Arvind Krishna Saibaba

Pasha, M., Saibaba, A. K., Gazzola, S., Espanol, M. I., & De Sturler, E. (2023). A COMPUTATIONAL FRAMEWORK FOR EDGE-PRESERVING REGULARIZATION IN DYNAMIC INVERSE PROBLEMS. ELECTRONIC TRANSACTIONS ON NUMERICAL ANALYSIS, 58, 486–516. https://doi.org/10.1553/etna_vol58s486

Hallman, E., Ipsen, I. C. F., & Saibaba, A. K. (2023). MONTE CARLO METHODS FOR ESTIMATING THE DIAGONAL OF A REAL SYMMETRIC MATRIX. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 44(1), 240–269. https://doi.org/10.1137/22M1476277

Al Daas, H., Ballard, G., Cazeaux, P., Hallman, E., Miedlar, A., Pasha, M., … Saibaba, A. K. (2023). RANDOMIZED ALGORITHMS FOR ROUNDING IN THE TENSOR-TRAIN FORMAT. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 45(1), A74–A95. https://doi.org/10.1137/21M1451191

Antil, H., & Saibaba, A. K. (2023). Randomized reduced basis methods for parameterized fractional elliptic PDEs. FINITE ELEMENTS IN ANALYSIS AND DESIGN, 227. https://doi.org/10.1016/j.finel.2023.104046

Farazmand, M., & Saibaba, A. K. (2023). Tensor-based flow reconstruction from optimally located sensor measurements. JOURNAL OF FLUID MECHANICS, 962. https://doi.org/10.1017/jfm.2023.269

Cho, T., Chung, J., Miller, S. M., & Saibaba, A. K. (2022). Computationally efficient methods for large-scale atmospheric inverse modeling. GEOSCIENTIFIC MODEL DEVELOPMENT, 15(14), 5547–5565. https://doi.org/10.5194/gmd-15-5547-2022

Saibaba, A. K., Minster, R., & Kilmer, M. E. (2022). Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions. ADVANCES IN COMPUTATIONAL MATHEMATICS, 48(5). https://doi.org/10.1007/s10444-022-09979-7

Majumder, S., Guan, Y., Reich, B. J., & Saibaba, A. K. (2022). Kryging: geostatistical analysis of large-scale datasets using Krylov subspace methods. STATISTICS AND COMPUTING, 32(5). https://doi.org/10.1007/s11222-022-10104-3

DUDLEY, E. T. H. A. N., SAIBABA, A. R. V. I. N. D. K., & ALEXANDERIAN, A. L. E. N. (2022). MONTE CARLO ESTIMATORS FOR THE SCHATTEN p-NORM OF SYMMETRIC POSITIVE SEMIDEFINITE MATRICES. ELECTRONIC TRANSACTIONS ON NUMERICAL ANALYSIS, 55, 213–241. https://doi.org/10.1553/etna_vol55s213

Kilmer, M. E., & Saibaba, A. K. (2022). STRUCTURED MATRIX APPROXIMATIONS VIA TENSOR DECOMPOSITIONS. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 43(4), 1599–1626. https://doi.org/10.1137/21M1418290

Minster, R., Saibaba, A. K., Kar, J., & Chakrabortty, A. (2021). EFFICIENT ALGORITHMS FOR EIGENSYSTEM REALIZATION USING RANDOMIZED SVD. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 42(2), 1045–1072. https://doi.org/10.1137/20M1327616

Saibaba, A. K., Hart, J., & Bloemen Waanders, B. (2021). Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis. NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, 28(4). https://doi.org/10.1002/nla.2364

Saibaba, A. K., Prasad, P., Sturler, E., Miller, E., & Kilmer, M. E. (2021). Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems. JOURNAL OF COMPUTATIONAL PHYSICS, 440. https://doi.org/10.1016/j.jcp.2021.110391

Saibaba, A. K., Chung, J., & Petroske, K. (2020). Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems. NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, 27(5). https://doi.org/10.1002/nla.2325

Miller, S. M., Saibaba, A. K., Trudeau, M. E., Mountain, M. E., & Andrews, A. E. (2020). Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite. GEOSCIENTIFIC MODEL DEVELOPMENT, 13(3), 1771–1785. https://doi.org/10.5194/gmd-13-1771-2020

Herman, E., Alexanderian, A., & Saibaba, A. K. (2020). RANDOMIZATION AND REWEIGHTED l(1)-MINIMIZATION FOR A-OPTIMAL DESIGN OF LINEAR INVERSE PROBLEMS. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 42(3), A1714–A1740. https://doi.org/10.1137/19M1267362

Minster, R., Saibaba, A. K., & Kilmer, M. E. (2020). Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format. SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE, 2(1), 189–215. https://doi.org/10.1137/19m1261043

Randomized Discrete Empirical Interpolation Method for Nonlinear Model Reduction. (2020). SIAM Journal on Scientific Computing. https://doi.org/10.1137/19m1243270

Saibaba, A. K., Bardsley, J., Brown, D. A., & Alexanderian, A. (2019). Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 7(3), 1105–1131. https://doi.org/10.1137/18M1220625

Chi, E. C., Hu, L., Saibaba, A. K., & Rao, A. U. K. (2019). Going Off the Grid: Iterative Model Selection for Biclustered Matrix Completion. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 28(1), 36–47. https://doi.org/10.1080/10618600.2018.1482763

Saibaba, A. K. (2019). RANDOMIZED SUBSPACE ITERATION: ANALYSIS OF CANONICAL ANGLES AND UNITARILY INVARIANT NORMS. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 40(1), 23–48. https://doi.org/10.1137/18M1179432

Zhang, J., Saibaba, A. K., Kilmer, M. E., & Aeron, S. (2018). A randomized tensor singular value decomposition based on the t-product. NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, 25(5). https://doi.org/10.1002/nla.2179

Alexanderian, A., & Saibaba, A. K. (2018). EFFICIENT D-OPTIMAL DESIGN OF EXPERIMENTS FOR INFINITE-DIMENSIONAL BAYESIAN LINEAR INVERSE PROBLEMS. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 40(5), A2956–A2985. https://doi.org/10.1137/17M115712X

Chung, J., Saibaba, A., Brown, M., & Westman, E. (2018). Efficient generalized Golub-Kahan based methods for dynamic inverse problems. Inverse Problems, 34(2). https://doi.org/10.1088/1361-6420/aaa0e1

Attia, A., Alexanderian, A., & Saibaba, A. K. (2018). Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems. INVERSE PROBLEMS, 34(9). https://doi.org/10.1088/1361-6420/aad210

Brown, D. A., Saibaba, A., & Vallelian, S. (2018). Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 6(3), 1076–1100. https://doi.org/10.1137/17M1137218

Drmac, Z., & Saibaba, A. K. (2018). THE DISCRETE EMPIRICAL INTERPOLATION METHOD: CANONICAL STRUCTURE AND FORMULATION IN WEIGHTED INNER PRODUCT SPACES. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 39(3), 1152–1180. https://doi.org/10.1137/17M1129635

Chung, J., & Saibaba, A. K. (2017). GENERALIZED HYBRID ITERATIVE METHODS FOR LARGE-SCALE BAYESIAN INVERSE PROBLEMS. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 39(5), S24–S46. https://doi.org/10.1137/16m1081968

Bakhos, T., Kitanidis, P. K., Ladenheim, S., Saibaba, A. K., & Szyld, D. B. (2017). MULTIPRECONDITIONED GMRES FOR SHIFTED SYSTEMS. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 39(5), S222–S247. https://doi.org/10.1137/16m1068694

Saibaba, A. K., Alexanderian, A., & Ipsen, I. C. F. (2017). Randomized matrix-free trace and log-determinant estimators. NUMERISCHE MATHEMATIK, 137(2), 353–395. https://doi.org/10.1007/s00211-017-0880-z

Saibaba, A. K. (2016). HOID: HIGHER ORDER INTERPOLATORY DECOMPOSITION FOR TENSORS BASED ON TUCKER REPRESENTATION. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 37(3), 1223–1249. https://doi.org/10.1137/15m1048628

Saibaba, A. K., Krishnamurthy, N., Anderson, P. G., Kainerstorfer, J. M., Sassaroli, A., Miller, E. L., … Kilmer, M. E. (2015). 3D parameter reconstruction in hyperspectral diffuse optical tomography. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 9319. https://doi.org/10.1117/12.2079775

Bakhos, T., Saibaba, A. K., & Kitanidis, P. K. (2015). A fast algorithm for parabolic PDE-based inverse problems based on Laplace transforms and flexible Krylov solvers. Journal of Computational Physics, 299, 940–954. https://doi.org/10.1016/j.jcp.2015.07.007

Saibaba, A. K., Miller, E. L., & Kitanidis, P. K. (2015). Fast Kalman filter using hierarchical matrices and a low-rank perturbative approach. Inverse Problems, 31(1). https://doi.org/10.1088/0266-5611/31/1/015009

Saibaba, A. K., Kilmer, M., Miller, E. L., & Fantini, S. (2015). Fast algorithms for hyperspectral diffuse optical tomography. SIAM Journal on Scientific Computing, 37(5), B712–B743. https://doi.org/10.1137/140990073

Saibaba, A. K., & Kitanidis, P. K. (2015). Fast computation of uncertainty quantification measures in the geostatistical approach to solve inverse problems. Advances in Water Resources, 82, 124–138. https://doi.org/10.1016/j.advwatres.2015.04.012

Saibaba, A. K., Lee, J., & Kitanidis, P. K. (2015). Randomized algorithms for generalized Hermitian eigenvalue problems with application to computing Karhunen-Loève expansion. Numerical Linear Algebra with Applications, 23(2), 314–339. https://doi.org/10.1002/nla.2026

Saibaba, A. K., Miller, E. L., & Kitandis, P. K. (2014). A fast Kalman filter for time-lapse electrical resistivity tomography. International Geoscience and Remote Sensing Symposium (IGARSS), 3152–3155. https://doi.org/10.1109/IGARSS.2014.6947146

Saibaba, A. K., Bakhos, T., & Kitanidis, P. K. (2013). A flexible krylov solver for shifted systemswith application to oscillatory hydraulic tomography. SIAM Journal on Scientific Computing, 35(6). https://doi.org/10.1137/120902690

Ambikasaran, S., Saibaba, A. K., Darve, E. F., & Kitanidis, P. K. (2013). Fast Algorithms for Bayesian Inversion. Computational Challenges in the Geosciences, pp. 101–142. https://doi.org/10.1007/978-1-4614-7434-0_5

Saibaba, A. K., Ambikasaran, S., Yue Li, J., Kitanidis, P. K., & Darve, E. F. (2012). Application of hierarchical matrices to linear inverse problems in geostatistics | Application des matrices hiérarchiques aux problèmes d'inversion linéaire en géostatistique. Oil and Gas Science and Technology, 67(5), 857–875. https://doi.org/10.2516/ogst/2012064

Saibaba, A. K., & Kitanidis, P. K. (2012). Efficient methods for large-scale linear inversion using a geostatistical approach. Water Resources Research, 48(5). https://doi.org/10.1029/2011WR011778