Lehrinhalte
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.
Literatur
1. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. (online Verfügbar: [url]http://www.stanford.edu/~boyd/cvxbook/[/url])
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.
Voraussetzungen
Knowledge in linear algebra and the basic concepts of signal processing and communications.
Zusätzliche Informationen
Please turn to the [url=https://www.nts.tu-darmstadt.de/teaching_nts/index.en.jsp]homepage[/url] of the group for organizational process of the oral exams.
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.
Literatur
1. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. (online Verfügbar: [url]http://www.stanford.edu/~boyd/cvxbook/[/url])
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.
Voraussetzungen
Knowledge in linear algebra and the basic concepts of signal processing and communications.
Zusätzliche Informationen
Please turn to the [url=https://www.nts.tu-darmstadt.de/teaching_nts/index.en.jsp]homepage[/url] of the group for organizational process of the oral exams.
- Lehrende: MüllerRaphael
- Lehrende: PesaventoMarius
Semester: ST 2020