SubjectsSubjects(version: 899)
Course, academic year 2021/2022
Optimization of Engineering Processes - N413011
Title: Optimalizace inženýrských procesů
Guaranteed by: Department of Mathematics (413)
Actual: from 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 [hours/week]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Is provided by: M413005
For type:  
Guarantor: Kubíček Milan prof. RNDr. CSc.
Is interchangeable with: M413005
Examination dates   Schedule   
Annotation -
Last update: Kubíček Milan prof. RNDr. CSc. (01.08.2013)
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.
Aim of the course -
Last update: Kubíček Milan prof. RNDr. CSc. (01.08.2013)

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.

Literature -
Last update: TAJ413 (28.08.2013)

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

A: Individually according to the project orientation.

Learning resources -
Last update: Kubíček Milan prof. RNDr. CSc. (27.08.2013)

Teaching methods -
Last update: TAJ413 (01.08.2013)

Lectures and exercise classes.

Syllabus -
Last update: Kubíček Milan prof. RNDr. CSc. (30.07.2013)

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.

Registration requirements -
Last update: Kubíček Milan prof. RNDr. CSc. (01.08.2013)

Mathematics I, Mathematics II.

Teaching methods
Activity Credits Hours
Konzultace s vyučujícími 0,5 14
Obhajoba individuálního projektu 0,1 3
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 1 28
Práce na individuálním projektu 0,5 14
Příprava na zkoušku a její absolvování 0,9 25
Účast na seminářích 1 28
5 / 5 140 / 140
Coursework assessment
Form Significance
Regular attendance 20
Defense of an individual project 10
Examination test 30
Continuous assessment of study performance and course -credit tests 10
Oral examination 30