352-0510/02 – Optimization (OS)
| Gurantor department | Department of Control Systems and Instrumentation | Credits | 6 |
| Subject guarantor | Ing. Jolana Škutová, Ph.D. | Subject version guarantor | Ing. Jolana Škutová, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 2 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2021/2022 | Year of cancellation | |
| Intended for the faculties | FS | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The main objective of the subject “Optimization” is acquainting students with methods of static and dynamic optimization. A student must be able to design of the objective function and propose the solution method. In the area of the dynamic optimization a student will be able to design so control, which ensures optimal control from the different point of view, e.g. energy, time, deviation etc.
Teaching methods
Lectures
Tutorials
Project work
Summary
Optimality criteria, conditions for optimality, constrains, forms of solution. The analytical and numerical methods of minimization of functions of single and several variables, equality constrains, inequality constrains. Kuhn-Tucker conditions, saddlepoint conditions. Minimizations of functionals, optimal control problems. Bellman’s principle of optimality and dynamic programming. Pontryagin’s minimum principle. Calculus of variations.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Credit: Passing two tests and elaboration of solutions of three programs.
Exam: written part (max. 45 points)
oral part (max. 20 points)
E-learning
Other requirements
Passing two tests and elaboration of three tasks.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Optimality criteria, conditions for optimality, constrains, forms of solution.
The analytical and numerical methods of minimization of functions of single and several variables, equality constrains, inequality constrains, Kuhn-Tucker conditions, saddle point conditions.
Minimization of functionals, optimal control problems.
Bellman’s principle of optimality and dynamic programming.
Pontryagin’s minimum principle.
Calculus of variations.
Neural Networks - models, architecture, backpropagation learning, optimization through neural networks.
Conditions for subject completion
Conditions for completion are defined only for particular subject version and form of study
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction