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The Geometry of Efficient Nonconvex Sampling

2026/03/26 by Santosh S. Vempala, Andre Wibisono · 1 voice
Computer Science · Mathematics · #Constant (computer programming) #Convex body #Convex geometry #Generalization #Geometry and complex manifolds #Markov Chains and Monte Carlo Methods #Point processes and geometric inequalities #Polynomial #Regular polygon #Sampling (signal processing) #Set (abstract data type) #Volume (thermodynamics) #cs.DS #cs.LG #math.ST #stat.ML

paper · pdf · open access · doi:10.48550/arxiv.2603.25622

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2026/03/26 · arxiv published 2026/03/26 · openalex created_date 2026/03/28 · arxiv updated 2026/06/30 · openalex updated_date 2026/07/28

Abstract

We present an efficient algorithm for uniformly sampling from an arbitrary compact body X ⊂ ℝn from a warm start under isoperimetry and a natural volume growth condition. Our result provides a substantial common generalization of known results for convex bodies and star-shaped bodies. The complexity of the algorithm is polynomial in the dimension, the Poincaré constant of the uniform distribution on X and the volume growth constant of the set X.

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