This lecture covers the application of Projected Gradient Descent (PGD) in optimization problems, focusing on convergence results and performance evaluation. The instructor explains the algorithm, its convergence to a global optimum, and the computation of sparse solutions. Through examples and theoretical insights, the lecture delves into the challenges and benefits of using PGD in multivariable control systems.