提出加热机制解决离散采样中解质量徘徊问题，提升组合优化效率。
Reheated Gradient-based Discrete Sampling for Combinatorial Optimization
- 引入物理中的临界温度概念设计重热机制
- 在多种组合优化任务中显著优于现有采样与数据驱动方法
- 适合需要高效探索解空间的组合优化研究者
最近，基于梯度的离散采样已成为解决各类组合优化（CO）问题的高效通用求解器，性能可媲美甚至超越流行的基于数据的方法。然而，我们发现这些方法存在一个关键问题，称为“轮廓游荡”：长时间采样出目标值相近的新解，导致计算效率低下且未能充分探索潜在解空间。本文提出一种受物理学中临界温度与比热概念启发的新型重热机制，旨在克服这一局限。实验表明，该方法在多种组合优化问题上均显著优于现有基于采样的算法和数据驱动方法。
原文摘要 · Abstract (English)
Recently, gradient-based discrete sampling has emerged as a highly efficient, general-purpose solver for various combinatorial optimization (CO) problems, achieving performance comparable to or surpassing the popular data-driven approaches. However, we identify a critical issue in these methods, which we term ''wandering in contours''. This behavior refers to sampling new different solutions that share very similar objective values for a long time, leading to computational inefficiency and suboptimal exploration of potential solutions. In this paper, we introduce a novel reheating mechanism inspired by the concept of critical temperature and specific heat in physics, aimed at overcoming this limitation. Empirically, our method demonstrates superiority over existing sampling-based and data-driven algorithms across a diverse array of CO problems.
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