19 citations · 24 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
A randomized intertial primal-dual fixed point algorithm for monotone inclusions
Meng Wen, Shigang Yue, Yuchao Tan +1
In this paper, we propose a randomized intertial block-coordinate primaldual fixed point algorithm to solve a wide array of monotone inclusion problems base on the modification of…
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…
Efficient primal-dual fixed point algorithm with dynamic stepsize for convex problems with applications to imaging restoration
Meng Wen, Shigang Yue, Yuchao Tang +1
We consider the problem of finding the minimization of the sum of a convex function and the composition of another convex function with a continuous linear operator from the view o…
A stochastic coordinate descent inertial primal-dual algorithm for large-scale composite optimization
Meng Wen, Yu-Chao Tang, Jigen Peng
We consider an inertial primal-dual algorithm to compute the minimizations of the sum of two convex functions and the composition of another convex function with a continuous linea…
A stochastic coordinate descent splitting primal-dual fixed point algorithm and applications to large-scale composite optimization
Meng Wen, Yu-Chao Tang, Jigen Peng
We consider the problem of finding the minimizations of the sum of two convex functions and the composition of another convex function with a continuous linear operator from the vi…
A stochastic coordinate descent primal-dual algorithm with dynamic stepsize for large-scale composite optimization
Meng Wen, Shigang Yue, Yuchao Tang +1
In this paper we consider the problem of finding the minimizations of the sum of two convex functions and the composition of another convex function with a continuous linear operat…
A splitting primal-dual proximity algorithm for solving composite optimization problems
Yu-Chao Tang, Chuan-Xi Zhu, Meng Wen +1
Our work considers the optimization of the sum of a non-smooth convex function and a finite family of composite convex functions, each one of which is composed of a convex function…