studies

Basic techniques for the algorithm design (divide-and-conquer, greediness, dynamic programming), time and space complexity, basic methods for the analysis of algorithm performance, sorting, seaching, basic algorithms for graphs depth-first search, breadth-first search and its applications such as topological sorting, finding of articulation points, and strongly-connected componets), shortest paths, minimum spanning trees, max flows, algorithms for basic arithmetic computation (matrix computations, fast fourier transform) complexity classes, introduction to the approximation and on-line algorithms.

The course introduces the decision support systems, their architecture and development approaches. Then the course focuses on analytical models and techniques developed to support decision making in semi-structured decision problems. It provides a deep insight of methods that require preference modelling, such as methods of multi-objective linear programming, methods of multi-criteria decision analysis and the analytic hierarchy process, as well as to the method of data envelopment analysis, which is the dominating data analysis method in performance measurement.