Network algorithms and optimisation

1.

Subject title

Network algorithms and optimisation

Мрежни алгоритми и оптимизација

2.

Code

m23_w_036

3.

Study program

Cloud Computing, Data science in computer science and engineering, IT management, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Inteligent Systems, Computer Science, Statistics and Data Analytics, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering, Internet Technologies and cyber security,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

ворн. проф. д-р Александра Дединец ворн. проф. д-р Мирослав Мирчев

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The goal of the program is for students to study network algorithms and optimization methods in networks of different types, as well as their application to real problems in communication and other types of complex networks, like information, infrastructure, social and biological networks.

11.

Subject content:

Introduction to network algorithms and optimization. Representation of various real problems in the form of graphs. Problems of shortest paths, minimum cost and maximum flow. Linear programming, simplex method and duality in network problems. Nonlinear Network Optimization, Convex Network Problems and Network Problems with Integer Constraints. Representation and resolution of various network problems such as routing, resource allocation and topological design. Gradient algorithms in network design. Decomposition techniques. Heuristic and metaheuristic algorithms, such as evolutionary algorithms, swarm intelligence, etc. Optimization using graph neural networks. Case studies such as algorithms and optimization in optical networks, internet, as well as in other real networks: transport, energy, financial, social, etc.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, практични вежби, тимска работа, пример случаи, поканети предавачи, самостојна изработка на проектна задача и семинарска работа и електронско учење

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

45 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

50 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

40 hours

17.

Grading method

17.1.

Tests

45 points

17.2.

Seminar work / project (presentation: written and oral)

50 points

17.3.

Activities and learning

10 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

реализирани активности

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

NULL

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature