SubjectsSubjects(version: 952)
Course, academic year 2021/2022
  
Introduction to Mathematical Optimization - AB413009
Title: Introduction to Mathematical Optimization
Guaranteed by: Department of Mathematics, Informatics and Cybernetics (446)
Faculty: Faculty of Chemical Engineering
Actual: from 2021 to 2022
Semester: winter
Points: winter s.:5
E-Credits: winter s.:5
Examination process: winter s.:
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
Teaching methods: full-time
Level:  
For type:  
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Maxová Jana RNDr. Ph.D.
Class: Předměty pro matematiku
Interchangeability : N413009
Annotation -
The subject is designed for all students in bachelor programmes, especially aimed at economics. Students learn basic notions and algorithms in mathematical optimization.
Last update: Kubová Petra (17.01.2020)
Aim of the course -

General skills:

1. basic terms in mathematical optimiztion

2. knowledge and understanding of basic algorithms

3. individual problem solving

4. basic mathematical background for formulation and solving of optimization problems

5. numerical algorithms .

Last update: Kubová Petra (17.01.2020)
Literature -

A: Dimitris Bertsimas and John N. Tsitsiklis : Introduction to Linear Optimization, 1997, ISBN-10: 1-886529-19-1

A: Alexander Schrijver : Theory of Linear and Integer Programming, New York 1986, ISBN-10: 0471982326

Last update: MAXOVAJ (23.01.2020)
Learning resources -

http://www.vscht.cz/mat/ZMO/Optim_maple.html

https://iti.mff.cuni.cz/series/2006/311.pdf

Last update: MAXOVAJ (23.01.2020)
Teaching methods -

Lectures and seminars

Last update: Kubová Petra (17.01.2020)
Syllabus -

1. Problems of mathematical optimization.

2. Linear programming.

3. Convex polyhedra.

4. Simplex method.

5. Duality of linear programming.

6. Integer programming, totally unimodular matrices.

7. Basic notions of graph theory.

8. Shortest path problem.

9. Tree, spanning tree, greedy algorithm.

10. Discrete optimalization problems as problems of integer programming.

11. Nonlinear optimization.

12. Kuhn-Tucker conditions.

13. Numerical methods for nonlinear programming.

14. Convex functions, positive semidefinite matrices.

Last update: Kubová Petra (17.01.2020)
Registration requirements -

Mathematics A, Mathematics B (or Mathematics I, Mathematics II)

Last update: Kubová Petra (17.01.2020)
Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 0.5 14
Práce na individuálním projektu 1 28
Příprava na zkoušku a její absolvování 1.5 42
Účast na seminářích 1 28
5 / 5 140 / 140
 
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