SubjectsSubjects(version: 853)
Course, academic year 2019/2020
  
Introduction to Mathematical Optimization - B413009
Title: Základy matematické optimalizace
Guaranteed by: Department of Mathematics (413)
Actual: from 2019
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2 C+Ex [hours/week]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Level:  
For type:  
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Turzík Daniel doc. RNDr. CSc.
Maxová Jana RNDr. Ph.D.
Class: Předměty pro matematiku
Interchangeability : N413009
Examination dates   Schedule   
Annotation -
Last update: Kubová Petra Ing. (01.05.2019)
The subject is designed for all students in bachelor programmes, especially aimed at economics. Students learn basic notions and algorithms in mathematical optimization.
Aim of the course -
Last update: Kubová Petra Ing. (01.05.2019)

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 .

Literature -
Last update: Kubová Petra Ing. (01.05.2019)

R: Turzík: Matematika III Základy optimalizace, skripta, VŠCHT Praha, 1999, ISBN:80-7080-363-0

Learning resources -
Last update: Kubová Petra Ing. (01.05.2019)

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

Teaching methods -
Last update: Kubová Petra Ing. (01.05.2019)

Lectures and seminars

Syllabus -
Last update: Kubová Petra Ing. (01.05.2019)

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.

Registration requirements -
Last update: Maxová Jana RNDr. Ph.D. (15.05.2019)

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

 
VŠCHT Praha