470-6503/02 – Methods of Optimization (MO)

Gurantor departmentDepartment of Applied MathematicsCredits10
Subject guarantorprof. RNDr. Zdeněk Dostál, DSc.Subject version guarantorprof. RNDr. Zdeněk Dostál, DSc.
Study levelpostgraduateRequirementChoice-compulsory
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2015/2016Year of cancellation
Intended for the facultiesFEI, FSIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
BER95 doc. Ing. Petr Beremlijski, Ph.D.
DOS35 prof. RNDr. Zdeněk Dostál, DSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Combined Examination 28+0

Subject aims expressed by acquired skills and competences

The student will be able to recognize basic classes of optimization problems and will understand conditions of their solvability and correct formulation. Effective algorithms, heuristics and software will be presented in an extent that is useful for solving engineering problems, so that the student will be able to apply their knowledge to the solution of practical problems.

Teaching methods

Lectures
Project work

Summary

Optimization methods are basic tools for improving design and technology. The students will learn about basic optimization problems, conditions of their solvability and correct formulation. Effective algorithms, heuristics and software will be presented in an extent that is useful for the soluving engineering problems.

Compulsory literature:

M. S: Bazaraa, C. M. Shetty: Nonlinear programming, J. Wiley, New York 1979, ruský překlad Mir Moskva 1982. J. Nocedal, S. Wright, Numerical Optimization, Springer, New York 2005. R. Fletcher: Practical Methods of Optimization, John Wiley & sons, Chichester 1997.

Recommended literature:

D. P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont 1999. Z. Dostal, Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities Springer, New York 2009. I. Griva, S. G. Nash, A. Sofer, Linear and Nonlinear Optimization, Second Edition, SIAM , Philadelphia 2008. D. T. Pham and D. Karaboga, Intelligent Optimization Techniques, Springer, London 2000.

Way of continuous check of knowledge in the course of semester

Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.

E-learning

Další požadavky na studenta

Additional requirements for the student are not.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Introduction to the calculus of variations. Linear spaces, functionals and their differentials (Fréchet, Gateaux), etc..

Conditions for subject completion

Combined form (validity from: 2015/2016 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

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Occurrence in special blocks

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