| Course title | Combinatorial Optimization |
|---|---|
| Course code | KMA/KOPT |
| Organizational form of instruction | Lecture + Exercise |
| Level of course | Bachelor |
| Year of study | not specified |
| Semester | Summer |
| Number of ECTS credits | 4 |
| Language of instruction | Czech |
| Status of course | Compulsory, Compulsory-optional |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
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| Course content |
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1. The linear programming and simplex method. 2. The principle of duality in linear programming. 3. Dual simplex method, integer programming methods. 4. Transport task: problem formulation and solution methods. 5. Assignment problem: problem formulation and solution methods. 6. Flows in networks: search for minimum and maximum flow. 7. The business traveler's problem (as a sample task): exact methods of solution. 8. Heuristics and meta-heuristics: genetic algorithms, simulated annealing, taboo search, ant colonies. 9. Other applications of combinatorial optimization.
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| Learning activities and teaching methods |
Monologic Lecture(Interpretation, Training), Demonstration
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| Learning outcomes |
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The purpose of the course is to supplement the classical (continuous) optimization with knowledge of discrete optimization and to acquaint students with modern methods of solving such problems. A deeper understanding of the given methods will follow in the master program.
Understanding Understand the basic problems of discrete optimization and different approaches to their solution. |
| Prerequisites |
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Basic knowledge of numerical methods and classical optimization methods.
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| Assessment methods and criteria |
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Student performance, Seminar Work
Colloquium: elaboration of a selected problem by one of the methods of combinatorial optimization, defense in the form of a presentation. |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics - Specialization in Data Science (2020) | Category: Mathematics courses | 2 | Recommended year of study:2, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): General Physics and Mathematical Physics (2019) | Category: Physics courses | 1 | Recommended year of study:1, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics (2023) | Category: Mathematics courses | 1 | Recommended year of study:1, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Mathematics (2023) | Category: Mathematics courses | 1 | Recommended year of study:1, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics - Specialization in Industrial Mathematics (2020) | Category: Mathematics courses | 2 | Recommended year of study:2, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics - Specialization in Business Mathematics (2021) | Category: Mathematics courses | 2 | Recommended year of study:2, Recommended semester: Summer |