On the global minima of polynomial dynamical system identification problems KU Leuven
Solving nonlinear optmization problems is an important step in data driven system identification and machine learning. However, iterative methods do not guarantee to find the global optimum of these problems. The goal of this research is to acquire more insight in the optmization problems arising from system identification. To achieve this goal, a method is devised to find the global least-squares optima of identification problems for ...