  In this paper, we explore a novel combination of supervised learning and
quadratic programming to refine dynamic pricing models in the car rental
industry. We utilize dynamic modeling of price elasticity, informed by ordinary
least squares (OLS) metrics such as p-values, homoscedasticity, error
normality. These metrics, when their underlying assumptions hold, are integral
in guiding a quadratic programming agent. The program is tasked with optimizing
margin for a given finite set target.
