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.
Applied convex optimization: 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 Sundeep Prabhakar Chepuri

Signal processing; Sparse Sampling; Statistical inference. Geert Leus

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

Last modified: 2017-11-01


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