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Instructors: Prof. Dr.-Ing. Marius Pesavento; Ganapati Hegde
Event type:
Exercise
Org-unit: Dept. 18 - Electrical Engineering and Information Technology
Displayed in timetable as:
Konvexe Optimierung
Subject:
Crediting for:
Hours per week:
1
Language of instruction:
Englisch
Min. | Max. participants:
- | -
Course Contents:
This graduate course introduces the basic theory of convex optimization and illustrates its use with many recent applications in communication systems and signal processing.
Outline: Introduction, convex sets and convex functions, convex problems and classes of convex problems (LP, QP, SOCP, SDP, GP), Lagrange duality and KKT conditions, basics of numerical algorithms and interior point methods, optimization tools, convex inner and outer approximations for non convex problems, sparse optimization, distributed optimization, mixted integer linear and non-linear programming, applications.
Literature:
1. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. (available at http://www.stanford.edu/~boyd/cvxbook/)
2. D. P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, Massachusetts, 2nd Ed., 1999.
3. Daniel P. Palomar and Yonina C. Eldar, Convex Optimization in Signal Processing and Communications, Cambridge University Press, 2009.
Preconditions:
Knowledge in linear algebra and the basic concepts of signal processing and communications.
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