Artigos com autorizações de acesso público - Arvind K. SaibabaSaiba mais
38 artigos disponíveis publicamente
Randomized algorithms for low-rank tensor decompositions in the Tucker format
R Minster, AK Saibaba, ME Kilmer
SIAM journal on mathematics of data science 2 (1), 189-215, 2020
Autorizações: US National Science Foundation
A randomized tensor singular value decomposition based on the t‐product
J Zhang, AK Saibaba, ME Kilmer, S Aeron
Numerical Linear Algebra with Applications 25 (5), e2179, 2018
Autorizações: US National Science Foundation, US Department of Defense, US Office of the …
Randomized matrix-free trace and log-determinant estimators
AK Saibaba, A Alexanderian, ICF Ipsen
Numerische Mathematik 137 (2), 353-395, 2017
Autorizações: US Department of Defense
Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems
A Attia, A Alexanderian, AK Saibaba
Inverse Problems 34 (9), 095009, 2018
Autorizações: US National Science Foundation, US Department of Energy
Randomized algorithms for generalized Hermitian eigenvalue problems with application to computing Karhunen–Loève expansion
AK Saibaba, J Lee, PK Kitanidis
Numerical Linear Algebra with Applications 23 (2), 314-339, 2016
Autorizações: US National Science Foundation
Efficient D-optimal design of experiments for infinite-dimensional Bayesian linear inverse problems
A Alexanderian, AK Saibaba
SIAM Journal on Scientific Computing 40 (5), A2956-A2985, 2018
Autorizações: US National Science Foundation
Randomized subspace iteration: Analysis of canonical angles and unitarily invariant norms
AK Saibaba
SIAM Journal on Matrix Analysis and Applications 40 (1), 23-48, 2019
Autorizações: US National Science Foundation
Generalized hybrid iterative methods for large-scale Bayesian inverse problems
J Chung, AK Saibaba
SIAM Journal on Scientific Computing 39 (5), S24-S46, 2017
Autorizações: US National Science Foundation
Efficient generalized Golub–Kahan based methods for dynamic inverse problems
J Chung, AK Saibaba, M Brown, E Westman
Inverse Problems 34 (2), 024005, 2018
Autorizações: US National Science Foundation
Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite
SM Miller, AK Saibaba, ME Trudeau, ME Mountain, AE Andrews
Geoscientific Model Development 13 (3), 1771-1785, 2020
Autorizações: US National Science Foundation, US National Aeronautics and Space …
Randomized algorithms for rounding in the tensor-train format
H Al Daas, G Ballard, P Cazeaux, E Hallman, A Międlar, M Pasha, ...
SIAM Journal on Scientific Computing 45 (1), A74-A95, 2023
Autorizações: US National Science Foundation
Randomized discrete empirical interpolation method for nonlinear model reduction
AK Saibaba
SIAM Journal on Scientific Computing 42 (3), A1582-A1608, 2020
Autorizações: US National Science Foundation
Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis
AK Saibaba, J Hart, B van Bloemen Waanders
Numerical linear algebra with applications 28 (4), e2364, 2021
Autorizações: US National Science Foundation, US Department of Energy
Multipreconditioned GMRES for shifted systems
T Bakhos, PK Kitanidis, S Ladenheim, AK Saibaba, DB Szyld
SIAM Journal on Scientific Computing 39 (5), S222-S247, 2017
Autorizações: US National Science Foundation, US Department of Energy
Low-rank independence samplers in hierarchical Bayesian inverse problems
DA Brown, A Saibaba, S Vallélian
SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1076-1100, 2018
Autorizações: US National Science Foundation
Efficient marginalization-based MCMC methods for hierarchical Bayesian inverse problems
AK Saibaba, J Bardsley, DA Brown, A Alexanderian
SIAM/ASA Journal on Uncertainty Quantification 7 (3), 1105-1131, 2019
Autorizações: US National Science Foundation
Randomization and Reweighted -Minimization for A-Optimal Design of Linear Inverse Problems
E Herman, A Alexanderian, AK Saibaba
SIAM Journal on Scientific Computing 42 (3), A1714-A1740, 2020
Autorizações: US National Science Foundation
Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems
AK Saibaba, J Chung, K Petroske
Numerical Linear Algebra with Applications 27 (5), e2325, 2020
Autorizações: US National Science Foundation
A computational framework for edge-preserving regularization in dynamic inverse problems
M Pasha, AK Saibaba, S Gazzola, MI Español, E de Sturler
Electronic Transactions on Numerical Analysis 58 (486), 486-516, 2023
Autorizações: US National Science Foundation, UK Engineering and Physical Sciences …
Monte Carlo methods for estimating the diagonal of a real symmetric matrix
E Hallman, ICF Ipsen, AK Saibaba
SIAM Journal on Matrix Analysis and Applications 44 (1), 240-269, 2023
Autorizações: US National Science Foundation, US Department of Energy
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