U.T. Austin

The talk will describe how ideas from random matrix theory can be leveraged to effectively, accurately, and reliably solve important problems that arise in data analytics and large scale matrix computations. We will focus in particular on accelerated techniques for computing low rank approximations to matrices. These techniques rely on randomised embeddings that reduce the effective dimensionality of intermediate steps in the computation. The resulting algorithms are particularly well suited for processing very large data sets.

The algorithms described are supported by rigorous analysis that depends on probabilistic bounds on the singular values of rectangular Gaussian matrices. The talk will briefly review some representative results.

Note: There is a related talk in the Computational Mathematics and Applications seminar on Thursday Feb 27, at 14:00 in L4. There, the ideas introduced in this talk will be extended to the problem of solving large systems of linear equations.