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We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...
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each type and attempts to maximize the revenue over the season. We provide a learning algorithm that is near-optimal when … the demand and capacity scale in proportion. The algorithm utilizes the primal-dual formulation of the problem and learns … the dual optimal solution explicitly. It allows the algorithm to overcome the curse of dimensionality (the rate of regret …
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