545-0061/02 – Optimalization (Opt)
| Gurantor department | Department of Economics and Control Systems | Credits | 5 |
| Subject guarantor | doc. Ing. Pavel Staša, Ph.D. | Subject version guarantor | doc. Ing. Pavel Staša, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2007/2008 | Year of cancellation | 2015/2016 |
| Intended for the faculties | HGF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The aim of this course is to develop basic knowledge and optimization of dynamic systems. After a one-dimensional static optimization methods are discussed basic methods of multivariate optimization and linear programming. Following the theoretical foundations of dynamic optimization with a focus on cyber systems. Finally, students are familiar with using genetic algorithms, and applications of evolutionary optimization methods.
Teaching methods
Lectures
Seminars
Tutorials
Project work
Summary
The course develops basic questions of optimization of dynamic systems. after putting
one-dimensional static optimization methods are discussed basic methods
Multivariate optimization and linear programming. The following theoretical
foundations of dynamic optimization with a focus on cyber systems. Vzávěru
students are familiar with the use of genetic algorithms and applications
optimization methods.
Compulsory literature:
Recommended literature:
Literature recommended by the supervisor to the specific topic BP / DP
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
Individuální, dle bližší specifikace vedoucího cvičení.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1st Optimization problems, methods of solving
2nd Analytical methods of solving one-dimensional optimization problem
3rd The method of golden section and Fibonacci method
4th Static optimization functions of several variables, types of tasks and methods of solution
5th Solving multi-dimensional optimization problem without constraints
6th Lagrange function, its determination and significance for multi-dimensional optimization tasks
7th Solving problems with multi-dimensional optical constraints with equality
8th Khun-Tucker conditions, the derivation and meaning
9th Solving multi-dimensional problems with optical constraints in the form of inequality
10th The role of linear programming and its solution, 1 and 2 role
11th Vector optimization
12th Minimizing weighted targeted FCI
13th Dynamic program. - Recurrent Bellman equation
14th Extreme control, addressing the dynamics of the closed loop control with extreme controller
15th Genetic algorithms and their applications, evolutionary optimization methods
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.