Applied Mathematics: Optimization and Network Models, 3 cr  TTZM0330
Credits
3 cr
Person in charge
Sirpa Alestalo
Course language
Finnish
Upcoming implementations
No upcoming implementations. See the syllabus for more information.
Learning outcomes of the course
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 nonlinear optimization problems.
Course contents
Competences
Directional and nondirectional network
Network cable coloring ?
Problems with scheduling
WelshPowell algorithm
Minimal tree
Shortest path
Dijkstra algorithm
BellmanFord algorithm
Network and routing
Flownets?
Maximim flow with minimal costs
FordFulkerson algorithm
Linear optimization
Simplex algorithm
Fundamentals of nonlinear optimization
Prerequisites and corequisites

Evaluation scale
Pass/Fail
Assessment criteria
Assessment criteria  grade 1 and 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.
Assessment criteria, passed/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.