Title: Deterministic Global Optimization of Dynamic Systems

Optimization problems with ordinary differential equations (ODEs) embedded are typically nonconvex and often exhibit multiple local minima, some of which are suboptimal. This talk will discuss the theory and implementation of finite, deterministic algorithms that can guarantee locating a global optimal solution of nonconvex optimization problems with ODEs embedded. In particular, we will discuss the construction of convex relaxations of nonconvex functionals with ODEs embedded. Our approach builds upon the relaxation procedure proposed more than 30 years ago by McCormick for convex relaxation of factorable functions. We will also discuss how the estimates generated by these relaxations can be computed practically.