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- W4320536398 abstract "We study the classical Network Revenue Management (NRM) problem with accept/reject decisions and $T$ IID arrivals. We consider a distributional form where each arrival must fall under a finite number of possible categories, each with a deterministic resource consumption vector, but a random value distributed continuously over an interval. We develop an online algorithm that achieves $O(log^2 T)$ regret under this model, with no further assumptions. We develop another online algorithm that achieves an improved $O(log T)$ regret, with only a second-order growth assumption. To our knowledge, these are the first results achieving logarithmic-level regret in a continuous-distribution NRM model without further non-degeneracy assumptions. Our results are achieved via new techniques including: a new method of bounding myopic regret, a semi-fluid relaxation of the offline allocation, and an improved bound on the dual convergence." @default.
- W4320536398 created "2023-02-14" @default.
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- W4320536398 date "2022-10-14" @default.
- W4320536398 modified "2023-09-29" @default.
- W4320536398 title "Degeneracy is OK: Logarithmic Regret for Network Revenue Management with Indiscrete Distributions" @default.
- W4320536398 doi "https://doi.org/10.48550/arxiv.2210.07996" @default.
- W4320536398 hasPublicationYear "2022" @default.
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