A First-Order Learning Algorithm for Online Resource Allocation with Constant Regret
A primal first-order learning policy that, in each period, performs one gradient ascent update of the action coordinates associated with the current request type, achieving expected additive regret relative to the hindsight optimum with a bound independent of the horizon.
Meng-Long Li, Jia-Wei Zhang
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