Gaussian Cooling and Dikin Walks: The Interior-Point Method for Logconcave Sampling

• URL: http://arxiv.org/abs/2307.12943v4
• Date: Thu, 21 Mar 2024 20:59:59 GMT
• Title: Gaussian Cooling and Dikin Walks: The Interior-Point Method for Logconcave Sampling
• Authors: Yunbum Kook, Santosh S. Vempala,
• Abstract summary: In the 1990s Nester and Nemirovski developed the Interior-Point Method (IPM) for convex optimization based on self-concordant barriers. In 2012, Kannan and Narayanan proposed the Dikin walk for uniformly sampling polytopes. Here we generalize this approach by developing and adapting IPM machinery together with the Dikin walk for poly-time sampling algorithms.
• Score: 8.655526882770742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The connections between (convex) optimization and (logconcave) sampling have been considerably enriched in the past decade with many conceptual and mathematical analogies. For instance, the Langevin algorithm can be viewed as a sampling analogue of gradient descent and has condition-number-dependent guarantees on its performance. In the early 1990s, Nesterov and Nemirovski developed the Interior-Point Method (IPM) for convex optimization based on self-concordant barriers, providing efficient algorithms for structured convex optimization, often faster than the general method. This raises the following question: can we develop an analogous IPM for structured sampling problems? In 2012, Kannan and Narayanan proposed the Dikin walk for uniformly sampling polytopes, and an improved analysis was given in 2020 by Laddha-Lee-Vempala. The Dikin walk uses a local metric defined by a self-concordant barrier for linear constraints. Here we generalize this approach by developing and adapting IPM machinery together with the Dikin walk for poly-time sampling algorithms. Our IPM-based sampling framework provides an efficient warm start and goes beyond uniform distributions and linear constraints. We illustrate the approach on important special cases, in particular giving the fastest algorithms to sample uniform, exponential, or Gaussian distributions on a truncated PSD cone. The framework is general and can be applied to other sampling algorithms.

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