Abstract
This paper studies a new nonconvex optimization problem aimed at recovering high-dimensional covariance matrices with a low rank plus sparse structure. The objective is composed of a smooth nonconvex loss and a nonsmooth composite penalty. A number of structural analytic properties of the new heuristics are presented and proven, thus providing the necessary framework for further investigating the statistical applications. In particular, the first and the second derivative of the smooth loss are obtained, its local convexity range is derived, and the Lipschitzianity of its gradient is shown. This opens the path to solve the described problem via a proximal gradient algorithm.
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