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Applications of the Local Weak Convergence Method to Random Graph Problems

Phase Transitions in Computation and Reconstruction
March 10, 2005 11:00 AM to 11:20 AM

Speakers:
Gamarnik, David

VMath - The Next Generation for Math Lectures on Streaming Video

Abstract:

Local Weak Convergence method (LWC) exploits local structure (typically a tree) of a large
random combinatorial object and leads to a complete asymptotic solution to several optimization
problems on random graphs. The method reduces the original problem into the problem of finding
fixed points of a certain distributional operator. We show that when the fixed point of the second iterate of the distributional operator is unique, it determines asymptotically the value of the underlying combinatorial optimization problem.
We demonstrate this on a problem of finding maximum weighted independent set in a sparse random graph. We further show that the uniqueness of the fixed point of the distributional operator is tightly related to the correlation decay and high temperature/low regimes in statistical physics models.
Joint work with Tomasz Nowicki and Grzegorz Swirszcz

Keywords:

Phase Transitions; computation;reconstruction

Lecture #10875

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