1 citations · 1 across the 4 of their papers we have counts for
5 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 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…
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…