University of Texas at Austin

Past Event: Babuška Forum

Finding structure with randomness

Joel Tropp, Professor, CalTech

11 – 12PM
Friday Apr 16, 2021

Zoom Meeting

Abstract

Over the last 20 years, randomized algorithms have revolutionized the field of matrix computations. These new methods can efficiently and robustly solve huge linear algebra problems that were previously inaccessible.

This talk introduces the randomized singular value decomposition (SVD) algorithm, perhaps the most widely used method that has emerged from this research program. The randomized SVD algorithm supports large-scale linear regression, principal component analysis, proper orthogonal decomposition, and many other methods for data reduction and summarization. The talk offers a high-level view of how randomness facilitates the SVD computation, the kinds of theoretical guarantees it allows, and some applications in science and engineering.

For more information, see the papers arXiv:0909.4061 and arXiv:2002.01387.

Biography

Joel A. Tropp is Steele Family Professor of Applied and Computational Mathematics at Caltech. His research centers on data science, applied mathematics, numerical algorithms, and random matrix theory. He attained the Ph.D. degree in Computational Applied Mathematics at the University of Texas at Austin in 2004, and he joined Caltech in 2007. Prof. Tropp won the PECASE in 2008, and he was recognized as a Highly Cited Researcher in Computer Science each year from 2014–2018. He is co-founder and Section Editor of the SIAM Journal on Mathematics of Data Science (SIMODS), and he was co-chair of the inaugural 2020 SIAM Conference on the Mathematics of Data Science. Prof. Tropp was elected SIAM Fellow in 2019 and IEEE Fellow in 2020.

Finding structure with randomness

Event information

Date
11 – 12PM
Friday Apr 16, 2021
Location Zoom Meeting
Hosted by Anna Yesypenko