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Sovellettu matematiikka: Optimointi ja verkkomallit (3cr)

Code

General information

Timing: 01.08.2019 - 31.12.2019; Implementation has ended.

Number of ECTS credits allocated: 3 cr

Local portion: 3 cr

Mode of delivery: Face-to-face

Unit: School of Technology

Teaching languages: Finnish

Degree programmes: Bachelor's Degree Programme in Information and Communications Technology

Teachers: Harri Varpanen

Groups: TTV17S4
Tieto- ja viestintätekniikka; TTV17S3
Tieto- ja viestintätekniikka; TTV17S5
Tieto- ja viestintätekniikka; TTV17S1
Tieto- ja viestintätekniikka

Course: TTZM0330

Unfortunately, no reservations were found for the realization Sovellettu matematiikka: Optimointi ja verkkomallit TTZM0330-9S0V1. It's possible that the reservations have not yet been published or that the realization is intended to be completed independently.

Evaluation scale

Pass/Fail

Objective

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

Materials

Niemi: Optimointi ja verkkomallit

Completion alternatives

Exercises 50% Final exam 50%

Student workload

Face-to-face instruction 39 h,independent work 42 h.

Assessment criteria, satisfactory (1)

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.

Assessment criteria, approved/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.