We extend the correspondences between adaptive learning algorithms and the Kalman filter to formulations with time-varying gains. Our correspondences hold exactly, in a computational implementation sense, and we discuss how they relate to previous approximate correspondences found in the literature.

We consider a choice-based, network revenue management (RM) problem in a setting where heterogeneous customers consider an assortment of products offered by a firm (e.g., different flight times, fare classes, and/or routes). Individual choice decisions are modeled through an ordered list of...