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A zero -norm optimization model is a mathematical program in which one either minimizes or restricts the number of certain sets of variables to being non -zero . Zero -norm optimization arises in various applications , such as compressive sensing , metabolic engineering , portfolio optimization and data mining . In these examples we find the most common form of zero -norm optimization : we minimize or restrict the number of allowed activities by minimizing or restricting the number of the respective { \em activity variables} that are allowed to being non -zero . In this thesis we study recent applications of zero -norm and models related to these optimization problems . We first discuss the applications and thereafter study the problem statement of the applications . Once the problem statement is understood we then see how the zero -norm model can be tackled to solve the problem . |
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