Lecture
This lecture covers the application of Augmented Lagrangian Method (ALM) with equality and inequality constraints in optimization problems. The instructor discusses the process of formulating and solving optimization problems with both types of constraints, emphasizing the importance of slack variables for handling inequalities. The lecture also delves into the concept of reformulating problems with slack variables to simplify the optimization process. Throughout the slides, various mathematical expressions and equations are used to illustrate the theoretical concepts behind ALM. The presentation concludes with a discussion on the practical implications of incorporating inequality constraints in optimization models.