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Selected Topics on Optimisation Theory and Methods II - NOPT007

Title:	Vybrané partie z teorie a metod optimalizace II
Guaranteed by:	Department of Applied Mathematics (32-KAM)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2020
Semester:	summer
E-Credits:	3
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	cancelled
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	prof. RNDr. Karel Zimmermann, DrSc.
Class:	Informatika Mgr. - volitelný
Classification:	Informatics > Optimalization

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: G_I (26.10.2001)

Advanced theory and methods for selected nonstandard optimization problems ( e.g. combinatorial problems, nonconvex problems, methods of global optimization, multicriterial programming, problems in infinite dimensional spaces). Continuation of OPT006, basic theoretical properties and methods from linear and convex programming necessary for understanding this advanced material.

Literature - Czech

Last update: RNDr. Pavel Zakouřil, Ph.D. (05.08.2002)

Karmanov,V.G.: Matěmatičeskoje programmirovanije, Moskva \"Nauka\" 1986

Poljak,B.T.:Vvěděnije v optimalizaciju, Moskva \"Nauka\" 1983

Rockafellar,R.T.: Convex Analysis, Princeton University Press 197O, rus. překlad z r. 1973 vydalo nakl. \"Mir\" Moskva

Hamala,M.: Nelineárne programovanie,2.vyd.,Alfa Bratislava

Himmelblau,D.M.: Applied Nonlinear Programming,McGraw-Hill 1972,rus.překlad vzd. nakl. \"Mir\" Moskva 1975

Ašmanov,S.A.,Timochov,A.V.: Těorija optimizacii v zadačach i upražněnijach, Moskva \"Nauka\", 1991

Syllabus -

Last update: T_KAM (20.04.2007)

1. Basic properties of convex sets.

2. Basic properties of convex functions.

3. Convex optimization, Kuhn-Tucker theory.

4. Some generalizations, quasi-convex and pseudo- convex functions.

5. Non-differentiable convex functions, subgradients.

6. Minimization of functions of one variable.

7. Quadratic optimization.

8. Methods of feasible direction.

9. Gradient methods.

10. Sequential unconstrained minimization techniques, penalty and barrier

functions.

11. Some method of discrete optimization.

12. Selected approaches to multi-criterion optimization problems.