Data-Driven Structured Matrix Approximation by Separation and Hierarchy

Applied and Computational Mathematics Seminar
Monday, February 24, 2020 - 1:55pm for 1 hour (actually 50 minutes)
Skiles 005
Dr. Difeng Cai – Emory University, Department of Mathematics
Sung Ha Kang

The past few years have seen the advent of big data, which brings unprecedented convenience to our daily life. Meanwhile, from a computational point of view, a central question arises amid the exploding amount of data: how to tame big data in an economic and efficient way. In the context of matrix computations, the question consists in the ability to handle large dense matrices. In this talk, I will first introduce data-sparse hierarchical representations for dense matrices. Then I will present recent development of a new data-driven algorithm called SMASH to operate dense matrices efficiently in the most general setting. The new method not only outperforms existing algorithms but also works in high dimensions. Various experiments will be provided to justify the advantages of the new method.