Subjects

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Optimization of Engineering Processes - M413005

Title:	Optimalizace inženýrských procesů
Form of teaching:	lecture+practicals
Guaranteed by:	Department of Mathematics, Informatics and Cybernetics (446)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2024
Duration in semesters:	1
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)
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

Guarantor:	Szala Leszek Marcin RNDr. Ph.D.
Classification:	Mathematics > Mathematics General
Interchangeability :	N413011

Examination dates Schedule

This subject contains the following additional online materials

E-learning course

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)

Course completion requirements -

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:

Kubíček, Milan. Optimalizace inženýrských procesů. Praha: Nakladatelství technické literatury, 1986, 191 s. s. ISBN .

Last update: Szala Leszek Marcin (16.09.2025)

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)

Learning resources -

http://www.vscht.cz/mat/Ang/indexAng.html

Last update: Hladíková Jana (16.01.2018)

Learning outcomes -

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)

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