Linear Program-Based Approximation for Personalized Reserve Prices

We study the problem of computing data-driven personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, input is a data set that contains submitted bids n buyers auctions, and to return r maximize revenue earned these by running with r. For this problem, which known be NP complete, we present novel linear program (LP) formulation rounding procedure, achieves 0.684 approximation. This improves over [Formula: see text]-approximation algorithm from Roughgarden Wang. show our analysis tight for procedure. also bound integrality gap LP, shows it impossible design an yields approximation factor larger than 0.828 respect LP. paper was accepted Chung Piaw Teo, Management Science Special Section Data-Driven Prescriptive Analytics.