Search Results for author:
Found 8 papers, 0 papers with code
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm
To efficiently estimate the hub graphical models, we introduce a two-phase algorithm.
An efficient algorithm for the $\ell_{p}$ norm based metric nearness problem
Due to the high memory requirement for the storage of the matrix related to the metric constraints, we take advantage of the special structure of the matrix and do not need to store the corresponding constraint matrix.
A dual semismooth Newton based augmented Lagrangian method for large-scale linearly constrained sparse group square-root Lasso problems
Square-root Lasso problems are proven robust regression problems.
A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems
In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex tuning-free robust regression problems.
Estimation of sparse Gaussian graphical models with hidden clustering structure
The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities.
A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machines
Support vector machines (SVMs) are successful modeling and prediction tools with a variety of applications.
A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems
In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for these problems.
A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing
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 the performance of the unmixing recently.
