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Course, academic year 2021/2022
  
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Engineering Optimalization - AP445007
Title: Engineering Optimalization
Guaranteed by: Department of Mathematics, Informatics and Cybernetics (446)
Faculty: Faculty of Chemical Engineering
Actual: from 2021
Semester: both
Points: 0
E-Credits: 0
Examination process:
Hours per week, examination: 3/0, other [HT]
Capacity: winter:unknown / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
Teaching methods: full-time
Level:  
For type: doctoral
Note: course is intended for doctoral students only
can be fulfilled in the future
you can enroll for the course in winter and in summer semester
Guarantor: Kukal Jaromír doc. Ing. Ph.D.
Interchangeability : P445007
Annotation -
The course deals with modern methods and tool of optimization. The aim of the course is to use selected lgorthms for solving real problems from technological praxis. For the exam it is necessary to propose a draft of publication form the field of disertation thesis.
Last update: Pátková Vlasta (19.11.2018)
Aim of the course -

Students will be able to:

  • use selected methods of optimosation to solve a real problém form technological praxis,
  • use selected methods of artificial intelligence to solve problems of global optimisation,
Last update: Pátková Vlasta (19.11.2018)
Literature -

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

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

A: Lange, K.: Optimization (2nd edition), Springer, New York 2013. ISBN 978-1-4614-5837-1.

Last update: Pátková Vlasta (19.11.2018)
Learning resources -

www.honeywellprocess.com/

www.mathworks.com/

Last update: Pátková Vlasta (19.11.2018)
Teaching methods -

lectures, project and solving of case stidies

Last update: Pátková Vlasta (19.11.2018)
Syllabus -

1. Optimisation process, aims. Theoretical introduction

2. Local optimisation, analytical and nunerical tools overview.

3. Linear, quadratic and nonlinear programming.

4. Discrete and global optimisation. Genetic and evolution algorithms.

5. Using Optimization Toolbox and Global Optimization Toolbox.

Last update: Pátková Vlasta (19.11.2018)
Entry requirements -

none

Last update: Pátková Vlasta (19.11.2018)
Registration requirements -

none

Last update: Pátková Vlasta (19.11.2018)
Course completion requirements -

3 individual projects: 0 - 25 bodů

Oral exam: 0-75 bodů

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

Last update: Pátková Vlasta (19.11.2018)
 
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