In this paper we explore the Linear-Quadratic Regulator (LQR) to model movement of the mouse pointer. We propose a model in which users are assumed to behave optimally with respect to a certain cost function. Users try to minimize the distance of the mouse pointer to the target smoothly and with minimal effort, by simultaneously minimizing the jerk of the movement. We identify parameters of our model from a dataset of reciprocal pointing with the mouse. We compare our model to the classical minimum-jerk and second-order lag models on data from 12 users with a total of 7702 movements. Our results show that our approach explains the data significantly better than either of these previous models.