Course detail
Computing Methods in Optimization Problems
FSI-VOU-AAcad. year: 2025/2026
The course introduces to the basic concepts of optimization and the use of appropriate software. Subsequently, optimization problems in engineering are solved. The main content of the course is to recognize and use a suitable model and methods for specific engineering tasks.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Offered to foreign students
Entry knowledge
Rules for evaluation and completion of the course
Course-unit credit: Active participation in the seminars, elaboration of a given project. Examination: Written.
Attendance at seminars is controlled. An absence can be compensated for via solving additional problems.
Aims
The student will aquire the ability to recognize a suitable optimization model for a given engineering problem. The student will be able to implement said model in an adequately chosen software and analyze the results.
Study aids
Prerequisites and corequisites
Basic literature
Rardin, R.L.: Optimization in Operations Research, 2nd edition. Pearson Higher Education, 2017. (EN)
Williams, H.P. Model Building in Mathematical Programming, 4th edition. J.Wiley and Sons, 2012.
Recommended reading
Boyd, S.P. a Vandenberghe, L. Convex Optimization, Cambridge University Press, 2004. (EN)
Kochenderfer, M. J., Wheeler, T. A.: Algorithms for Optimization. MIT Press, 2019. (EN)
Wolsey, L. A.: Integer Programming. Wiley, 1998. (EN)
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Software tools for optimization: languages/enviroments: EXCEL, MATLAB, Julia. The use of solvers.
3. - 5. Optimization problems in engineering, types of optimization models (linear, quadratic, convex, etc.)
6. - 7. Integer programming problems – applications in logistics, scheduling, etc.
8. Linearization, modelling with SOS1 and SOS2 variables.
9. Black-box optimization and optimization within a simulation environment.
10. Dynamic optimization models.
11. - 13. Models with uncertain data – stochastic and robust formulations.
Computer-assisted exercise
Teacher / Lecturer
Syllabus