Lecture

Projected Gradient Descent: Convergence and Optimization

Description

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.

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