7.5 credits cr.
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Optimization problems can be found in many quantative disciplines like economics and engineering.
The main focus of the course is convex analysis and a rather modern treatment of optimization problems. It covers basic convex analysis and Lagrange duality theory with their applications in linear and nonlinear programming problems with and without constraints and a touch to modern convex optimization theory. It also provides links to other specific optimization problems such as matrix game, integer programming and dynamic programming.
The contents of the course may be applied in modelling and computation nearly everywhere when mathematical models or computations can not be made exact. In particular it provides a solid theoretic background and skill for understanding nature and mathematical structure of different problems so that a practical problems can be tackled successfully. An apparent example of such is understanding (big) data to make optimization algorithms work for example in Machine learning.
The course consists of one element.
Instruction is given in the form of lectures and exercises.
The course is assessed through written examination.
A list of examiners can be found on
ScheduleThe schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
Course literatureNote that the course literature can be changed up to two months before the start of the course.