Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.
Explores the convergence of Langevin Monte Carlo algorithms under different growth rates and smoothness conditions, emphasizing fast convergence for a wide class of potentials.
