PV027 Optimization

Faculty of Informatics
Autumn 2020
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Tue 10:00–11:50 A217
Prerequisites
Prerequisites: mathematical analysis MB001 Calculus II and linear algebra MB003 Linear Algebra and Geometry I.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 40 student(s).
Current registration and enrolment status: enrolled: 0/40, only registered: 0/40, only registered with preference (fields directly associated with the programme): 0/40
fields of study / plans the course is directly associated with
there are 71 fields of study the course is directly associated with, display
Course objectives
This is a basic course on methods of mathematical optimization and their practical use.
Graduate will gain orientation in methods of mathematical optimization.
Learning outcomes
Graduate will be able to select appropriate optimization method to solve a particular problem.
Graduate will be able to explain principles of optimization methods.
Syllabus
  • Unconstrained optimization: Nelder--Mead method, steepest descent, Newton methods, conjugate gradient, trust region methods. Least squares problem and analysis of experimental data.
  • Linear programming, revised Simplex method, interior point methods. Applications of linear programming. Integer programming, branch and bound method. Dynamic programming.
  • Nonlinear constrained optimization: penalty functions, quadratic programming, sequential quadratic programming method.
  • Global optimization: simulated annealing, genetic algorithms, diffusion equation method.
Literature
  • FLETCHER, R. Practical methods of optimization. 1st ed. Chichester: John Wiley & Sons, 1987, xiv, 436. ISBN 0471915475. info
Teaching methods
Lectures and trainings focused on solving of examples.
Assessment methods
credit for home work, final written examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught once in two years.
Teacher's information
http://ncbr.chemi.muni.cz/~svobodova/vyuka/optimalizace
The course is also listed under the following terms Spring 2004, Spring 2006, Spring 2008, Spring 2010, Spring 2011, Autumn 2012, Autumn 2014, Autumn 2016, Autumn 2018, Autumn 2022, Spring 2024, Spring 2025.
  • Enrolment Statistics (Autumn 2020, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2020/PV027