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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.