KELLER Colloquium in Computing & Mathematical Sciences

Low-rank matrix approximations, such as partial spectral decompositions or principal component analysis (PCA), play a central role in data analysis and scientific computing. The talk will describe a set of randomized algorithms for efficiently computing such approximations. These techniques exploit modern computational architectures more fully than classical methods and enable certain computations involving massive data sets. We will also describe recent work on how randomization can be used to accelerate the  computation of full (as opposed to partial) matrix factorizations, and the compression of rank structured matrices.