Event Date
Speaker: Patrice Koehl, Professor, Computer Science / Genome Center, UC Davis
Title: "Optimal transports at finite temperature"
Abstract: Optimal transport (OT) has become a discipline by itself that offers solutions to a wide range of theoretical problems in probability and mathematics. Despite its appealing theoretical properties, solving the OT problem involves the resolution of a linear program whose computational cost can quickly become prohibitive whenever the size of the problem exceeds a few hundred points.
The recent introduction of entropy regularization, however, has led to the development of fast algorithms for solving an approximate OT problem. The successes of those algorithms have resulted in a popularization of the applications of OT in several applied fields such as imaging sciences and machine learning, and in data sciences in general. Problems remain, however, as to the numerical convergence of those regularized approximations towards the actual OT solution. In addition, the physical meaning of this regularization is unclear. In this talk, I will describe a novel approach to solving the discrete balanced as well as unbalanced OT problems using techniques adapted from statistical physics. I will illustrate applications of this framework to the problem of 2D image comparison as well as to the problem of comparing three dimensional shapes.
Seminar Date/Time: Thursday October 8th, 4:10pm
This seminar will be delivered remotely via Zoom. To access the Zoom meeting for this seminar, please contact the instructor Fushing Hsieh (fhsieh@ucdavis.edu) or Pete Scully (pscully@ucdavis.edu) for the meeting ID and password, stating your affiliation.