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Discrete and Experimental Optimization


The main content of this course will be the study of various optimization problems that arise in theory and practice.

The first part of the course will be devoted to the theoretical background and algorithms for optimization problems. In the second part various optimization problems and more advanced and specialized algorithms will be discussed. An essential part of the course will be the implementation and evaluation of various algorithms in Matlab.

Most of the examples that are discussed in the class come from Machine Learning.

Suggested reading:

  • Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press. (also available as PDF here)
  • Yurii Nesterov. Introductory Lectures on Convex Optimization. Kluwer Academic Publishers.
  • Jorge Nocedal and Stephen J. Wright. Numerical Optimization. Springer.

Please register for this class through CAJ (only for registered users).