19D051PRO - Applied Robust Optimization

Course specification
Course title Applied Robust Optimization
Acronym 19D051PRO
Study programme Electrical Engineering and Computing
Module System Control and Signal Processing
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition None.
      The goal Introduce students to principles of robust optimization, domain and limitations in application, as well as available techniques for solving problems.
      The outcome Students are able to: define appropriate robust optimization problem setup, apply tools for solving problems, perform critical analysis of obtained results and examine possibilities for improvement in the sense of robustness and/or performance.
      Contents of lectures Introduction. Classical and robust optimization. Models. Robust versions of classical algorithms. Elementary algorithms and classification of problems. Robust counterparts. Robust multi-stage optimization. Tools for solving problems. Application: control systems, machine learning, mining and prediction of big data, decision support in cyber-physical and socio-economic systems.
      Contents of exercises None.
      1. A. Ben-Tal, L. El Ghaoui and A. Nemirovskii, Robust optimization, Princeton University Press, 2009. (Original title)
      2. F. Lin, Robust control design: An optimal control approach, Wiley, 2007. (Original title)
      3. P. Xanthopoulos, P. Pardalos and T. Trafalis, Robust data mining, Springer, 2013. (Original title)
      4. S. Sra, S. Nowozin and S.J. Wright, Optimization for machine learning, The MIT Press, 2012. (Original title)
      5. Cornuejols and Tütüncü, Optimization methods in finance, Cambridge University Press, 2007. (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      Methods of teaching Lectures. Students are obliged to solve and defend individually assigned project, which earns them exam points.
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures Test paper
      Practical lessons Oral examination 30
      Projects 70