59 citations · 220 across the 52 of their papers we have counts for
10 papers · 1 filter
Parametric Hierarchical Matrix Approximations to Kernel Matrices
Abraham Khan, Chao Chen, Vishwas Rao +1
Kernel matrices are ubiquitous in computational mathematics, often arising from applications in machine learning and scientific computing. In two or three spatial or feature dimens…
Adaptive Randomized Tensor Train Rounding using Khatri-Rao Products
Hussam Al Daas, Grey Ballard, Laura Grigori +3
Approximating a tensor in the tensor train (TT) format has many important applications in scientific computing. Rounding a TT tensor involves further compressing a tensor that is a…
Many (most?) column subset selection criteria are NP hard for a few columns
Ilse C. F. Ipsen, Arvind K. Saibaba
We consider a variety of criteria for selecting k representative columns from a real mxn matrix A, when sufficiently few columns are required, i.e., 1<= k<= min{rank(A), m/3}. The…
A tensor-based dynamic mode decomposition based on the -product
Arvind K. Saibaba, Misha E. Kilmer, Khalil Hall-Hooper +2
Dynamic mode decomposition (DMD) is a data-driven method for estimating the dynamics of a discrete dynamical system. This paper proposes a tensor-based approach to DMD for applicat…
Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory
Arvind K. Saibaba, Ilse C. F. Ipsen
We consider matrices that depend, possibly nonlinearly, on a parameter from a compact parameter space . We…
Improved Analysis of Khatri-Rao Random Projections and Applications
Arvind K. Saibaba, Bhisham Dev Verma, Grey Ballard
Randomization has emerged as a powerful set of tools for large-scale matrix and tensor decompositions. Randomized algorithms involve computing sketches with random matrices. A prev…
Structured Column Subset Selection for Bayesian Optimal Experimental Design
Hugo Díaz, Arvind K. Saibaba, Srinivas Eswar +2
We consider optimal experimental design (OED) for Bayesian inverse problems, where the experimental design variables have a certain multiway structure. Given different experime…
Bridging the Gap Between Deterministic and Probabilistic Approaches to State Estimation
Lev Kakasenko, Alen Alexanderian, Mohammad Farazmand +1
We consider the problem of state estimation from limited discrete and noisy measurements. In particular, we focus on modal state estimation, which approximates the unknown state of…
A control-oriented approach to optimal sensor placement
Madhusudan Madhavan, Alen Alexanderian, Arvind K. Saibaba +2
We propose a control-oriented optimal experimental design (cOED) approach for linear PDE-constrained Bayesian inverse problems. In particular, we consider optimal control problems…
Optimal sensor placement under model uncertainty in the weak-constraint 4D-Var framework
Alen Alexanderian, Hugo Díaz, Vishwas Rao +1
In data assimilation, the model may be subject to uncertainties and errors. The weak-constraint data assimilation framework enables incorporating model uncertainty in the dynamics…