SubjectsSubjects(version: 953)
Course, academic year 2019/2020
  
Engineering Optimalization - P445007
Title: Inženýrská Optimalizace
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2019 to 2020
Semester: both
Points: 0
E-Credits: 0
Examination process:
Hours per week, examination: 3/0, other [HT]
Capacity: winter:unlimited / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level:  
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.
Is interchangeable with: AP445007
Examination dates   Schedule   
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: Mareš Jan (07.06.2018)
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: Mareš Jan (07.06.2018)
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: Kukal Jaromír (04.09.2018)
Learning resources -

www.honeywellprocess.com/

www.mathworks.com/

Last update: Mareš Jan (07.06.2018)
Teaching methods -

lectures, project and solving of case stidies

Last update: Mareš Jan (07.06.2018)
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: Mareš Jan (07.06.2018)
Entry requirements -

none

Last update: Mareš Jan (07.06.2018)
Registration requirements -

none

Last update: Mareš Jan (07.06.2018)
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: Mareš Jan (07.06.2018)
Coursework assessment
Form Significance
Defense of an individual project 30
Oral examination 70

 
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