Course detail
Optimization - Mathematical Programming
FSI-9OMP Acad. year: 2022/2023 Winter semester
The solution of many actual engineering problems cannot be achieved without the knowledge of mathematical foundations of optimization.
The course focuses on mathematical programming areas. The presented material is related to theory (convexity, linearity, differentiability, and stochasticity), algorithms (deterministic, stochastic, heuristic), the use of
specialized software, and modelling. All important types of mathematical models are discussed, involving linear, discrete, convex, multicriteria and stochastic. Every year, the course is updated by including the recent topics related to areas interests of students.
Language of instruction
Czech
Supervisor
Department
Learning outcomes of the course unit
Students will learn fundamental theoretical knowledge about optimization modelling. The knowledge will be applied in applications.
Prerequisites
Introductory knowledge of mathematical modelling of engineering systems.
Basic MSc. knowledge of Calculus, linear algebra, probability, statistics and numerical methods applied to engineering disciplines.
Planned learning activities and teaching methods
The course is taught through lectures explaining the basic principles and theory of the discipline.
Assesment methods and criteria linked to learning outcomes
The exam runs in the form of workshop. The paper oral and written presentation is required and specialized discussion is assumed.
Aims
The course is focused on knowledge useful for engineering optimization models. Motivation of presented concepts is emphasized.
Specification of controlled education, way of implementation and compensation for absences
The faculty rules are applied.
The study programmes with the given course
Programme D-ENE-P: Power Engineering, Doctoral, recommended course
Programme D-ENE-K: Power Engineering, Doctoral, recommended course
Type of course unit
Lecture
20 hours, optionally
Teacher / Lecturer
Syllabus
1. Basic models
2. Linear models
3. Special (network flow and integer) models
4. Nonlinear models
5. General models (parametric, multicriteria, nondeterministic,
dynamic, hierarchical)