Guanghao Ye (Massachusetts Institute of Technology)

Time:

• 10:00-11:00 (Time in Beijing)
• January 19, 2023 (Friday)

Venue:

518, Research Building 4

Abstract:

We discuss how to incorporate data structures based on nested dissection into the interior point methods (IPM) framework to obtain efficient linear programming (LP) solvers for a number of problems, including planar min-cost flow, planar k-multicommodity flow, LPs with low treewidth, and general separable LPs. Based on joint papers with Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Lawrence Li, Richard Peng, and Sushant Sachdeva.

Speaker Bio:

Guanghao Ye is a PhD student at MIT EECS, advised by Jon Kelner. He previously earned his Bachelor’s and Master’s degrees at the University of Washington, under the supervision of Yin Tat Lee. His research broadly focuses on theoretical computer science, particularly in the algorithmic aspects of optimization and its applications.