Convex Optimization: Theory and Applications

This lecture covers the theory and applications of convex optimization, focusing on local and global minima, convex sets, functions, and intersections. It also discusses convex functions, gradient, Hessian, and examples in n dimensions. The lecture delves into the classification of nonlinear programming problems, including linearly constrained NLP, LP, QP, and QCQP. It concludes with the wide applicability of convex optimization in various fields like finance, engineering, and transportation.