Arvind K. Saibaba

发表

Randomized algorithms for generalized Hermitian eigenvalue problems with application to computing Karhunen–Loève expansion

Peter K. Kitanidis, Arvind K. Saibaba, Jonghyun Lee, 2013, Numer. Linear Algebra Appl..

Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis pdf

Arvind K. Saibaba, Joseph Hart, Bart van Bloemen Waanders, 2020, Numer. Linear Algebra Appl..

Efficient Randomized Algorithms for Subspace System Identification pdf

Aranya Chakrabortty, Arvind K. Saibaba, Rachel Minster, 2020, ArXiv.

Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems

Arvind K. Saibaba, D. Andrew Brown, Sarah Vallélian, 2018, SIAM/ASA J. Uncertain. Quantification.

A fast algorithm for parabolic PDE-based inverse problems based on Laplace transforms and flexible Krylov solvers

Peter K. Kitanidis, Arvind K. Saibaba, Tania Bakhos, 2014, J. Comput. Phys..

Randomized Discrete Empirical Interpolation Method for Nonlinear Model Reduction

Arvind K. Saibaba, A. Saibaba, 2019, SIAM J. Sci. Comput..

Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems

Arvind K. Saibaba, Alen Alexanderian, D. Andrew Brown, 2019, SIAM/ASA J. Uncertain. Quantification.

HOID: Higher Order Interpolatory Decomposition for Tensors Based on Tucker Representation pdf

Arvind K. Saibaba, A. Saibaba, 2015, SIAM J. Matrix Anal. Appl..

Efficient Algorithms for Eigensystem Realization using Randomized SVD.

Aranya Chakrabortty, Arvind K. Saibaba, Rachel Minster, 2020 .

Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems

Arvind K. Saibaba, Julianne Chung, Katrina Petroske, 2020, Numer. Linear Algebra Appl..

A fast Kalman filter for time-lapse electrical resistivity tomography

Eric L. Miller, Peter K. Kitanidis, Arvind K. Saibaba, 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

Quantifying uncertainties in Large-scale Bayesian linear inverse problems using Krylov subspace methods

Arvind K. Saibaba, Julianne Chung, Katrina Petroske, 2018, 1808.09066.

Fast Algorithms for Hyperspectral Diffuse Optical Tomography

Eric L. Miller, Misha Elena Kilmer, Sergio Fantini, 2015, SIAM J. Sci. Comput..

3D parameter reconstruction in hyperspectral diffuse optical tomography

Sergio Fantini, Arvind K. Saibaba, Pamela G. Anderson, 2015, Photonics West - Biomedical Optics.

Fast computation of uncertainty quantification in the geostatistical approach to inverse problems

Peter K. Kitanidis, Arvind K. Saibaba, A. Saibaba, 2014 .

A randomized tensor singular value decomposition based on the t‐product

Misha Elena Kilmer, Arvind K. Saibaba, Shuchin Aeron, 2016, Numer. Linear Algebra Appl..

Randomized algorithms for low-rank tensor decompositions in the Tucker format pdf

Arvind K. Saibaba, Misha E. Kilmer, Rachel Minster, 2019, SIAM J. Math. Data Sci..

Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems

Arvind K. Saibaba, Alen Alexanderian, 2018, SIAM J. Sci. Comput..

Uncertainty quantification in large Bayesian linear inverse problems using Krylov subspace methods

Arvind K. Saibaba, Julianne Chung, Katrina Petroske, 2018 .

Fast Algorithms for Bayesian Inversion

Eric Darve, Peter K. Kitanidis, Arvind K. Saibaba, 2013 .

Efficient generalized Golub–Kahan based methods for dynamic inverse problems

Arvind K. Saibaba, Julianne Chung, Erik Westman, 2017, 1705.09342.

Multipreconditioned Gmres for Shifted Systems

Peter K. Kitanidis, Arvind K. Saibaba, Daniel B. Szyld, 2016, SIAM J. Sci. Comput..

Generalized Hybrid Iterative Methods for Large-Scale Bayesian Inverse Problems

Arvind K. Saibaba, Julianne Chung, A. Saibaba, 2016, SIAM J. Sci. Comput..

A Flexible Krylov Solver for Shifted Systems with Application to Oscillatory Hydraulic Tomography

Peter K. Kitanidis, Arvind K. Saibaba, Tania Bakhos, 2012, SIAM J. Sci. Comput..

Randomized matrix-free trace and log-determinant estimators

Ilse C. F. Ipsen, Arvind K. Saibaba, Alen Alexanderian, 2016, Numerische Mathematik.

Randomized Subspace Iteration: Analysis of Canonical Angles and Unitarily Invariant Norms

Arvind K. Saibaba, A. Saibaba, 2018, SIAM J. Matrix Anal. Appl..

Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems pdf

Arvind K. Saibaba, Alen Alexanderian, Ahmed Attia, 2018, Inverse Problems.

Monte Carlo Estimators for the Schatten p-norm of Symmetric Positive Semidefinite Matrices pdf

Arvind K. Saibaba, Alen Alexanderian, Ethan Dudley, 2020, ETNA - Electronic Transactions on Numerical Analysis.

Going Off the Grid: Iterative Model Selection for Biclustered Matrix Completion

Arvind K. Saibaba, Arvind U. K. Rao, Eric C. Chi, 2016, Journal of Computational and Graphical Statistics.

Randomized algorithms for rounding in the Tensor-Train format pdf

Arvind K. Saibaba, Grey Ballard, Agnieszka Miedlar, 2021, SIAM J. Sci. Comput..

Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions pdf

Arvind K. Saibaba, Misha E. Kilmer, Rachel Minster, 2021, Advances in Computational Mathematics.

Structured Matrix Approximations via Tensor Decompositions pdf

Arvind K. Saibaba, Misha E. Kilmer, M. Kilmer, 2021, SIAM J. Matrix Anal. Appl..

Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems

Eric L. Miller, Misha Elena Kilmer, Eric de Sturler, 2021, J. Comput. Phys..

Efficient edge-preserving methods for dynamic inverse problems pdf

Eric de Sturler, Arvind K. Saibaba, Silvia Gazzola, 2021, ArXiv.

Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions pdf

Arvind K. Saibaba, William Reese, Jonghyun Lee, 2021, ArXiv.

Fast Kalman filter using hierarchical matrices and a low-rank perturbative approach

Peter K. Kitanidis, Arvind K. Saibaba, Eric Miller, 2014, 1405.2276.

Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite

Arvind K. Saibaba, Scot M. Miller, Arlyn E. Andrews, 2020 .

Efficient methods for large‐scale linear inversion using a geostatistical approach

Peter K. Kitanidis, Arvind K. Saibaba, A. Saibaba, 2012 .