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Simone Naldi (caponord2007gmail.com) Abstract: We consider the problem of minimizing a linear function over an affine section of the cone of positive semidefinite matrices, with the additional constraint that the feasible matrix has prescribed rank. When the rank constraint is active, this is a nonconvex optimization problem, otherwise it is a semidefinite program. Both find numerous applications especially in systems control theory and combinatorial optimization, but even in more general contexts such as polynomial optimization or real algebra. While numerical algorithms exist for solving this problem, such as interiorpoint or Newtonlike algorithms, in this paper we propose an approach based on symbolic computation. We design an exact algorithm for solving rankconstrained semidefinite programs, whose complexity is essentially quadratic on natural degree bounds associated to the given optimization problem: for subfamilies of the problem where the size of the feasible matrix is fixed, the complexity is polynomial in the number of variables. The algorithm works under assumptions on the input data: we prove that these assumptions are generically satisfied. We also implement it in Maple and discuss practical experiments. Keywords: Semidefinite programming, determinantal varieties, linear matrix inequalities, rank constraints, exact algorithms, computer algebra, polynomial optimization, spectrahedra, sums of squares. Category 1: Linear, Cone and Semidefinite Programming (Semidefinite Programming ) Citation: Download: [PDF] Entry Submitted: 02/01/2016 Modify/Update this entry  
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