Academic year 2023/2024 |
Supervisor: | RNDr. Pavel Popela, Ph.D. | |||
Supervising institute: | ÚM | |||
Teaching language: | Czech | |||
Aims of the course unit: | ||||
The course objective is to develop the advanced knowledge of sophisticated optimization techniques as well as the understanding and applicability of principal concepts. |
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Learning outcomes and competences: | ||||
The course is mainly designated for mathematical engineers, however it might be useful for applied sciences students as well. Students will learn of the recent theoretical topics in advanced optimization and advanced optimization algorithms. They will also develop their ideas about suitable models for typical applications. |
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Prerequisites: | ||||
The presented topics require basic knowledge of optimization concepts (see SOP). Standard knowledge of probabilistic and statistical concepts is assumed. |
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Course contents: | ||||
The course focuses on advanced optimization models and methods of solving engineering problems. It includes especially stochastic programming (deterministic reformulations, theoretical properties, and selected algorithms) and selected areas of network flows, integer and dynamic programming. |
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Teaching methods and criteria: | ||||
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures. |
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Assesment methods and criteria linked to learning outcomes: | ||||
The exam is based on presentation of prepared paper and following joint oral discussion of results. |
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Controlled participation in lessons: | ||||
The attendance at seminars is required as well as active participation. Passive or missing students are required to work out additional assignments. |
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Type of course unit: | ||||
Lecture | 13 × 2 hrs. | optionally | ||
Computer-assisted exercise | 13 × 1 hrs. | compulsory | ||
Course curriculum: | ||||
Lecture | 1. Underlying mathematical program. |
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Computer-assisted exercise | Exercises on: |
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Literature - fundamental: | ||||
1. Kall, P.-Wallace,S.W.: Stochastic Programming, Wiley 1994. | ||||
2. Birge,J.R.-Louveaux,F.: Introduction to Stochastic Programing, Springer, 1997. | ||||
Literature - recommended: | ||||
1. Popela,P.: Stochastic programming. sylabus, PDF, 2022 | ||||
2. Williams,H.P.: Model Building in Mathematical Programming. Wiley and Sons, 2012 | ||||
3. Klapka, J. a kol: Metody operačního výzkumu, VUT, 2000. | ||||
4. Prekopa, A: Stochastic Programming, Kluwer, 1996. |
The study programmes with the given course: | |||||||||
Programme | Study form | Branch | Spec. | Final classification | Course-unit credits | Obligation | Level | Year | Semester |
N-MAI-P | full-time study | --- no specialisation | -- | Cr,Ex | 4 | Compulsory | 2 | 1 | W |
Faculty of Mechanical Engineering
Brno University of Technology
Technická 2896/2
616 69 Brno
Czech Republic
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