Normalizing constants of log-concave densities

Abstract

We derive explicit bounds for the computation of normalizing constants $Z$ for log-concave densities $\pi =\exp (-U)/Z$ with respect to the Lebesgue measure on ${\mathbb{R}}^d$. Our approach relies on a Gaussian annealing combined with recent and precise bounds on the Unadjusted Langevin Algorithm (High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm, A. Durmus and E. Moulines). Polynomial bounds in the dimension $d$ are obtained with an exponent that depends on the assumptions made on $U$. The algorithm also provides a theoretically grounded choice of the annealing sequence of variances. A numerical experiment supports our findings. Results of independent interest on the mean squared error of the empirical average of locally Lipschitz functions are established.