A. M.-C. So
In the formulation of optimization models, the data defining the objective functions and/or constraints are often collected via estimation or sampling, and hence are only approximations of the nominal values. One approach to incorporate data uncertainty in optimization models is through chance constrained programming, in which one only needs to satisfy the constraints for most but not all realizations of the data. Unfortunately, such an approach often leads to computationally difficult optimization problems. Our aim in this project is twofold: (i) to develop tractable reformulations or approximations of chance constrained optimization problems, in which the data satisfy certain stochastic properties, and (ii) to apply our methodologies to practical problems, such as those arise in signal processing, wireless communications, control and finance.