EE4530 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)
prof.dr.ir. Geert Leus
Signal processing for communications, with applications to underwater communications, cognitive radio, and multiple-input multiple-output (MIMO) systems. Signal processing for (compressive) sensing with applications to ultrasound imaging and radar. Distributed signal processing. Graph signal processing.
dr. Borbála Hunyadi
Biomedical signal processing, Tensor decompositions
MSc Alberto Natali
Graph-based Signal Processing, Learning over Graphs, (Network) Data Science
Last modified: 2023-11-03