Czech

Not applicable.

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 optimization and advanced optimization algorithms. They will also develop their ideas about suitable models for typical applications.

The presented topics require basic knowledge of optimization concepts (see SOP). Standard knowledge of probabilistic and statistical concepts is assumed.

Not applied.

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

The lectures combine advanced theory with illustrative examples. The practical exercises are concerned on handling numerical tasks.

There is a written exam accompanied by oral discussion of results.

1. Underlying mathematical program.
2. WS and HN approach.
3. IS and EV reformulations.
4. EO, EEV, EVPI and VSS.
5. MM and VO, the solution of the large problems.
6. PO and QO, relation to integer programming.
7. Deterministic and probabilistic constraints, the use of recourse.
8. WS theory - convexity and measurability.
9. WS theory - probability distribution identification.
10. Twostage problems, classification and modelling.
11. Basic results in convexity of SPs.
12. Applied twostage programming.
13. Dynamic programming and multistage models.

Not applicable.

The course objective is to develop the advanced knowledge of sophisticated optimization techniques as well as the understanding and applicability of principal concepts.

The attendance at seminars is required as well as active participation. Passive or missing students are required to work out additional assignments.

Not applicable.

Not applicable.

Birge,J.R.-Louveaux,F.: Introduction to Stochastic Programing, Springer, 1995 (EN)
Kall, P.-Wallace,S.W.: Stochastic Programming, Wiley 1994. (EN)
Prekopa, A: Stochastic Programming, Kluwer, 1996. (EN)

Klapka, J. a kol: Metody operačního výzkumu, VUT, 2000. (CS)