STOR Colloquium: Ilse Ipsen, North Carolina State University
North Carolina State University
Randomized Algorithms for Matrix Computations
The emergence of massive data sets, over the past fifteen or so years, has led to the development of Randomized Numerical Linear Algebra. Fast and accurate randomized matrix algorithms are being designed for applications like machine learning, population genomics, astronomy, nuclear engineering, and optimal experimental design.
We give a flavour of randomized algorithms for the solution of least squares/regression problems and, if time permits, for the computation of logdeterminants. Along the way we illustrate important concepts from numerical analysis (conditioning and pre-conditioning) and statistics (sampling and leverage scores).