KELLER Colloquium in Computing & Mathematical Sciences

Monday October 29, 2018 4:00 PM

Randomized algorithms for accelerating matrix computations

Speaker: Gunnar Martinsson, University of Oxford, Visiting Professor of Applied Mathematics, University of Colorado at Boulder
Location: Annenberg 105

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 theĀ  computation of full (as opposed to partial) matrix factorizations, and the compression of rank structured matrices.

Series H. B. Keller Colloquium Series

Contact: Diane Goodfellow diane@cms.caltech.edu