16 citations · 56 across the 12 of their papers we have counts for
4 papers · 1 filter
Sampled Limited Memory Methods for Massive Linear Inverse Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to ne…
Sampled Tikhonov Regularization for Large Linear Inverse Problems
J. Tanner Slagel, Julianne Chung, Matthias Chung +2
In this paper, we investigate iterative methods that are based on sampling of the data for computing Tikhonov-regularized solutions. We focus on very large inverse problems where a…
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant c…
Optimal low-rank approximations of Bayesian linear inverse problems
Alessio Spantini, Antti Solonen, Tiangang Cui +3
In the Bayesian approach to inverse problems, data are often informative, relative to the prior, only on a low-dimensional subspace of the parameter space. Significant computationa…