59 citations · 220 across the 52 of their papers we have counts for
6 papers · 1 filter
Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions
William Reese, Arvind K. Saibaba, Jonghyun Lee
There are several challenges associated with inverse problems in which we seek to reconstruct a piecewise constant field, and which we model using multiple level sets. Adopting a B…
Randomized algorithms for rounding in the Tensor-Train format
Hussam Al Daas, Grey Ballard, Paul Cazeaux +5
The Tensor-Train (TT) format is a highly compact low-rank representation for high-dimensional tensors. TT is particularly useful when representing approximations to the solutions o…
Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions
Arvind K. Saibaba, Rachel Minster, Misha E. Kilmer
In this paper, we introduce a method for multivariate function approximation using function evaluations, Chebyshev polynomials, and tensor-based compression techniques via the Tuck…
Efficient edge-preserving methods for dynamic inverse problems
Mirjeta Pasha, Arvind K. Saibaba, Silvia Gazzola +2
We consider efficient methods for computing solutions to dynamic inverse problems, where both the quantities of interest and the forward operator (measurement process) may change a…
Structured Matrix Approximations via Tensor Decompositions
Misha E. Kilmer, Arvind K. Saibaba
We provide a computational framework for approximating a class of structured matrices; here, the term structure is very general, and may refer to a regular sparsity pattern (e.g.,…
Approximating monomials using Chebyshev polynomials
Arvind K. Saibaba
This paper considers the approximation of a monomial over the interval by a lower-degree polynomial. This polynomial approximation can be easily computed analyticall…