CONVEX OPTIMIZATION

Catalog description

Introduces the fundamental concepts, theories of convex and nonconvex optimization, and the algorithmic solutions as well as applications to many research disciplines including signal processing, networking, communications, and machine learning. Emphasis will be on (i) convex analysis and optimality conditions, (ii) first-order large-scale algorithms (gradient, proximal gradient, ADMM, Frank-Wolfe, stochastic gradient, block coordinate descent), and (iii) convergence analysis.