7 citations · 15 across the 13 of their papers we have counts for
15 papers
A distributed semismooth Newton based augmented Lagrangian method for distributed optimization
Qihao Ma, Chengjing Wang, Peipei Tang +2
This paper proposes a novel distributed semismooth Newton based augmented Lagrangian method for solving a class of optimization problems over networks, where the global objective i…
Complete Characterizations of Well-Posedness in Parametric Composite Optimization
Boris S. Mordukhovich, Peipei Tang, Chengjing Wang
This paper characterizes the well-posedness of Karush-Kuhn-Tucker system for perturbed composite optimization. Using the parabolic regularity, we introduce a novel second-order var…
An augmented Lagrangian method for strongly regular minimizers in a class of convex composite optimization problems
Chengjing Wang, Peipei Tang
In this paper, we study a class of convex composite optimization problems. We begin by characterizing the equivalence between the primal/dual strong second-order sufficient conditi…
Second-Order Characterizations of Tilt Stability in Composite Optimization
Boris S. Mordukhovich, Peipei Tang, Chengjing Wang
Tilt stability is a fundamental concept of variational analysis and optimization that plays a pivotal role in both theoretical issues and numerical computations. This paper investi…
Characterizations of Strong Variational Sufficiency in General Models of Composite Optimization
Boris S. Mordukhovich, Peipei Tang, Chengjing Wang
This paper investigates a recently introduced notion of strong variational sufficiency in optimization problems whose importance has been highly recognized in optimization theory,…
Perturbation analysis of a class of composite optimization problems
Peipei Tang, Chengjing Wang
In this paper, we study the perturbation analysis of a class of composite optimization problems, which is a very convenient and unified framework for developing both theoretical an…
An inexact proximal majorization-minimization Algorithm for remote sensing image stripe noise removal
Chengjing Wang, Xile Zhao, Qingsong Wang +2
The stripe noise existing in remote sensing images badly degrades the visual quality and restricts the precision of data analysis. Therefore, many destriping models have been propo…
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
Chengjing Wang, Peipei Tang, Wenling He +1
Graphical models have exhibited their performance in numerous tasks ranging from biological analysis to recommender systems. However, graphical models with hub nodes are computatio…
An efficient algorithm for the norm based metric nearness problem
Peipei Tang, Bo Jiang, Chengjing Wang
Given a dissimilarity matrix, the metric nearness problem is to find the nearest matrix of distances that satisfy the triangle inequalities. This problem has wide applications, suc…
A dual semismooth Newton based augmented Lagrangian method for large-scale linearly constrained sparse group square-root Lasso problems
Chengjing Wang, Peipei Tang
Square-root Lasso problems are proven robust regression problems. Furthermore, square-root regression problems with structured sparsity also plays an important role in statistics a…
A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems
Peipei Tang, Chengjing Wang, Bo Jiang
In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex tuning-free robust regression problems. The basic idea is to apply the prox…
A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machines
Dunbiao Niu, Chengjing Wang, Peipei Tang +2
Support vector machines (SVMs) are successful modeling and prediction tools with a variety of applications. Previous work has demonstrated the superiority of the SVMs in dealing wi…
A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems
Peipei Tang, Chengjing Wang, Defeng Sun +1
In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for these problems. Ou…
A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing
Longfei Ren, Chengjing Wang, Peipei Tang +1
Since sparse unmixing has emerged as a promising approach to hyperspectral unmixing, some spatial-contextual information in the hyperspectral images has been exploited to improve t…
A Dual Alternating Direction Method of Multipliers for Image Decomposition and Restoration
Qingsong Wang, Chengjing Wang, Peipei Tang +1
In this paper, we develop a dual alternating direction method of multipliers (ADMM) for an image decomposition model. In this model, an image is divided into two meaningful compone…