English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider a high-dimensional dynamic pricing problem undernon-stationarity, where a firm sells products to $T$ sequentially arrivingconsumers that behave according to an unknown demand model with potentialchanges at unknown times. The demand model is assumed to be a high-dimensionalgeneralized linear model (GLM), allowing for a feature vector in $\mathbb R^d$that encodes products and consumer information. To achieve optimal revenue(i.e., least regret), the firm needs to learn and exploit the unknown GLMswhile monitoring for potential change-points. To tackle such a problem, wefirst design a novel penalized likelihood-based online change-point detectionalgorithm for high-dimensional GLMs, which is the first algorithm in thechange-point literature that achieves optimal minimax localization error ratefor high-dimensional GLMs. A change-point detection assisted dynamic pricing(CPDP) policy is further proposed and achieves a near-optimal regret of order$O(s\sqrt{\Upsilon_T T}\log(Td))$, where $s$ is the sparsity level and$\Upsilon_T$ is the number of change-points. This regret is accompanied with aminimax lower bound, demonstrating the optimality of CPDP (up to logarithmicfactors). In particular, the optimality with respect to $\Upsilon_T$ is seenfor the first time in the dynamic pricing literature, and is achieved via anovel accelerated exploration mechanism. Extensive simulation experiments and areal data application on online lending illustrate the efficiency of theproposed policy and the importance and practical value of handlingnon-stationarity in dynamic pricing.

High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection