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Expand/collapse block Information for lesson "Convex Optimization I & II (EE 364A&B) Stanford"
Description: (EE364A)Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. (EE364B) Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, and communications.
Resources: OpenCourseware from Yale, UC Berkeley, Stanford, MIT along with many of the World's finest University's.
Language: English
Professors: Michael Williams
Units: 37
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