Fetching the paper…
Reading the bibliography…
Recent work in theoretical computer science and scientific computing has focused on nearly-linear-time algorithms for solving systems of linear equations.
Sensitivity Analysis in Linear Regression
S. Chatterjee and A.S. Hadi · 1988
Earlier work this paper cites.
Matrix Perturbation Theory
G.W. Stewart and J.G. Sun · 1990
Earlier work this paper cites.
Matrix Computations
G.H. Golub and C.F. Van Loan · 1996
Earlier work this paper cites.
Spectral graph theory
F.R.K. Chung · 1997
Earlier work this paper cites.
L.N. Trefethen and D. Bau III · 1997
Earlier work this paper cites.
Support theory for preconditioning
E.G. Boman and B. Hendrickson · 2003
Earlier work this paper cites.
Maximum-weight-basis preconditioners
E.G. Boman, D. Chen, B. Hendrickson, and S. Toledo · 2004
Earlier work this paper cites.
Fast Monte-Carlo algorithms for finding low-rank approximations
A. Frieze, R. Kannan, and S. Vempala · 2004
Earlier work this paper cites.
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
N. Ailon and B. Chazelle · 2006
Earlier work this paper cites.
Support-graph preconditioners
M. Bern, J.R. Gilbert, B. Hendrickson, N. Nguyen, and S. Toledo · 2006
Earlier work this paper cites.
Matrix approximation and projective clustering via volume sampling
A. Deshpande, L. Rademacher, S. Vempala, and G. Wang · 2006
Earlier work this paper cites.
Fast Monte Carlo algorithms for matrices I: Approximating matrix multiplication
P. Drineas, R. Kannan, and M.W. Mahoney · 2006
Earlier work this paper cites.
Fast Monte Carlo algorithms for matrices II: Computing a low-rank approximation to a matrix
P. Drineas, R. Kannan, and M.W. Mahoney · 2006
Earlier work this paper cites.
Fast Monte Carlo algorithms for matrices III: Computing a compressed approximate matrix decomposition
P. Drineas, R. Kannan, and M.W. Mahoney · 2006
Cited alongside, same era.
Sampling algorithms for ℓ 2 \ell_{2} regression and applications
P. Drineas, M.W. Mahoney, and S. Muthukrishnan · 2006
Cited alongside, same era.
Preconditioner approximations for probabilistic graphical models
P. Ravikumar and J. Lafferty · 2006
Cited alongside, same era.
Improved approximation algorithms for large matrices via random projections
T. Sarlós · 2006
Cited alongside, same era.
Nearly-linear time algorithms for preconditioning and solving symmetric, diagonally dominant linear systems
D.A. Spielman and S.-H. Teng · 2006
Cited alongside, same era.
An estimator for the diagonal of a matrix
C. Bekas, E. Kokiopoulou, and Y. Saad · 2007
A fast randomized algorithm for overdetermined linear least-squares regression
V. Rokhlin and M. Tygert · 2008
Later among the works it cites.
Graph sparsification by effective resistances
D.A. Spielman and N. Srivastava · 2008
Later among the works it cites.
D.A. Spielman and S.-H. Teng · 2008
Later among the works it cites.
Spectral sparsification of graphs
D.A. Spielman and S.-H. Teng · 2008
Later among the works it cites.
Blendenpik: Supercharging LAPACK’s least-squares solver
H. Avron, P. Maymounkov, and S. Toledo · 2009
Later among the works it cites.
Twice-Ramanujan sparsifiers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Sparsity and incoherence in compressive sampling
E. Candes and J. Romberg · 2007
Cited alongside, same era.
Faster least squares approximation
P. Drineas, M.W. Mahoney, S. Muthukrishnan, and T. Sarlós · 2007
Cited alongside, same era.
PCA-correlated SNPs for structure identification in worldwide human populations
P. Paschou, E. Ziv, E.G. Burchard, S. Choudhry, W. Rodriguez-Cintron, M.W. Mahoney, and P. Drineas · 2007
Cited alongside, same era.
Sampling from large matrices: an approach through geometric functional analysis
M. Rudelson and R. Vershynin · 2007
Cited alongside, same era.
Solving elliptic finite element systems in near-linear time with support preconditioners
E.G. Boman, B. Hendrickson, and S. Vavasis · 2008
Cited alongside, same era.
Unsupervised feature selection for principal components analysis
C. Boutsidis, M.W. Mahoney, and P. Drineas · 2008
Cited alongside, same era.
J. Batson, D.A. Spielman, and N. Srivastava · 2009
Later among the works it cites.
Low cost high performance uncertainty quantification
C. Bekas, A. Curioni, and I. Fedulova · 2009
Later among the works it cites.
Spectral methods in machine learning and new strategies for very large datasets
M.-A. Belabbas and P. J. Wolfe · 2009
Later among the works it cites.
An improved approximation algorithm for the column subset selection problem
C. Boutsidis, M.W. Mahoney, and P. Drineas · 2009
Later among the works it cites.
Unsupervised feature selection for the k k -means clustering problem
C. Boutsidis, M.W. Mahoney, and P. Drineas · 2009
Later among the works it cites.
CUR matrix decompositions for improved data analysis
M.W. Mahoney and P. Drineas · 2009
Later among the works it cites.
Efficient volume sampling for row/column subset selection
A. Deshpande and L. Rademacher · 2010
Closest in time.
Numerical methods for electronic structure calculations of materials
Y. Saad, J. R. Chelikowsky, and S. M. Shontz · 2010
Closest in time.