解决优化中移动成本问题，让搜索更省力。
The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs
- 将旅行商问题融入批量贝叶斯优化，降低输入切换成本。
- 理论证明算法收敛性，实际中平均移动成本显著下降。
- 适合需频繁调整输入且成本敏感的优化场景。
本文提出一种考虑度量移动成本的贝叶斯优化框架，解决了实际应用中输入变更产生不同成本的关键挑战。该方法可无缝集成现有批量算法，在每批设计中通过求解旅行商问题来确定观测顺序。所提方法在移动成本上提供了收敛性理论保证。实验表明，该方法在保持与传统贝叶斯优化相当遗憾性能的同时，有效降低了随时间推移的平均移动成本。该框架在各类具有移动成本的多臂赌博机设置中也展现出广阔应用前景。
原文摘要 · Abstract (English)
This paper introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs. Our approach is a convenient plug-in that seamlessly integrates with the existing literature on batched algorithms, where designs within batches are observed following the solution of a Traveling Salesman Problem. The proposed method provides a theoretical guarantee of convergence in terms of movement costs for BO. Empirically, our method effectively reduces average movement costs over time while maintaining comparable regret performance to conventional BO methods. This framework also shows promise for broader applications in various bandit settings with movement costs.
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