17 citations · 30 across the 8 of their papers we have counts for
9 papers
Portfolio Selection by Joint Estimation-Robustness Optimization with the Return and Risk Constraints
Yingxiao Wang, Lingchen Kong, Selin D. Ahipasaoglu +1
In answering the main criticism of the classical robust optimization that the size of the uncertainty set has to be determined beforehand, a recently proposed Joint Estimation and…
Gradient Projection Newton Algorithm for Sparse Collaborative Learning Using Synthetic and Real Datasets of Applications
Jun Sun, Lingchen Kong, Shenglong Zhou
Exploring the relationship among multiple sets of data from one same group enables practitioners to make better decisions in medical science and engineering. In this paper, we prop…
Regularization parameter selection for low rank matrix recovery
Pan Shang, Lingchen Kong
Low rank matrix recovery is the focus of many applications, but it is a NP-hard problem. A popular way to deal with this problem is to solve its convex relaxation, the nuclear norm…
Tuning parameter selection rules for nuclear norm regularized multivariate linear regression
Pan Shang, Lingchen Kong
We consider the tuning parameter selection rules for nuclear norm regularized multivariate linear regression (NMLR) in high-dimensional setting. High-dimensional multivariate linea…
Sparse and Low-Rank Covariance Matrices Estimation
Shenglong Zhou, Naihua Xiu, Ziyan Luo +1
This paper aims at achieving a simultaneously sparse and low-rank estimator from the semidefinite population covariance matrices. We first benefit from a convex optimization which…
Exact Recovery for Sparse Signal via Weighted Minimization
Shenglong Zhou, Naihua Xiu, Yingnan Wang +1
Numerical experiments in literature on compressed sensing have indicated that the reweighted minimization performs exceptionally well in recovering sparse signal. In this pap…