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Automatic dualization


J. Löfberg

IMS Semidefinite Programming and its Applications

Many optimization problems gain from being interpreted and solved in either primal or dual form. For a user with a particular application, one of these forms is usually more natural for the modelling phase, but this is not always the most efficient one for computations. This talk presents an implementation in the optimization modeling tool YALMIP that allows the user to define conic optimization problems in a preferred format, and then automatically derive a YALMIP model of the dual of this problem, solve the dual, and recover original variables. Applications of this feature in sum-of-squares programming, control theory and learning theory problems will be given if time permits.


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