NLP采样：结合MCMC和NLP方法进行多样化的受限采样
NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Sampling
AI总结：
本文提出NLP采样框架，结合MCMC和NLP方法，解决多样化的受限采样问题，并通过实验验证其有效性。
摘要：
在许多领域中，生成多样化的样本并满足严格约束是一个核心挑战。通过本工作，我们旨在提供一个整合视角和框架，将MCMC、约束优化以及机器人学等领域的技术方法相结合，并通过实证评估获得这些方法的优势洞察。我们提出NLP采样作为一般性问题表述，提出一组重启双阶段方法作为整合跨领域方法的框架，并在分析和机器人操作规划问题上进行评估。此外，我们还提供了若干概念性讨论，例如拉格朗日参数的作用、全局采样以及扩散NLP和相应基于模型的去噪采样器的概念。
英文摘要：
Generating diverse samples under hard constraints is a core challenge in many areas. With this work we aim to provide an integrative view and framework to combine methods from the fields of MCMC, constrained optimization, as well as robotics, and gain insights in their strengths from empirical evaluations. We propose NLP Sampling as a general problem formulation, propose a family of restarting two-phase methods as a framework to integrated methods from across the fields, and evaluate them on analytical and robotic manipulation planning problems. Complementary to this, we provide several conceptual discussions, e.g. on the role of Lagrange parameters, global sampling, and the idea of a Diffused NLP and a corresponding model-based denoising sampler.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
