Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo
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For the task of sampling from a density π∝exp(-V) on ℝ^d, where V is possibly non-convex but L-gradient Lipschitz, we prove that averaged Langevin Monte Carlo outputs a sample with ε-relative Fisher information after O( L^2 d^2/ε^2) iterations. This is the sampling analogue of complexity bounds for finding an ε-approximate first-order stationary points in non-convex optimization and therefore constitutes a first step towards the general theory of non-log-concave sampling. We discuss numerous extensions and applications of our result; in particular, it yields a new state-of-the-art guarantee for sampling from distributions which satisfy a Poincaré inequality.
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