Navigation

26E054TO - Optimization techniques in control systems and signal processing

Course specification
Course title Optimization techniques in control systems and signal processing
Acronym 26E054TO
Study programme Electrical Engineering and Computing
Module Signals and Systems
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status mandatory
Condition none
The goal The aim of the course is to introduce the basics of optimization techniques used in signal processing and systems control. Students will be presented with approaches to the formulation of optimization problems, as well as available analytical and numerical optimization methods, with an emphasis on specific applications.
The outcome Students will be able to formulate optimal criteria that meet the desired requirements, and then select and design an adequate technique for solving the given problem from a wide range of offered optimization techniques, whether it is optimization without constraints or with them.
Contents
Contents of lectures Basic optimization concepts without and with constraints. Mathematical programming. Convexity. Linear programming, geometry of linear problems, simplex method, duality, interior point methods. Nonlinear programming: direct and indirect search (random and network search, gradient methods, conjugate gradient method, penalty function methods). Dynamic programming. Introduction to heuristic methods.
Contents of exercises Formulation and solution of optimization problems. Solving specific optimization problems using symbolic and/or numerical methods within the Python or Matlab programming packages.
Literature
  1. Luenberger, David G., and Yinyu Ye. Linear and nonlinear programming. Vol. 2. Reading, MA: Addison-wesley, 1984. (Original title)
  2. Rao, Singiresu S. Engineering optimization: theory and practice. John Wiley & Sons, 2019. (Original title)
  3. Bertsimas, Dimitris, and John Tsitsiklis. Introduction to Linear Optimization. Belmont, MA: Athena Scientific, 1997. (Original title)
  4. Bellman R. E., Dreyfus S. E. Applied dynamic programming. Princeton University Press, 2015 (Original title)
  5. Boyd, S. P. Convex Optimization. Cambridge University Press, 2004 (Original title)
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
3 1 1
Methods of teaching Lecture (45), auditory exercises (15), computer exercises (15).
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures Test paper 35
Practical lessons 30 Oral examination
Projects
Colloquia 35
Seminars