Nonlinear Programming: Part I

This lecture covers the fundamentals of Nonlinear Programming (NLP) and its applications in Optimal Control. Topics include the discussion on NLP techniques, examples of nonlinear programs, feasibility sets, definitions of optimality, and necessary conditions for optimality. The lecture also delves into Convex Programming, Regular Points, and the interpretation of Lagrange Multipliers. Different methods such as Sequential Quadratic Programming (SQP) and Interior Point Methods are explored, along with the importance of computing derivatives efficiently. The lecture concludes with a summary of key concepts and references for further study.