Angang Cui

Affine matrix rank minimization problem via non-convex fraction function penalty

Affine matrix rank minimization problem is a fundamental problem with a lot of important applications in many fields. It is well known that this problem is combinatorial and NP-hard in general. In this paper, a continuous promoting low rank non-convex fraction function is studied to replace the rank function in this NP-hard problem. Inspired by our former work in compressed sensing, an iterative singular value thresholding algorithm is proposed to solve the regularization transformed affine matrix rank minimization problem. For different

A Primal Douglas–Rachford Splitting Method for the Constrained Minimization Problem in Compressive Sensing

a>0

Minimization of fraction function penalty in compressed sensing

, we could get a much better result by adjusting the different value of

A nonconvex approach to low-rank and sparse matrix decomposition with application to video surveillance.

a

Modified lp-norm regularization minimization for sparse signal recovery

, which is one of the advantages for the iterative singular value thresholding algorithm compared with some state-of-art methods. Some convergence results are established and numerical experiments show that this thresholding algorithm is feasible for solving the regularization transformed affine matrix rank minimization problem. Moreover, we proved that the value of the regularization parameter

Sparse Portfolio Selection via Non-convex Fraction Function

\lambda>0

Generalized singular value thresholding operator to affine matrix rank minimization problem.

can not be chosen too large. Indeed, there exists

A New Nonconvex Strategy to Affine Matrix Rank Minimization Problem

\bar{\lambda}>0

Quasi-linear compressed sensing via non-convex fraction function penalty

such that the optimal solution of the regularization transformed affine matrix rank minimization problem is equal to zero for any

Sparse nonnegative solution of underdetermined linear system via non-convex fraction function penalty

\lambda>\bar{\lambda}