Course detail
Optimization - Mathematical Programming
FSI-9OMPAcad. year: 2022/2023
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
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Basic MSc. knowledge of Calculus, linear algebra, probability, statistics and numerical methods applied to engineering disciplines.
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Paradalos et al.: Handbook of Optimization. Wiley and Sons
Williams,H.P.: Model Building in Mathematical Programming. Wiley and Sons
Recommended reading
Popela,P.: Lineární programování v kostce. sylabus, 2015, PDF
Popela,P.: Nonlinear programming. VUT sylabus, 2021, PDF
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Linear models
3. Special (network flow and integer) models
4. Nonlinear models
5. General models (parametric, multicriteria, nondeterministic,
dynamic, hierarchical)