Poincare Inequality for Local Log-Polyak-\L ojasiewicz Measures: Non-asymptotic Analysis in Low-temperature Regime
Yun Gong, Niao He, Zebang Shen

TL;DR
This paper establishes a Poincaré inequality for a class of non-convex measures with local minima forming a connected manifold, leading to non-asymptotic convergence rates for Langevin dynamics in low-temperature regimes.
Contribution
It introduces a novel analysis of log-Polyak-Lojasiewicz measures with connected local minima, providing epsilon-independent bounds and convergence guarantees for Langevin dynamics.
Findings
Poincaré constant lower bounded independently of epsilon
Langevin dynamics converges at rate O(1/) in low-temperature regime
Local minima set forms a compact embedded submanifold
Abstract
Potential functions in highly pertinent applications, such as deep learning in over-parameterized regime, are empirically observed to admit non-isolated minima. To understand the convergence behavior of stochastic dynamics in such landscapes, we propose to study the class of log-P{\L} measures , where the potential satisfies a local Polyak-{\L}ojasiewicz (P{\L}) inequality, and its set of local minima is provably connected. Notably, potentials in this class can exhibit local maxima and we characterize its optimal set to be a compact embedding submanifold of without boundary. The non-contractibility of distinguishes our function class from the classical convex setting topologically. Moreover, the embedding structure induces a naturally defined Laplacian-Beltrami operator on , and we show that its first…
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Taxonomy
TopicsStochastic processes and financial applications · Nonlinear Partial Differential Equations · Stochastic processes and statistical mechanics
