Course detail

Optization 1

FP-Vo1PAcad. year: 2017/2018

The course presents fundamental optimization models and methods for solving of technical problems. The principal ideas of mathematical programming are discussed: problem analysis, model building, solution search, and the interpretation of results. The course mainly deals with linear programming (polyhedral sets, simplex method, duality) and nonlinear programming (convex analysis, Karush-Kuhn-Tucker conditions, selected algorithms). Basic information about network flows and integer programming is included as well as further generalizations of studied mathematical programs.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

RNDr. Pavel Popela, Ph.D.

Department

Institute of Informatics (ÚI)

Learning outcomes of the course unit

The course is designed for mathematical engineers and it is useful for applied sciences students. Students will learn the theoretical background of fundamental topics in optimization (especially linear and non-linear programming). They will also made familiar with useful algorithms and interesting applications.

Prerequisites

Fundamental knowledge of principal concepts of Calculus and Linear Algebra in the scope of the mathematical engineering curriculum is assumed.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

Graded course-unit credit is awarded based on the result in a written exam involving modelling-related, computational-based, and theoretical questions. The short oral exam is also included.

Course curriculum

1. Introductory optimization: problem formulation and analysis, model building, theory.
2. Visualisation, algorithms, software, postoptimization.
3. Linear programming (LP): Convex and polyhedral sets.
4. LP: Feasible sets and related theory.
5. LP: The simplex method.
6. LP: Duality, sensitivity and parametric analysis.
7. Network flows modelling.
8. Introduction to integer programming.
9. Nonlinear programming (NLP): Convex functions and their properties.
10. NLP: Unconstrained optimization and line search algorithms.
11. NLP: Unconstrained optimization and related multivariate methods.
12. NLP: Constrained optimization and KKT conditions.
13. NLP: Constrained optimization and related multivariate methods.
14. Selected general cases.

Work placements

Not applicable.

Aims

The course objective is to emphasize optimization modelling together with solution methods. It involves problem analysis, model building, model description and transformation, and the choice of the algorithm. Introduced methods are based on the theory and illustrated by geometrical point of view.

Specification of controlled education, way of implementation and compensation for absences

The attendance at seminars is required as well as active participation. Passive or missing students are required to work out additional assignments.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Bazaraa et al.: Linear Programming and Network Flows, , Wiley 1990 (EN)
Bazaraa et al.: Nonlinear Programming, , Wiley 1993 (EN)
Dupačová et al.: Lineárne programovanie, Alfa 1990 (SK)

Recommended reading

Dvořák a kol.: Operační analýza, , 2002 (CS)
Charamza a kol.: Modelovací systém GAMS, , 1995 (CS)
Klapka a kol.: Metody operačního výzkumu, , 2000 (CS)

Classification of course in study plans

  • Programme BAK-KME Bachelor's

    branch BAK-MME , 3 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

RNDr. Pavel Popela, Ph.D.

Syllabus

Lectures consist of an explanation of basic principles, methodology of the discipline, problems and their solutions. Exercises promote the practical mastery of subject presented in lectures or assigned for individual study with the active participation of students.

Exercise

13 hod., compulsory

Teacher / Lecturer

RNDr. Pavel Popela, Ph.D.