FI:PV027 Optimization - Course Information

PV027 Optimization

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

Fri 12:00–13:50 A318

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 50 student(s).
Current registration and enrolment status: enrolled: 18/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50

fields of study / plans the course is directly associated with

• Image Processing and Analysis (programme FI, N-VIZ)
• Applied Informatics (programme FI, N-AP)
• Information Technology Security (eng.) (programme FI, N-IN)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics and systems biology (programme FI, N-UIZD)
• Bioinformatics (programme FI, N-AP)
• Computer Games Development (programme FI, N-VIZ_A)
• Computer Graphics and Visualisation (programme FI, N-VIZ_A)
• Computer Networks and Communications (programme FI, N-PSKB_A)
• Cybersecurity Management (programme FI, N-RSSS_A)
• Discrete algorithms and models (programme FI, N-TEI)
• Formal analysis of computer systems (programme FI, N-TEI)
• Graphic design (programme FI, N-VIZ)
• Graphic Design (programme FI, N-VIZ_A)
• Hardware Systems (programme FI, N-PSKB_A)
• Hardware systems (programme FI, N-PSKB)
• Image Processing and Analysis (programme FI, N-VIZ_A)
• Information security (programme FI, N-PSKB)
• Information Systems (programme FI, N-IN)
• Information Security (programme FI, N-PSKB_A)
• Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
• Parallel and Distributed Systems (programme FI, N-IN)
• Computer graphics and visualisation (programme FI, N-VIZ)
• Computer Graphics (programme FI, N-IN)
• Computer Networks and Communication (programme FI, N-IN)
• Computer Networks and Communications (programme FI, N-PSKB)
• Computer Systems (programme FI, N-IN)
• Principles of programming languages (programme FI, N-TEI)
• Embedded Systems (eng.) (programme FI, N-IN)
• Embedded Systems (programme FI, N-IN)
• Cybersecurity management (programme FI, N-RSSS)
• Services development management (programme FI, N-RSSS)
• Software Systems Development Management (programme FI, N-RSSS)
• Services Development Management (programme FI, N-RSSS_A)
• Service Science, Management and Engineering (eng.) (programme FI, N-AP)
• Service Science, Management and Engineering (programme FI, N-AP)
• Software Systems Development Management (programme FI, N-RSSS_A)
• Software Systems (programme FI, N-PSKB_A)
• Software systems (programme FI, N-PSKB)
• Machine learning and artificial intelligence (programme FI, N-UIZD)
• Theoretical Informatics (programme FI, N-IN)
• Teacher of Informatics and IT administrator (programme FI, N-UCI)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Computer Games Development (programme FI, N-VIZ)
• Processing and analysis of large-scale data (programme FI, N-UIZD)
• Image Processing (programme FI, N-AP)
• Natural language processing (programme FI, N-UIZD)

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

• Enrolment Statistics (Autumn 2022, recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2022/PV027