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摘要翻译：
我们考虑了一个公司寻求使用个性化定价在有限的销售范围内向不同类型的消费者销售一个外生给定的产品库存的问题。我们假设到达的消费者的类型是可以观察到的，但与每种类型相关的需求函数最初是未知的。该公司为每种类型动态设定个性化价格，并试图在一个季节内最大限度地增加收入。我们提供了一个学习算法，当需求和容量成比例时，该算法接近最优。该算法利用问题的原对偶形式，显式学习对偶最优解。它允许算法克服维数的诅咒（后悔率与类型数量无关），并为资源约束下的学习问题提供了新的算法设计。
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英文标题：
《A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with
an Inventory Constraint》
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作者：
Ningyuan Chen, Guillermo Gallego
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最新提交年份：
2021
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
We consider the problem of a firm seeking to use personalized pricing to sell an exogenously given stock of a product over a finite selling horizon to different consumer types. We assume that the type of an arriving consumer can be observed but the demand function associated with each type is initially unknown. The firm sets personalized prices dynamically for each type and attempts to maximize the revenue over the season. We provide a learning algorithm that is near-optimal when the demand and capacity scale in proportion. The algorithm utilizes the primal-dual formulation of the problem and learns the dual optimal solution explicitly. It allows the algorithm to overcome the curse of dimensionality (the rate of regret is independent of the number of types) and sheds light on novel algorithmic designs for learning problems with resource constraints.
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PDF链接：
https://arxiv.org/pdf/1812.09234
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