Objectives and outcomes
Students acquire knowledge about the types of optimisation methods and the possibilities of their application to solving engineering problems. Analysis of models and problems of optimisation methods application. Applications of numerical methods, methods of dynamic programming and global optimisation.
Lectures
Optimisation basics: setting and classification of optimisation problems. Integer programming: Gomori method, algorithm, binary and programming. Genetic algorithms for discrete programming. Dynamic programming. Global optimisation. A Monte Carlo method: the method of statistical sampling; computer simulations. Game theory. Multi-criteria optimisation: Pareto optimisation. Applications in the design of digital filters, digital predictive control systems, the optimal distribution of force in robotic systems and multi-user detection in wireless communication channels.
Paper work
Use of software packages for analysis and application of optimisation methods. Application of optimisation methods to given situations. Studying scientific journals and other literature through research work, students independently broaden the knowledge acquired in lectures. Working with a professor, students are trained to independently write a scientific paper.