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Revenue management, used in industries such as airlines, hotels, and retail, requires dynamic decisions on pricing and product assortments, whereas resources such as seats or inventory cannot be replenished. Traditional approaches often struggle with complexity or weak theoretical guarantees. The authors propose a primal-dual learning framework that combines optimization with machine learning\u2019s upper confidence bound method. Their approach achieves near-optimal regret bounds, remaining computationally efficient even in large or infinite decision spaces. Applications include dynamic assortment selection, network revenue management with generalized linear demand, and joint pricing\u2013assortment optimization. Numerical experiments show the methods consistently outperform benchmarks, offering practical, scalable solutions for data-driven industries.<\/jats:p>","DOI":"10.1287\/opre.2021.0483","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T16:43:38Z","timestamp":1764953018000},"page":"825-839","source":"Crossref","is-referenced-by-count":1,"title":["A Primal-Dual Approach Toward Resource-Constrained Revenue Management with Demand Learning and Large Action Space"],"prefix":"10.1287","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0380-0797","authenticated-orcid":false,"given":"Sentao","family":"Miao","sequence":"first","affiliation":[{"name":"Leeds School of Business, University of Colorado Boulder, Boulder, Colorado 80309"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9410-0392","authenticated-orcid":false,"given":"Yining","family":"Wang","sequence":"additional","affiliation":[{"name":"Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4988-6028","authenticated-orcid":false,"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Leonard N. 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