Please note: This seminar will take place in DC 2568 and online.
Lap Chi Lau, Professor
David R. Cheriton School of Computer Science
Spectral sparsification and discrepancy minimization are two well-studied areas that are closely related. Building on recent connections between these two areas, we generalize the “deterministic discrepancy walk” framework by Pesenti and Vladu [SODA~23] for vector discrepancy to matrix discrepancy, and use it to give a simpler proof of the matrix partial coloring theorem of Reis and Rothvoss [SODA~20]. Moreover, we show that this matrix discrepancy framework provides a unified approach for various spectral sparsification problems, from stronger notions including unit-circle approximation and singular-value approximation to weaker notions including graphical spectral sketching and effective resistance sparsification. In all of these applications, our framework produces improved results with a simpler and deterministic analysis.
Joint work with Robert Wang and Hong Zhou.
To attend this seminar, please go to DC2568. You can also attend this seminar online.