Optimization Methods and Game Theory – 2020/21

General information on the course

Lectures schedule

Video recordings of lectures

Suggested textbooks:

  • S. Boyd, L. Vandenberghe, Convex optimization, Cambridge University Press, 2004
  • M.S. Bazaraa, H.D. Sherali, C.M. Shetty, Nonlinear programming: theory and algorithms, Wiley & Sons, 2006 (Chapters 1-6, 8-9)
  • J. Nocedal, S. Wright, Numerical Optimization, Springer Series in Operations Research and Financial Engineering, 2006 (Chapters 1-3, 5, 12, 16, 17)
  • J. Gondzio, Interior point methods 25 years later, European Journal of Operational Research, vol. 218 (2012), pp. 587-601
  • A.R. Conn, K. Scheinberg, L.N. Vicente, Introduction to Derivative-Free Optimization, SIAM series on Optimization, 2009 (Chapters 1, 7)
  • N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, 2004.
  • A.J. Smola, B. Scholkopf, A tutorial on support vector regression, Statistics and Computing, vol. 14 (2004), pp. 199-222.
  • M. Teboulle, A Unified Continuous Optimization Framework for Center-Based Clustering Methods, Journal of Machine Learning Research, vol. 8 (2007), pp. 65-102.
  • D.T. Luc, Theory of Vector Optimization, Springer, 1989 (Chapters 2, 4)
  • Y. Sawaragi, H. Nakayama, T. Tanino, Theory of Multiobjective Optimization, Academic Press, 1985 (Chapters 3, 7)
  • M.J. Osborne, A. Rubinstein, A Course in Game Theory, MIT press, 1994 (Chapters 1-4)
  • N. Nisan, T. Roughgarden, E. Tardos, V.V. Vazirani, Algorithmic Game Theory, Cambridge University Press, 2007 (Chapter 3)

Slides:

Exercises:

Further exercises:

Exams:

Oral exams — Microsoft Teams