提出新定价策略，在复杂需求下实现最优动态定价。
Minimax-Optimal Semiparametric Contextual Dynamic Pricing with Multimodal Revenue
- 结合方向性预估与局部多项式学习，自适应调整价格决策。
- 在任意协变量序列下达到最小最大最优率，误差随平滑度降低。
- 适用于非唯一最优价、非凸收益场景，适合实际商业系统部署。
研究任意协变量序列下的上下文动态定价问题，购买量为有界且可能非二值。需求服从半参数盈余指数模型，包含未知线性估值参数和未知霍尔德光滑响应函数。不假设收益的凹性或强单峰性，允许最优价格不唯一。提出一种基于方向性预估校正的分层决策划分策略，融合方向性预估、局部多项式学习、可预测数据分配与全局动作消除。预估校正消除了估值参数误差的一阶影响，而永久标签使得自适应采样下仍能保证集中性。该策略在时间跨度上达到最小最大平滑度相关率（对数因子内），且该下界已在常数上下文二值需求子类中成立。
原文摘要 · Abstract (English)
We study contextual dynamic pricing with arbitrary covariate sequences and bounded, possibly nonbinary purchase quantities. Demand follows a semiparametric surplus-index model with an unknown linear valuation parameter and an unknown Hölder-smooth response. We impose neither concavity nor strong unimodality on revenue and allow nonunique optimal prices. We develop a pilot-corrected layered decision-partitioning policy that combines directional pilot estimation, local polynomial learning, predictable data assignment, and global action elimination. Pilot correction removes the first-order effect of valuation-parameter error, while permanent labels enable concentration under adaptive sampling. The policy attains the minimax smoothness-dependent horizon rate up to logarithmic factors; a matching lower bound already holds for a constant-context binary-demand subclass.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。