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, ADMM.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)
- Alternating Direction Method of Multipliers
dr.ir. Sundeep Prabhakar Chepuri
Signal processing; Sparse Sampling; Statistical inference.
prof.dr.ir. Geert Leus
Signal processing for communication and networking, with applications to underwater communication, cognitive radio and sensor networks.
Last modified: 2016-11-12