Improved Dimension Dependence for the Metropolis-Adjusted Langevin Algorithm and Lower Bounds for Sampling
berkeley.edu
Improved Dimension Dependence for the Metropolis-Adjusted Langevin Algorithm and Lower Bounds for Sampling
The popular diffusion scaling heuristic predicts that the mixing time of the Metropolis-Adjusted Langevin Algorithm (MALA) should scale as d^{1/3}, where d is the dimension. However, the diffusion scaling limit requires stringent assumptions and is asymptotic in nature. In the first part of my talk, I will discuss our work on improving the ...
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