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Speeding up Langevin Dynamics by Mixing

Offered By: Society for Industrial and Applied Mathematics via YouTube

Tags

Partial Differential Equations Courses Applied Mathematics Courses Monte Carlo Methods Courses Numerical Analysis Courses Statistical Mechanics Courses Computational Physics Courses Stochastic Differential Equation Courses Langevin Dynamics Courses

Course Description

Overview

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Explore the intricacies of Langevin Dynamics in this seminar presented by Gautam Iyer from Carnegie Mellon University. Delve into the overdamped Langevin equation, a stochastic differential equation governing particle dynamics driven by potential gradients and random fluctuations. Discover how this equation's stationary distribution forms the basis of the Langevin Monte Carlo algorithm for sampling. Learn about the challenges posed by potentials with multiple local minima and the exponential convergence times they can cause. Investigate a novel method that introduces a "mixing drift" to dramatically reduce convergence time to polynomial order. Gain insights into this collaborative research effort with Alex Christie, Yuanyuan Feng, and Alexei Novikov, aimed at speeding up Langevin Dynamics through innovative mixing techniques.

Syllabus

Seminar In the Analysis and Methods of PDE (SIAM PDE): Gautam Iyer


Taught by

Society for Industrial and Applied Mathematics

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