Subjects

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Optimization of Engineering Processes - M413005

Title:	Optimalizace inženýrských procesů
Guaranteed by:	Department of Mathematics (413)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2020 to 2020
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
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time
Level:
Note:	course can be enrolled in outside the study plan enabled for web enrollment

Guarantor:	Maxová Jana 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)

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)

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)

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