无需预先知道需求参数，动态定价算法自动适应并优化收益。
Parameter-Adaptive Dynamic Pricing
- 通过分块需求函数域与线性带子结构实现自适应定价
- 在未知需求参数下仍保持更优的后悔界表现
- 适合电商、交通等需实时调价的场景
动态定价在电子商务和交通等领域至关重要，需在探索需求模式与利用定价策略间取得平衡。现有方法通常依赖对需求函数的精确知识，如霍尔德光滑度等级和利普希茨常数，限制了实际应用。本文提出一种无需先验参数知识的自适应方法，通过划分需求函数定义域并采用线性带子结构，设计出参数自适应动态定价（PADP）算法，有效控制后悔值，提升灵活性与实用性。该算法在后悔界方面优于现有方法，并可扩展至含上下文信息的情形。数值实验验证了其在未知需求参数下的优越性能。
原文摘要 · Abstract (English)
Dynamic pricing is crucial in sectors like e-commerce and transportation, balancing exploration of demand patterns and exploitation of pricing strategies. Existing methods often require precise knowledge of the demand function, e.g., the H{ö}lder smoothness level and Lipschitz constant, limiting practical utility. This paper introduces an adaptive approach to address these challenges without prior parameter knowledge. By partitioning the demand function's domain and employing a linear bandit structure, we develop an algorithm that manages regret efficiently, enhancing flexibility and practicality. Our Parameter-Adaptive Dynamic Pricing (PADP) algorithm outperforms existing methods, offering improved regret bounds and extensions for contextual information. Numerical experiments validate our approach, demonstrating its superiority in handling unknown demand parameters.
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