PV027 Optimization

Faculty of Informatics
Spring 2026
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
In-person direct teaching
Teacher(s)
doc. RNDr. Tomáš Brázdil, Ph.D., MBA (lecturer)
Bc. Adam Kukučka (seminar tutor)
RNDr. Vít Musil, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Tomáš Brázdil, Ph.D., MBA
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Tue 17. 2. to Tue 12. 5. Tue 8:00–9:50 A218
  • Timetable of Seminar Groups:
PV027/01: Mon 23. 2. to Mon 18. 5. Mon 14:00–15:50 A319, V. Musil
PV027/02: Wed 25. 2. to Wed 13. 5. Wed 18:00–19:50 A220, A. Kukučka
Prerequisites
Prerequisites: mathematical analysis MB152 Differential and Integral Calculus and linear algebra MB151 Linear Models.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 32 fields of study the course is directly associated with, display
Abstract
This is a basic course on methods of mathematical optimization.
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.
Key topics
  • Unconstrained optimization: Nelder--Mead method, steepest descent, Newton's method, quasi-Newton methods.
  • Linear programming, Simplex method. Integer programming, branch and bound method, Gomory cuts.
  • Nonlinear constrained optimization: Lagrange multipliers, penalty methods, sequential quadratic programming.
Study resources and literature
  • Martins, Joaquim R. R. A., and Andrew Ning. Engineering Design Optimization. Cambridge University Press, 2021
  • NOCEDAL, Jorge and Stephen J. WRIGHT. Numerical optimization. 2nd ed. New York: Springer, 2006, xxii, 664. ISBN 1493937111. info
Approaches, practices, and methods used in teaching
Lectures and tutorials focused on solving examples.
Method of verifying learning outcomes and course completion requirements
oral examination
Language of instruction
English
Further Comments
The course is taught annually.
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 2020, Autumn 2022, Spring 2024, Spring 2025.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2026/PV027