PV027 Optimization

Faculty of Informatics
Spring 2025
Extent and Intensity
2/1/1. 4 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. (lecturer)
RNDr. Vít Musil, Ph.D. (seminar tutor)
doc. RNDr. Radka Svobodová, Ph.D. (assistant)
Guaranteed by
doc. RNDr. Tomáš Brázdil, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Prerequisites
Prerequisites: mathematical analysis MB151 Linear models and linear algebra MB153 Statistics I.
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 31 fields of study the course is directly associated with, display
Course objectives
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.
Syllabus
  • 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.
Literature
  • FLETCHER, R. Practical methods of optimization. 1st ed. Chichester: John Wiley & Sons, 1987, xiv, 436. ISBN 0471915475. info
Teaching methods
Lectures and tutorials focused on solving examples.
Assessment methods
oral examination
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
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.
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