Information Engineering

The course gives theoretical basis and tools for mathematical modeling and solving decision and optimization problems using quantitative methods. At the end of the course, students should be able to recognize such problems, build mathematical models for them, and solve them using a number of modeling techniques and solution algorithms, also by means of specific software tools. Expected learning outcomes (Dublin Descriptors): 1. Understand all basic mathematical aspects of solving constrained and unconstrained optimization problems. Understand main modeling techniques in mathematical programming. 2. Be able to develop an algorithm for solving optimization problems. Be able to select and use suitable software to solve such models. 3. Be able to identify weaknesses of optimization models and limits of numerical methods (students develop these abilities also during any practical test of the course when they practically implement algorithms for solving optimization problems). 4. Be able to describe any aspect of a mathematical program and of the main algorithms for the solution of constrained and unconstrained programs (students develop these abilities also during any practical test of the course when they practically solve relevant decision problems by working in groups). 5. Get mathematical basis to self-study solution techniques for complex mathematical programs such as nonconvex and multi-objective programming.