Convex Optimization
Lecturer: Wolfgang Utschick with Benedikt Böck
Target Audience: Master EI and MSCE
Language: English
Next Exam: tbd (no responsibility is taken for the correctness of this information)
Additional Information: TUMonline and Moodle
Lectures/Tutorials in Winter Semester 2022/23
Content
Introduction: Problem Statement of Optimization, Basic Definitions, Categorization. Convex Analysis: Convex Sets and Functions. Linear Programming: Extremal points, Extremal directions. Optimality Conditions: Karush-Kuhn-Tucker Conditions, Constraint Qualifications. Lagrangian Duality: Duality Theorems, Solutions for the Primal and Dual Problem. Algorithms: Subgradient Methods, Cutting Plane Algorithms, Projection Methods, Fixpoint Algorithms. Applications: Network Optimization, Problems from Multiuser Information Theory, Resource Allocation, Parameter Optimization in Layered/Distributed Communication Systems. |