In contextual dynamic pricing, a seller sequentially prices goods based on contextual information.

Since the provider's goal of maximizing revenue can only be achieved

We propose a randomized dynamic pricing policy based on a variant of the Online Newton Stepalgorithm (ONS)thatachievesaO(d T log(T))regretguarantee underan adversarial arrival model.

We consider the Continuum Bandit problem where the goal is to find the optimal action under an unknown reward function, with an additional monotonicity constraint (or, markdown constraint) that requires that the action sequence be non-increasing. This problem faithfully models a natural single-product dynamic pricing problem, called markdown pricing, where the objective is to adaptively reduce the price over a finite sales horizon to maximize expected revenues. Jia et al '21 and Chen '21 independently showed a tight $T^{3/4}$ regret bound over $T$ rounds under *minimal* assumptions of unimodality and Lipschitzness in the reward (or, revenue) function. This bound shows that the demand learning in markdown pricing is harder than unconstrained (i.e., without the monotonicity constraint) pricing under unknown demand which suffers regret only of the order of $T^{2/3}$ under the same assumptions (Kleinberg '04). However, in practice the demand functions are usually assumed to have certain functional forms (e.g.

Disney strikes a deal with OpenAI, a few trillion dollar IPOs are brewing for 2026, and dynamic pricing is everywhere it seems. Please enable javascript to get your Slate Plus feeds. If you can't access your feeds, please contact customer support. Check your phone for a link to finish setting up your feed. Please enter a valid phone number.