Curricula

Objectives

The student knows the basic mathematical concepts related to networks as well as knows and understands the network algorithms presented in the course (See Course Contents) that enable to find the exact optimal solution. The student is able to form out of an optimization problem a linear model with solutions. The student understands the general principle of optimization and has acquainted themselves with some non-linear optimization problems.

Content

Competences
Directional and non-directional network
Network cable coloring ?
Problems with scheduling
Welsh-Powell algorithm
Minimal tree
Shortest path
Dijkstra algorithm
Bellman-Ford algorithm
Network and routing
Flownets?
Maximim flow with minimal costs
Ford-Fulkerson algorithm
Linear optimization
Simplex algorithm
Fundamentals of non-linear optimization

Time and location

Zoom / Teams

Learning materials and recommended literature

Opettaja antaa materiaalit opintojakson alussa / aikana.

Teaching methods

Luennot ja ohjaukset alkuviikosta luokassa, loppuviikosta Zoomissa (samat asiat kuin alkuviikosta, tallennetaan).

Viikoittaiset tehtävät, palautetaan kirjallisesti.

Lopputesti.

Läpäisykriteerit: kaikki tehtävät hyväksytysti palautettu, lopputesti läpäisty.

Python-koodia ja student-palvelinta käytetään havainnollistamaan algoritmeja. Aiempaa python-kokemusta ei tarvita.

Exam dates and retake possibilities

Sovitaan opintojakson alussa.

Student workload

Ohjaukset 30h, itsenäinen työskentely 51h.

Evaluation scale

Pass/Fail

Evaluation criteria, satisfactory (1-2)

All learning outcomes will be assessed based on both exercises and final exam.
Pass:
The student shows based on the exam and returned exercises both understanding of basic concepts and algorithms and their ability to apply them into practice. With the items of assessment, the student demonstrates ability to solve a linear optimization problem and is able to identify the constraints of linear method.
In order to pass the course, a minimum of 50 % of the maximum points of items of assessment are required.

Evaluation criteria, pass/failed

All learning outcomes will be assessed based on both exercises and final exam.
Pass: The student shows based on the exam and returned exercises both understanding of basic concepts and algorithms and his/her ability to apply them to practice. With the items of assessment, the student demonstrates ability to solve a linear optimization problem and is able to identify the constraints of linear method. In order to pass the course, a minimum of 50 % of the maximum points of items of assessment are required.

Prerequisites

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