—We present an alternative analysis of weighted 1 minimization for sparse signals with a nonuniform sparsity model, and extend our results to nuclear norm minimization for matric...
The matrix rank minimization problem has applications in many fields such as system identification, optimal control, low-dimensional embedding etc. As this problem is NP-hard in ...
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in machine learning, control theory, and discrete geometry. This c...
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-ran...
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Pa...
This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood a...