Abstract
Bounded Regret for LP-Based Revenue-Management ProblemsIn “An Improved Analysis of LP-Based Control for Revenue Management,” Chen, Li, and Ye study a class of quantity-based network revenue-management problems. The authors consider a stochastic setting where all the orders are i.i.d. sampled and the customers are of finite type. They focus on the classic LP-based adaptive algorithm and consider regret as the performance measure. They found that when the underlying LP is nondegenerate, the algorithm achieves a problem-dependent regret upper bound that is independent of the horizon/number of time periods T; when the underlying LP is degenerate, the algorithm achieves a tight regret upper bound that scales on the order of T log(T) and matches the lower bound up to a logarithmic order.
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