*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Atlanta, GA | Posted: November 4, 2019
Summary Sentence:
Lectures by Nima Anari on Rapidly Mixing Random Walks via Log-concave Polynomials
Full Summary:
No summary paragraph submitted.
Nima Anari TalkA fundamental tool used in sampling, counting, and inference problems is the Markov Chain Monte Carlo method, which uses random walks to solve computational problems. The main parameter defining the efficiency of this method is how quickly the random walk mixes (converges to the stationary distribution). Prof. Anari from Stanford University gave a series of lectures on using log concave polynomials in analysis of random walks to sample and count bases of a matroid. The results have resolved multiple conjectures in combinatorics, probability theory and algorithms.
