55 citations · 62 across the 12 of their papers we have counts for
12 papers · 1 filter
Adaptive iterative singular value thresholding algorithm to low-rank matrix recovery
Angang Cui, Jigen Peng, Haiyang Li
The problem of recovering a low-rank matrix from the linear constraints, known as affine matrix rank minimization problem, has been attracting extensive attention in recent years.…
Nonconvex fraction function recovery sparse signal by convex optimization algorithm
Angang Cui, Jigen Peng, Haiyang Li +1
In this paper, we will generate a convex iterative FP thresholding algorithm to solve the problem . Two schemes of convex iterative FP thresholding algorithms are gener…
A non-convex approach to low-rank and sparse matrix decomposition
Angang Cui, Meng Wen, Haiyang Li +1
In this paper, we develop a nonconvex approach to the problem of low-rank and sparse matrix decomposition. In our nonconvex method, we replace the rank function and the -nor…
Iterative thresholding algorithm based on non-convex method for modified lp-norm regularization minimization
Angang Cui, Jigen Peng, Haiyang Li +2
Recently, the -norm regularization minimization problem has attracted great attention in compressed sensing. However, the -norm in problem…
A New Nonconvex Strategy to Affine Matrix Rank Minimization Problem
Angang Cui, Jigen Peng, Haiyang Li +2
The affine matrix rank minimization (AMRM) problem is to find a matrix of minimum rank that satisfies a given linear system constraint. It has many applications in some important a…
Sparse Portfolio Selection via Non-convex Fraction Function
Angang Cui, Jigen Peng, Chengyi Zhang +2
In this paper, a continuous and non-convex promoting sparsity fraction function is studied in two sparse portfolio selection models with and without short-selling constraints. Firs…