel, Jack Dongarra, Julie Langou, Julien Langou, Piotr Luszczek and Michael. Mahoney · 2020
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“Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nyström method”
M. Dereziński, R. Khanna and M.. Mahoney · 2020
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“Precise expressions for random projections: Low-rank approximation and randomized Newton”
Michał Dereziński, Feynman Liang, Zhenyu Liao and Michael Mahoney · 2020
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“Randomized CP tensor decomposition”
N Erichson, Krithika Manohar, Steven Brunton and J Kutz · 2020
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“Sparse principal component analysis via variable projection”
N Erichson, Peng Zheng, Krithika Manohar, Steven Brunton, J Kutz and Aleksandr Aravkin · 2020
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“Faster Johnson–Lindenstrauss Transforms via Kronecker Products”
Ruhui Jin, Tamara Kolda and Rachel Ward · 2020
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“Error Estimation for Sketched SVD via the Bootstrap”
Miles. Lopes, N. Erichson and Michael Mahoney · 2020
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“Replacing Pivoting in Distributed Gaussian Elimination with Randomized Techniques”
Neil Lindquist, Piotr Luszczek and Jack Dongarra · 2020
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“Guarantees for the Kronecker Fast Johnson–Lindenstrauss Transform Using a Coherence and Sampling Argument”
Osman Malik and Stephen Becker · 2020
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“Kernel Methods Through the Roof: Handling Billions of Points Efficiently”
Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco and Alessandro Rudi · 2020
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“Randomized numerical linear algebra: Foundations and Algorithms”
Per-Gunnar Martinsson and Joel. Tropp · 2020
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“Tensorized Random Projections”
Beheshteh Rakhshan and Guillaume Rabusseau · 2020
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“Randomized Algorithms for Matrix Computations” Lecture notes (available online in April 2021), 2020
Joel. Tropp · 2020
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“Randomized QLP decomposition”
Nianci Wu and Hua Xiang · 2020
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“Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling”
David Woodruff and Amir Zandieh · 2020
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“Randomized block Gram-Schmidt process for solution of linear systems and eigenvalue problems”, 2021
Oleg Balabanov and Laura Grigori · 2021
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“Norm and Trace Estimation with Random Rank-one Vectors”
Zvonimir Bujanovic and Daniel Kressner · 2021
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“Johnson–Lindenstrauss Embeddings with Kronecker Structure”, 2021
Stefan Bamberger, Felix Krahmer and Rachel Ward · 2021
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“Scrambled Linear Pseudorandom Number Generators” Software available at https://prng.di.unimi.it/
David Blackman and Sebastiano Vigna · 2021
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“Hashing embeddings of optimal dimension, with applications to linear least squares”
Coralia Cartis, Jan Fiala and Zhen Shao · 2021
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“Sparse sketches with small inversion bias”
Michał Dereziński, Zhenyu Liao, Edgar Dobriban and Michael Mahoney · 2021
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“Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update”
Michał Dereziński, Jonathan Lacotte, Mert Pilanci and Michael Mahoney · 2021
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“Determinantal Point Processes in Randomized Numerical Linear Algebra”
M. Dereziński and M.. Mahoney · 2021
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Yijun Dong and Per-Gunnar Martinsson · 2021
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