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Optimization, Advanced Course, 6 ECTS credits.
/Optimeringslära fortsättningskurs/
For:
CS
DAV
Mat
Y
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Prel. scheduled
hours: 38
Rec. self-study hours: 122
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Area of Education: Science
Main field of studies: Mathematics, Applied Mathematics,
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Advancement level
(G1, G2, A): G2
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Aim:
Optimization deals with mathematical theory and methods aiming at analyzing and solving decision problems that arise in technology, economy, medicine, etc. The course gives, together with the introductory course, a broad orientation of the field of optimization. After the course, the student shall:
- be able to identify optimization problems and classify them according to
their properties, into, for example, network problems or discrete
problems
- construct mathematical models of more complex optimization problems
- have knowledge about and be able to apply basic solution principles for
some classes of commonly appearing optimization problems, such as, for
example, the simplex method for network flows
- be able to use commonly available software for solving optimization
problems that appear regularly in applications
- be able to use relaxations to approximate optimization problems and
heuristic methods for finding feasible solutions, and be able to
estimate the optimal objective value through lower and upper bounds
- have good knowledge about practical applications of optimization
methodologies
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Prerequisites: (valid for students admitted to programmes within which the course is offered)
Introduction to optimization.
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.
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Supplementary courses:
Mathematical optimization.
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Organisation:
Lectures which include theory, problem solving and applications. Exercises which are intended to give individual training in problem solving. A laboratory course with emphasis on modelling and the use of optimization software.
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Course contents:
A continuation of the material presented in the introductory course. The course includes more advanced topics within mathematical modelling, network optimization, sensitivity analysis in linear programming, discrete optimization, nonlinear optimization, and Lagrangian relaxation. Some new topics are also included, such as dynamic programming and heuristics.
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Course literature:
Jan Lundgren, Mikael Rönnqvist & Peter Värbrand - Optimeringslära (Studentlitteratur, 2008)
Jan Lundgren, Mathias Henningsson & Mikael Rönnqvist - Optimeringslära övningsbok (Studentlitteratur, 2008)
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Examination: |
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Written examination
Labratory course
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4 ECTS
2 ECTS
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Course language is Swedish/English.
Department offering the course: MAI.
Director of Studies: Ingegerd Skoglund
Examiner: Oleg Burdakov
Link to the course homepage at the department
Course Syllabus in Swedish
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