SubjectsSubjects(version: 949)
Course, academic year 2019/2020
Engineering Optimization - M445011
Title: Inženýrská optimalizace
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2019 to 2020
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
For type: Master's (post-Bachelor)
Additional information:
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Mareš Jan doc. Ing. Ph.D.
Interchangeability : N445061
Examination dates   Schedule   
Annotation -
Last update: Pátková Vlasta (20.04.2018)
The aim is to give a survey of classic and current optimization methods and to apply them to solving practical and real-world engineering problems. Students will learn to formulate optimization problems, state the requirements and constraints put on solution, transform optimization problem to a correct mathematical form, use adequate numerical algorithms in suitable computational environment (Matlab: Symbolic Math Toolbox, Optimization Toolbox, Microsoft Excel: Solver, etc.) and verify and critically evaluate obtained results.
Aim of the course -
Last update: Pátková Vlasta (20.04.2018)

Students will be able to:

  • formulate optimization problems
  • solve basic and advanced optimization tasks in different computing environments
  • use various optimization programs and tools
Literature -
Last update: Pátková Vlasta (20.04.2018)

R: Venkataraman P.: Applied Optimization with MATLAB Programming. Wiley, New York 2002, 0-471-34958-5

R: Himmelblau, D. M.: Applied Nonlinear Programming. McGraw-Hill, New York 1972, 0-07-028921-2

A: Rao, S. S.: Engineering Optimization. Theory and Practice. Wiley, New York 1996, 0-471-55034-5

A: Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989, 0-201-15767-5

Learning resources -
Last update: Pátková Vlasta (20.04.2018)

Syllabus -
Last update: Pátková Vlasta (20.04.2018)

1. Optimization process, concepts and goals, general scheme and basic elements

2. Classical analytical theory of extremes, non-classical applications

3. Linear programming

4. Simplex method

5. Quadratic programming

6. Non-linear programming, one-dimensional and multidimensional seeking

7. Gradient and non-gradient methods

8. Optimization methods with equality and inequality constraints, multiple criteria decision making.

9. Optimization of multistage processes, dynamical programming, maximum principle

10. Variation calculus

11. Combinatorial optimization, graph optimization methods

12. Discrete optimization, branch and bound method

13. Stochastic optimization, simulated annealing method

14. Genetic algorithm, evolution algorithm, taboo search algorithms

Registration requirements -
Last update: Pátková Vlasta (20.04.2018)

Algorithms and Programming, Mathematics I

Course completion requirements - Czech
Last update: Mareš Jan doc. Ing. Ph.D. (03.10.2023)

Vypracování a obhajoba tří samostatných projektů: 0 - 25 bodů

Ústní zkouška: 0-75 bodů

Celkové bodové hodnocení: 100-90 A, 89-80 B, 79-70 C, 69-60 D, 59-50 E, méně než 50 F.

Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Práce na individuálním projektu 2 56
Příprava na zkoušku a její absolvování 1 28
Účast na seminářích 1 28
5 / 5 140 / 140
Coursework assessment
Form Significance
Regular attendance 20
Report from individual projects 40
Examination test 20
Oral examination 20