Graduate and Doctoral Courses

This course intends to provide the students with a thorough understanding of numerical optimization methods for both unconstrained and constrained non-linear minimization as well as up-to-date methods of stochastic global optimization (simulated annealing and genetic algorithms) along with neural net methods.

By combining recent theory with concrete practical and computational experience based on analysis and comparison of efficient recently developed algorithms for solving real life optimization problems and their implementation on supercomputers, taught by an instructor active in research in numerical optimization.

The material will be presented in a manner reflecting the most recent advances in the field during the last 15 years along with adequate software to illustrate each method presented. New trends such as non-smooth and non-differentiable optimization algorithms will conclude the course.