26 citations · 26 across the 7 of their papers we have counts for
7 papers
Blind error estimation for CUR approximation
Lorenzo Lazzarino, Katherine J. Pearce, Nathaniel Pritchard
Low-rank approximation is a fundamental tool for scalable matrix computations. While such approximations have classically been formed via the truncated SVD, recent advances in rand…
Efficient error estimators for Generalized Nyström
Lorenzo Lazzarino, Katherine J. Pearce, Nathaniel Pritchard
Randomized algorithms in numerical linear algebra have proven to be effective in ameliorating issues of scalability when working with large matrices, efficiently producing accurate…
IterativeCUR: Large Rank-Adaptive Approximation From a Small Recycled Sketch
Nathaniel Pritchard, Taejun Park, Yuji Nakatsukasa +1
The computation of accurate low-rank matrix approximations is central to improving the scalability of various techniques in machine learning, uncertainty quantification, and contro…
Scientific Applications Leveraging Randomized Linear Algebra
Vivak Patel, D. Adrian Maldonado, Maksim Melnichenko +5
This report showcases the role of, and future directions for, the field of Randomized Numerical Linear Algebra (RNLA) in a selection of scientific applications. These applications…
Towards Practical Large-scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification
Nathaniel Pritchard, Vivak Patel
As the scale of problems and data used for experimental design, signal processing and data assimilation grow, the oft-occuring least squares subproblems are correspondingly growing…
Solving, Tracking and Stopping Streaming Linear Inverse Problems
Nathaniel Pritchard, Vivak Patel
In large-scale applications including medical imaging, collocation differential equation solvers, and estimation with differential privacy, the underlying linear inverse problem ca…
Demarcating Geographic Regions using Community Detection in Commuting Networks with Significant Self-Loops
Mark He, Joseph Glasser, Nathaniel Pritchard +2
We develop a method to identify statistically significant communities in a weighted network with a high proportion of self-looping weights. We use this method to find overlapping a…