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Engineering Optimization - N445061

Title:	Inženýrská optimalizace
Guaranteed by:	Department of Computing and Control Engineering (445)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2013 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:	unknown / unlimited (unknown)
Min. number of students:	unlimited
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Level:
Additional information:	http://moodle.vscht.cz/course/view.php?id=57

Guarantor:	Mareš Jan prof. Ing. Ph.D. Hanta Vladimír Ing. CSc.
Is interchangeable with:	M445011

Examination dates Schedule

Annotation -

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.

Last update: SMIDOVAL (15.12.2012)

Literature -

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

Last update: Hanta Vladimír (01.07.2013)

Syllabus -

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

Last update: Hanta Vladimír (22.06.2009)

Learning resources -

http://moodle.vscht.cz/course/view.php?id=57

http://www.mathworks.com/products/optimization/

http://www.mathworks.com/products/global-optimization/

http://www.mathworks.com/matlabcentral/fileexchange/index?term=tag%3A%22optimization%22

Last update: Hanta Vladimír (01.07.2013)

Learning outcomes -

Students will be able to:

formulate optimization problems

solve basic and advanced optimization tasks in different computing environments

use various optimization programs and tools

Last update: Hanta Vladimír (01.07.2013)

Registration requirements -

Algorithms and Programming, Mathematics I

Last update: Hanta Vladimír (01.07.2013)

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