As is well known, the alternating direction method of multipliers (ADMM) is one of the most
famous distributed algorithms for solving the convex optimization problems. In this paper, by …

We consider the problem of minimizing the sum of a smooth function and a composition of a
zero-one loss function with a linear operator, namely the zero-one composite optimization …

Recent advance in linear support vector machine with the 0-1 soft-margin loss (L 0/1-SVM)
shows the ability to solve the 0-1 loss problem directly. However, its theoretical and …

With the exploration and development of marine resources, sonar imaging technology is
gaining increasing attention. Multiplicative speckle noise is often widely distributed in sonar …

Sparse logistic regression is for classification and feature selection simultaneously. Although
many studies have been done to solve 1-regularized logistic regression, there is no …

In this paper, we focus on the two-block nonconvex and nonsmooth optimization with linear
constraints, where the objective function is the sum of a convex but nonsmooth function and …

In this paper, a partial Bregman alternating direction method of multipliers (ADMM) with a
general relaxation factor α∈(0, 1+ 5 2) is proposed for structured nonconvex and …

This paper develops an adaptive Proximal Alternating Direction Method of Multipliers (P-
ADMM) for solving linearly-constrained, weakly convex, composite optimization problems …

Recent advance on linear support vector machine with the 0-1 soft margin loss ($ L_ {0/1} $-
SVM) shows that the 0-1 loss problem can be solved directly. However, its theoretical and …

This paper proposes and analyzes a dampened proximal alternating direction method of
multipliers (DP. ADMM) for solving linearly constrained nonconvex optimization problems …