In this paper, we study a general online linear programming problem whoseformulation encompasses many practical dynamic resource allocation problems,including internet advertising display applications, revenue management,various routing, packing, and auction problems. We propose a model, which undermild assumptions, allows us to design near-optimal learning-based onlinealgorithms that do not require the a priori knowledge about the total number ofonline requests to come, a first of its kind. We then consider two variants ofthe problem that relax the initial assumptions imposed on the proposed model.
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