FI:PV027 Optimization - Course Information

PV027 Optimization

Extent and Intensity

2/1. 3 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

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. RNDr. Luděk Matyska, CSc.

Timetable

Mon 16:00–18:50 B411

Prerequisites

Prerequisites: mathematical analysis M001 Calculus II and linear algebra M004 Linear Algebra and Geometry II.

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 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30

fields of study / plans the course is directly associated with

• Applied Informatics (programme FI, B-AP)
• Applied Informatics (programme FI, N-AP)
• Informatics with another discipline (programme FI, B-BI)
• Informatics with another discipline (programme FI, B-FY)
• Informatics with another discipline (programme FI, B-GE)
• Informatics with another discipline (programme FI, B-GK)
• Informatics with another discipline (programme FI, B-CH)
• Informatics with another discipline (programme FI, B-IO)
• Informatics with another discipline (programme FI, B-MA)
• Informatics with another discipline (programme FI, B-SO)
• Informatics with another discipline (programme FI, B-TV)
• Informatics (programme FI, B-IN)
• Informatics (programme FI, M-IN)
• Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS)

Course objectives

This is a basic course on methods of mathematical optimization and their practical use.

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

Language of instruction

Czech

Further Comments

The course is taught once in two years.

Teacher's information

http://ncbr.chemi.muni.cz/~n19n/vyuka/optimalizace

• Enrolment Statistics (Spring 2006, recent)
  
• Permalink: https://is.muni.cz/course/fi/spring2006/PV027