SubjectsSubjects(version: 952)
Course, academic year 2021/2022
Optimization of Engineering Processes - M413005
Title: Optimalizace inženýrských procesů
Guaranteed by: Department of Mathematics, Informatics and Cybernetics (446)
Faculty: Faculty of Chemical Engineering
Actual: from 2021 to 2021
Semester: winter
Points: winter s.:5
E-Credits: winter s.:5
Examination process: winter s.:
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: 24 / 24 (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
For type:  
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Maxová Jana RNDr. Ph.D.
Interchangeability : N413011
This subject contains the following additional online materials
Annotation -
The course is oriented on ability of formulation of optimization problem based on mathematical model of a process. Methods of classical analysis for unconstained and constrained extrema are discussed. Basic methods of linear, nonlinear and dynamic programming are introduced. Vector optimization problems are discussed. Methods are demonstrated on selected engineering problems.
Last update: Hladíková Jana (16.01.2018)
Aim of the course -

Students will be able to understand and formulate optimization problem and to solve it in simple cases, to use suitable software in more complicated cases, to analyze the problem and suggest a solution.

Last update: Hladíková Jana (16.01.2018)
Literature -

R: Kubíček M.: Optimalizace inženýrských procesů. SNTL Praha 1986. ISBN 05-098-86

A: Individually according to the project orientation.

Last update: Hladíková Jana (16.01.2018)
Learning resources -

Last update: Hladíková Jana (16.01.2018)
Teaching methods -

Lectures and exercise classes.

Last update: Hladíková Jana (16.01.2018)
Syllabus -

1. Formulation of optimization problem.

2. Extrema of functions of real variables-methods of classical analysis.

3. Free extremum, extremum with equality constraints.

4. Extremum with inequality constraints.

5. Linear programming.

6. Simplex method.

7. Nonlinear programming.

8. Methods of adaptive search.

9. Gradient methods.

10. Penalty functions.

11. Elements of dynamic programming.

12. Example of sources distribution.

13. Vector optimization.

14. Construction of Pareto compromise set.

Last update: Hladíková Jana (16.01.2018)
Entry requirements -

Students are expected to have either completed the prerequisite courses Mathematics A and Mathematics B or possess the equivalent knowledge prior to enrolling in the course.

Last update: Borská Lucie (13.05.2019)
Registration requirements -

No requirements.

Last update: Borská Lucie (06.05.2019)
Teaching methods
Activity Credits Hours
Konzultace s vyučujícími 0.5 14
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 1 28
Příprava na zkoušku a její absolvování 1.5 42
Účast na seminářích 1 28
5 / 5 140 / 140