QIN Linxia, XIU Naihua, KONG Lingchen
In this paper, we emphasize on the nonconvex relaxation of the semidefinite matrix rank minimization problem and the involved properties. Firstly, we apply a nonconvex relaxation, the Schatten p-norm (0<p<1) semidefinite program, for solving the desired rank minimization problem. Next, by defining the semidefinite restricted isometry/orthogonal constant, we propose a sufficient condition for the uniqueness of the solution to (P). Finally, with the semidefinite restricted isometry property (semi-RIP), we give a sufficient condition under which the Schatten p-norm relaxation and the underlying matrix rank minimization share the unique common solution. In particular, for any 0<p<1, we obtain a uniform exact recovery condition.