IAM-PIMS-MITACS Distinguished Colloquium: Prof. Joel A. Tropp
Speakers
Details
Computer scientists have long known that randomness can be used to
improve the performance of algorithms. A familiar application is the
process of dimension reduction, in which a random map transports data
from a high-dimensional space to a lower-dimensional space while
approximately preserving some geometric properties. By operating with
the compact representation of the data, it is theoretically possible to
produce approximate solutions to certain large problems very
efficiently. Recently, it has been observed that dimension reduction has
powerful applications in numerical linear algebra and numerical
analysis. This talk provides a high-level introduction to randomized
methods for computing standard matrix approximations, and it summarizes a
new analysis that offers (nearly) optimal bounds on the performance of
these methods. In practice, the techniques are so effective that they
compete with – or even outperform – classical algorithms. Since matrix
approximations play a ubiquitous role in areas ranging from information
processing to scientific computing, it seems certain that randomized
algorithms will eventually supplant the standard methods in some
application domains. This is joint work with Gunnar Martinsson and
Nathan Halko
Additional Information
Location: LSK 301
For more information please visit Institute of Applied Mathematics
Prof. Joel A. Tropp