Nonlinear Programming

3 Credits

Focuses on the identification and formulation of convex optimization problems, where such problems are those nonlinear problems that have optimal solutions. Studies the principles for the selection of algorithms and their programmatic implementation to solve them Derives convergence rates and compares these rates across major classes of problems. Explores the application to problems in approximate optimal control, such as model predictive control and reinforcement learning.