EE4530 Applied convex optimization

Topics: Applied convex optimization: role of convexity in optimization, convex sets and functions, Canonical convex problems (SDP, LP, QP), second-order methods, first-order methods for large-scale problems.
The course covers several basic and advanced topics in convex optimization. The goal of this course is to recognize/formulate problems as convex optimization problems and develop algorithms for moderate as well as large size problems. The course provides insights that can be used in a variety of disciplines.

This course treats:

  • Background and optimization basics
  • Convex sets and functions
  • Canonical convex optimization problems (LP, QP, SDP)
  • Second-order methods (unconstrained and equality constrained minimization)
  • First-order methods (gradient, subgradient, conjugate gradient)

Teachers Geert Leus

Signal processing for communication and networking, with applications to underwater communication, cognitive radio and sensor networks.

ir. Mario Coutino

Array signal processing, Sensor networks, Optimization, Numerical Lineal Algebra

Last modified: 2019-12-16


Credits: 5 EC
Period: 0/4/0/0
Contact: Geert Leus