Arvind K. Saibaba

According to our database, Arvind K. Saibaba authored at least 44 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bayesian D-Optimal Experimental Designs via Column Subset Selection: The Power of Reweighted Sensors.

Srinivas Eswar, Vishwas Rao, Arvind K. Saibaba

CoRR, 2024

Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation.

Amit N. Subrahmanya, Vishwas Rao, Arvind K. Saibaba

CoRR, 2024

2023

Monte Carlo Methods for Estimating the Diagonal of a Real Symmetric Matrix.

Eric Hallman, Ilse C. F. Ipsen, Arvind K. Saibaba

SIAM J. Matrix Anal. Appl., March, 2023

Randomized Algorithms for Rounding in the Tensor-Train Format.

Hussam Al Daas, Grey Ballard, Paul Cazeaux, Eric Hallman, Agnieszka Miedlar, Mirjeta Pasha, Tim W. Reid, Arvind K. Saibaba

SIAM J. Sci. Comput., February, 2023

Efficient iterative methods for hyperparameter estimation in large-scale linear inverse problems.

Khalil A Hall-Hooper, Arvind K. Saibaba, Julianne Chung, Scot M. Miller

CoRR, 2023

Randomized low-rank approximations beyond Gaussian random matrices.

Arvind K. Saibaba, Agnieszka Miedlar

CoRR, 2023

Randomized Reduced Basis Methods for Parameterized Fractional Elliptic PDEs.

Harbir Antil, Arvind K. Saibaba

CoRR, 2023

2022

Structured Matrix Approximations via Tensor Decompositions.

Misha E. Kilmer, Arvind K. Saibaba

SIAM J. Matrix Anal. Appl., 2022

Kryging: geostatistical analysis of large-scale datasets using Krylov subspace methods.

Suman Majumder, Yawen Guan, Brian J. Reich, Arvind K. Saibaba

Stat. Comput., 2022

Tensor-based flow reconstruction from optimally located sensor measurements.

Mohammad Farazmand, Arvind K. Saibaba

CoRR, 2022

Hybrid Projection Methods for Solution Decomposition in Large-scale Bayesian Inverse Problems.

Julianne Chung, Jiahua Jiang, Scot M. Miller, Arvind K. Saibaba

CoRR, 2022

Efficient algorithms for Bayesian Inverse Problems with Whittle-Matérn Priors.

Harbir Antil, Arvind K. Saibaba

CoRR, 2022

Robust Parameter Identifiability Analysis via Column Subset Selection.

Katherine J. Pearce, Ilse C. F. Ipsen, Mansoor A. Haider, Arvind K. Saibaba, Ralph C. Smith

CoRR, 2022

Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR).

Ege Ozsar, Misha Elena Kilmer, Eric L. Miller, Eric de Sturler, Arvind K. Saibaba

CoRR, 2022

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

Arvind K. Saibaba, Rachel Minster, Misha E. Kilmer

Adv. Comput. Math., 2022

2021

Efficient Algorithms for Eigensystem Realization Using Randomized SVD.

Rachel Minster, Arvind K. Saibaba, Jishnudeep Kar, Aranya Chakrabortty

SIAM J. Matrix Anal. Appl., 2021

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

Arvind K. Saibaba, Joseph L. Hart, Bart G. van Bloemen Waanders

Numer. Linear Algebra Appl., 2021

Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems.

Arvind K. Saibaba, Pranjal Prasad, Eric de Sturler, Eric L. Miller, Misha E. Kilmer

J. Comput. Phys., 2021

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

William Reese, Arvind K. Saibaba, Jonghyun Lee

CoRR, 2021

Efficient edge-preserving methods for dynamic inverse problems.

Mirjeta Pasha, Arvind K. Saibaba, Silvia Gazzola, Malena I. Español, Eric de Sturler

CoRR, 2021

Approximating monomials using Chebyshev polynomials.

Arvind K. Saibaba

CoRR, 2021

2020

Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format.

Rachel Minster, Arvind K. Saibaba, Misha E. Kilmer

SIAM J. Math. Data Sci., 2020

Randomized Discrete Empirical Interpolation Method for Nonlinear Model Reduction.

Arvind K. Saibaba

SIAM J. Sci. Comput., 2020

Randomization and Reweighted ℓ₁-Minimization for A-Optimal Design of Linear Inverse Problems.

Elizabeth Herman, Alen Alexanderian, Arvind K. Saibaba

SIAM J. Sci. Comput., 2020

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

Arvind K. Saibaba, Julianne Chung, Katrina Petroske

Numer. Linear Algebra Appl., 2020

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

Ethan Dudley, Arvind K. Saibaba, Alen Alexanderian

CoRR, 2020

Efficient Randomized Algorithms for Subspace System Identification.

Rachel Minster, Arvind K. Saibaba, Jishnudeep Kar, Aranya Chakrabortty

CoRR, 2020

2019

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

Arvind K. Saibaba

SIAM J. Matrix Anal. Appl., 2019

Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems.

Arvind K. Saibaba, Johnathan M. Bardsley, D. Andrew Brown, Alen Alexanderian

SIAM/ASA J. Uncertain. Quantification, 2019

Randomization and reweighted 𝓁¹-minimization for A-optimal design of linear inverse problems.

Elizabeth Herman, Alen Alexanderian, Arvind K. Saibaba

CoRR, 2019

2018

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

Alen Alexanderian, Arvind K. Saibaba

SIAM J. Sci. Comput., 2018

The Discrete Empirical Interpolation Method: Canonical Structure and Formulation in Weighted Inner Product Spaces.

Zlatko Drmac, Arvind Krishna Saibaba

SIAM J. Matrix Anal. Appl., 2018

A randomized tensor singular value decomposition based on the t-product.

Jiani Zhang, Arvind K. Saibaba, Misha E. Kilmer, Shuchin Aeron

Numer. Linear Algebra Appl., 2018

Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems.

D. Andrew Brown, Arvind K. Saibaba, Sarah Vallélian

SIAM/ASA J. Uncertain. Quantification, 2018

Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems.

Ahmed Attia, Alen Alexanderian, Arvind K. Saibaba

CoRR, 2018

2017

Generalized Hybrid Iterative Methods for Large-Scale Bayesian Inverse Problems.

Julianne Chung, Arvind K. Saibaba

SIAM J. Sci. Comput., 2017

Multipreconditioned Gmres for Shifted Systems.

Tania Bakhos, Peter K. Kitanidis, Scott Ladenheim, Arvind K. Saibaba, Daniel B. Szyld

SIAM J. Sci. Comput., 2017

Randomized matrix-free trace and log-determinant estimators.

Arvind K. Saibaba, Alen Alexanderian, Ilse C. F. Ipsen

Numerische Mathematik, 2017

2016

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

Arvind K. Saibaba

SIAM J. Matrix Anal. Appl., 2016

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

Arvind K. Saibaba, Jonghyun Lee, Peter K. Kitanidis

Numer. Linear Algebra Appl., 2016

2015

Fast Algorithms for Hyperspectral Diffuse Optical Tomography.

Arvind K. Saibaba, Misha Elena Kilmer, Eric L. Miller, Sergio Fantini

SIAM J. Sci. Comput., 2015

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

Tania Bakhos, Arvind K. Saibaba, Peter K. Kitanidis

J. Comput. Phys., 2015

2014

A fast Kalman filter for time-lapse electrical resistivity tomography.

Arvind K. Saibaba, Eric L. Miller, Peter K. Kitanidis

Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, 2014

2013

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

Arvind K. Saibaba, Tania Bakhos, Peter K. Kitanidis

SIAM J. Sci. Comput., 2013