45 citations · 52 across the 14 of their papers we have counts for
14 papers
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…
Modified lp-norm regularization minimization for sparse signal recovery
Angang Cui, Jigen Peng, Haiyang Li
In numerous substitution models for the -norm minimization problem , the -norm minimization with have been considered as the most natural c…
Recovering Sparse Nonnegative Signals via Non-convex Fraction Function Penalty
Angang Cui, Haiyang Li, Meng Wen +1
Many real world practical problems can be formulated as -minimization problems with nonnegativity constraints, which seek the sparsest nonnegative signals to underdetermi…
Generalized singular value thresholding operator to affine matrix rank minimization problem
Angang Cui, Haiyang Li, Jigen Peng +1
It is well known that the affine matrix rank minimization problem is NP-hard and all known algorithms for exactly solving it are doubly exponential in theory and in practice due to…
Minimization of fraction function penalty in compressed sensing
Haiyang Li, Qian Zhang, Angang Cui +1
In the paper, we study the minimization problem of a non-convex sparsity promoting penalty function $$P_{a}(x)=\sum_{i=1}^{n}p_{a}(x_{i})=\sum_{i=1}^{n}\frac{a|x_{i}|}{1+a|x_{i}|}$…
Non-convex Fraction Function Penalty: Sparse Signals Recovered from Quasi-linear Systems
Angang Cui, Jigen Peng, Haiyang Li
The goal of compressed sensing is to reconstruct a sparse signal under a few linear measurements far less than the dimension of the ambient space of the signal. However, many real-…
Exact recovery low-rank matrix via transformed affine matrix rank minimization
Angang Cui, Jigen Peng, Haiyang Li
The goal of affine matrix rank minimization problem is to reconstruct a low-rank or approximately low-rank matrix under linear constraints. In general, this problem is combinatoria…
Affine matrix rank minimization problem via non-convex fraction function penalty
Angang Cui, Jigen Peng, Haiyang Li +2
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-har…
An inertial primal-dual fixed point algorithm for composite optimization problems
Meng Wen, Yu-Chao Tang, Jigen Peng
We consider an inertial primal-dual fixed point algorithm (IPDFP) to compute the minimizations of the following Problem (1.1). This is a full splitting approach, in the sense that…