A conference on numerical analysis and scientific computing
for graduate students and postdocs from the Mid-Atlantic region.
The emergence of massive data sets, over the past fifteen or so years, has lead 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).