School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, USA
Posts Tagged ‘EricVigoda’
Eric Vigoda, Analyzing Markov Chains using Belief Propagation
Wednesday, December 7th, 2016Analyzing Markov Chains using Belief Propagation
Eric Vigoda
School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, USA
School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, USA
2016/12/16 Friday 3PM-4PM
For counting weighted independent sets weighted by a parameter λ (known as the hard-core model) there is a beautiful connection between statistical physics phase transitions on infinite, regular trees and the computational complexity of approximate counting on graphs of maximum degree D. For λ below the critical point there is an elegant algorithm devised by Weitz (2006). The drawback of his algorithm is that the running time is exponential in log D. In this talk we’ll describe recent work which shows O(n log n) mixing time of the single-site Markov chain when the girth>6 and D is at least a sufficiently large constant. Our proof utilizes BP (belief propagation) to design a distance function in our coupling analysis. This is joint work with C. Efthymiou, T. Hayes, D. Stefankovic, and Y. Yin which appeared in FOCS ’16.
Eric Vigoda, Computational Phase Transitions for the Potts Model
Monday, March 30th, 2015Computational Phase Transitions for the Potts Model
Eric Vigoda
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
2015/4/14 Tue 4PM-5PM
This is a followup talk to my CS colloquium on March 2. In that talk I discussed the problems of counting independent sets and colorings. Those problems are examples of antiferromagnetic systems in which neighboring vertices prefer different assignments. In this talk we will look at ferromagnetic systems where neighboring vertices prefer the same assignment. We will focus on the ferromagnetic Potts model. We will look at the phase transitions in this model, and their connections to the complexity of associated counting/sampling problems and the performance of related Markov chains.
The talk is based on joint works with Andreas Galanis, Daniel Stefankovic, and Linji Yang.
The talk is based on joint works with Andreas Galanis, Daniel Stefankovic, and Linji Yang.
[Lecture Series] Eric Vigoda, Approximating the Permanent
Friday, February 27th, 2015FYI (Lecture Series)
Approximating the Permanent
Eric Vigoda
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
2015/3/11 Wed 9AM-10:15AM (E3-1, Room 1501)
I’ll explain the canonical paths method for bounding the mixing time of a
Markov chain. I’ll show the analysis of a Markov chain for generating a
random matching of a graph and its extension for the estimating the
number of perfect matchings of a bipartite graph.
Markov chain. I’ll show the analysis of a Markov chain for generating a
random matching of a graph and its extension for the estimating the
number of perfect matchings of a bipartite graph.
[Lecture Series] Eric Vigoda, Introduction to Markov Chain Monte Carlo Methods
Friday, February 27th, 2015FYI (Lecture Series)
Introduction to Markov Chain Monte Carlo Methods
Eric Vigoda
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
2015/3/6 Fri 9AM-10:15AM (E3-1, Room 1501)
I’ll introduce Markov chains and their key properties. Then I’ll explain the PageRank algorithm of Brin and Page which is the basis of the Google search engine.