面对库存遮蔽需求的动态定价，提出最优算法。
Dynamic Pricing with Adversarially-Censored Demands
- 基于导数乐观估计设计定价策略
- 在对抗性库存下实现√T阶最优后悔值
- 适合研究在线决策与隐私保护场景
我们研究一个在线动态定价问题：每个时段t=1,2,…,T的潜在需求是随机的，且依赖于价格。但在每个时段开始时，系统会施加一个易腐库存限制，若潜在需求超过库存水平则被截断。为解决此问题，我们提出一种基于导数乐观估计的定价算法。即使在对抗性库存序列下，该算法仍能实现˜O(√T)的最优后悔界。研究成果推进了带截断反馈的在线决策领域的理论边界，提供了对抗观测下的理论最优解。
原文摘要 · Abstract (English)
We study an online dynamic pricing problem where the potential demand at each time period $t=1,2,\ldots, T$ is stochastic and dependent on the price. However, a perishable inventory is imposed at the beginning of each time $t$, censoring the potential demand if it exceeds the inventory level. To address this problem, we introduce a pricing algorithm based on the optimistic estimates of derivatives. We show that our algorithm achieves $\tilde{O}(\sqrt{T})$ optimal regret even with adversarial inventory series. Our findings advance the state-of-the-art in online decision-making problems with censored feedback, offering a theoretically optimal solution against adversarial observations.
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