What is different about embedded optimization?

Eric C. Kerrigan, Bulat Khusainov, George A. Constantinides

Abstract: Embedded optimization involves solving a sequence of optimization problems where the data of these problems are updated by measurements of a process that evolves in time. The solutions to these optimization problems are also used to make decisions or update some inputs to the process. Because of external disturbances, measurement noise, model uncertainty and computational errors, the exact evolution of the combined cyber-physical system cannot be accurately predicted. As a consequence, the correctness and efficiency of the optimization solver should be a function of time and the dynamics of the process. We will show that, because of the tight coupling between the algorithm, computer hardware and process, embedded optimization is more than just fast or robust optimization. We will demonstrate via some simple case studies how an engineer can systematically trade off the performance and robustness of the closed-loop system against the required computing resources, such as time, energy and space.