Convergence of Unadjusted Langevin in High Dimensions: Delocalization of
Bias
Convergence of Unadjusted Langevin in High Dimensions: Delocalization of
Bias
The unadjusted Langevin algorithm is commonly used to sample probability distributions in extremely high-dimensional settings. However, existing analyses of the algorithm for strongly log-concave distributions suggest that, as the dimension $d$ of the problem increases, the number of iterations required to ensure convergence within a desired error in the $W_2$ …