SubjectsSubjects(version: 990)
Course, academic year 2026/2027
  
   
Introduction to Mathematical Optimization - B413009
Title: Základy matematické optimalizace
Form of teaching: lecture+practicals
Guaranteed by: Department of Mathematics, Informatics and Cybernetics (446)
Faculty: Faculty of Chemical Engineering
Actual: from 2023
Duration in semesters: 1
Semester: both
Points: 5
E-Credits: 5
Examination process:
Hours per week, examination: 2/2, C+Ex [HT]
Capacity: winter:103 / unlimited (unknown)
summer:unknown / unknown (unknown)
Maximum number of enrolled students: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Level:  
Repeated enrollment: - / - / - / 9
Note: course can be enrolled in outside the study plan
enabled for web enrollment
you can enroll for the course in winter and in summer semester
Guarantor: Szala Leszek Marcin RNDr. Ph.D.
Classification: Mathematics > Optimization
Interchangeability : N413009
Examination dates   Schedule   
Annotation -
The course is intended for all students in bachelor programmes, particularly those studying chemical cybernetics or focusing on economics. Students will become familiar with fundamental concepts and methods used in optimization.
Last update: Szala Leszek Marcin (16.09.2025)
Course completion requirements -

During the semester, two in‑term tests are administered. To obtain the course credit, a student must achieve at least 50% of the maximum number of points in total. If this requirement is not met, the student must take a comprehensive test during the examination period and obtain at least 50%. No additional resit attempts are available.

An examination is held during the examination period. During all tests and the final exam, independent work is required. The use of any materials or aids other than writing instruments is prohibited; the use of artificial intelligence or any other external resources is not allowed.

Last update: Szala Leszek Marcin (23.06.2026)
Literature -

Obligatory:

  • Boyd, Stephen and Vandenberghe, Lieven. Convex Optimization. Cambridge: Cambridge University Press, 2004, s. ISBN 978-0-521-83378-3.
  • Conforti, Michele and Cornuéjols, Gérard and Zambelli, Giacomo. Integer Programming. Cham: Springer, 2014, s. ISBN 978-3-319-11008-0.
  • Diestel, Reinhard. Graph theory. New York: Springer-Verlag, 2000, s. ISBN 978-3-662-53621-6.

Recommended:

  • Schrijver, A.. Theory of linear and integer programming. Chichester: Wiley, 1986, XI, 471 sl. s. ISBN 0-471-98232-6.

Optional:

  • Bertsimas, Dimitris and Tsitsiklis, John N.. Introduction to Linear Optimization. Belmont: Athena Scientific, 1997, s. ISBN 1-886529-19-1.

Last update: Szala Leszek Marcin (16.09.2025)
Teaching methods -

Lectures and seminars

Last update: Kubová Petra (01.05.2019)
Syllabus -

1. Problems of mathematical optimization.

2. Linear programming.

3. Convex polyhedra.

4. Simplex method.

5. Duality of linear programming.

6. Integer programming, totally unimodular matrices.

7. Basic notions of graph theory.

8. Shortest path problem.

9. Tree, spanning tree, greedy algorithm.

10. Discrete optimalization problems as problems of integer programming.

11. Nonlinear optimization.

12. Kuhn-Tucker conditions.

13. Numerical methods for nonlinear programming.

14. Convex functions, positive semidefinite matrices.

Last update: MAXOVAJ (17.01.2020)
Learning resources -

https://iti.mff.cuni.cz/series/2006/311.pdf

Last update: Szala Leszek Marcin (16.09.2025)
Learning outcomes -

General skills:

1. basic terms in mathematical optimiztion

2. knowledge and understanding of basic algorithms

3. individual problem solving

4. basic mathematical background for formulation and solving of optimization problems

5. numerical algorithms .

Last update: Kubová Petra (01.05.2019)
Registration requirements -

Mathematics A, Mathematics B (or Mathematics I, Mathematics II)

Last update: MAXOVAJ (20.01.2020)
Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 0.5 14
Práce na individuálním projektu 1 28
Příprava na zkoušku a její absolvování 1.5 42
Účast na seminářích 1 28
5 / 5 140 / 140
 
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