SEE EE364B - Convex Optimization II (Spring, 2008)
Stanford Engineering Everywhere EE364B - Convex Optimization II
author: Stephen P. Boyd,
Department of Electrical Engineering, Stanford University
released under terms of: Creative Commons Attribution Non-Commercial (CC-BY-NC)
released under terms of: Creative Commons Attribution Non-Commercial (CC-BY-NC)
Continuation of Convex Optimization I. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Course requirements include a substantial project.
Prerequisites: Convex Optimization I
Course Homepage: http://see.stanford.edu/see/courseinfo.aspx?coll=523bbab2-dcc1-4b5a-b78f-4c9dc8c7cf7a
Course features at Stanford Engineering Everywhere page:
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