用无监督学习提升组合优化搜索效率，解决经典难题
Unsupervised Learning for Quadratic Assignment
- 基于排列损失的非自回归方法，直接从问题实例学习
- 在二次分配问题上持续提升解的质量，验证有效性
- 模型可跨规模和密度泛化，适合通用优化场景
我们提出PLUME搜索，一种数据驱动的组合优化搜索框架，通过无监督学习提升搜索效率。与监督或强化学习不同，PLUME搜索利用基于排列的损失函数，以非自回归方式直接从问题实例中学习。我们在二次分配问题（QAP）上评估其性能，该问题是基础的NP难问题，涵盖多种组合优化任务。实验结果表明，PLUME搜索能持续提升解的质量。此外，我们研究了模型的泛化能力，发现所学模型可在不同密度和规模间有效泛化。
原文摘要 · Abstract (English)
We introduce PLUME search, a data-driven framework that enhances search efficiency in combinatorial optimization through unsupervised learning. Unlike supervised or reinforcement learning, PLUME search learns directly from problem instances using a permutation-based loss with a non-autoregressive approach. We evaluate its performance on the quadratic assignment problem, a fundamental NP-hard problem that encompasses various combinatorial optimization problems. Experimental results demonstrate that PLUME search consistently improves solution quality. Furthermore, we study the generalization behavior and show that the learned model generalizes across different densities and sizes.
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