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We provide near-optimal, polynomial-time algorithms for pricing n items to optimize revenue against a unit-demand buyer whose values are independent from known distributions. For any chosen ϵ>0 and values in [0,1], our algorithm's revenue is optimal up to an additive ϵ. For values sampled from monotone hazard rate (MHR) or regular distributions, we achieve a (1−ϵ)-fraction of the optimal revenue...
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