New smooth C-functions for symmetric cone complementarity problems. - Lingchen Kong; Naihua Xiu (Optim. Lett., 2007)

The solution set structure of monotone linear complementarity problems over second-order cone. - Lingchen Kong; Naihua Xiu; Jiye Han (Oper. Res. Lett., 2008)

A Regularized Smoothing Newton Method for Symmetric Cone Complementarity Problems. - Lingchen Kong; Jie Sun 001; Naihua Xiu (SIAM J. Optim., 2008)

Vector-Valued Implicit Lagrangian for Symmetric Cone complementarity Problems. - Lingchen Kong; Levent Tunçel; Naihua Xiu (Asia Pac. J. Oper. Res., 2009)

Sufficiency of linear transformations on Euclidean Jordan algebras. - Linxia Qin; Lingchen Kong; Jiye Han (Optim. Lett., 2009)

Sufficient Conditions for Low-rank Matrix Recovery, Translated from Sparse Signal Recovery - Lingchen Kong; Levent Tunçel; Naihua Xiu (CoRR, 2011)

Equivalent Conditions for Jacobian Nonsingularity in Linear Symmetric Cone Programming. - Lingchen Kong; Levent Tunçel; Naihua Xiu (J. Optim. Theory Appl., 2011)

Linear Program Relaxation of Sparse Nonnegative Recovery in Compressive Sensing Microarrays. - Linxia Qin; Naihua Xiu; Lingchen Kong; Yu Li (Comput. Math. Methods Medicine, 2012)

Existence and Uniqueness of Solutions for Homogeneous Cone Complementarity Problems. - Lingchen Kong; Levent Tunçel; Naihua Xiu (J. Optim. Theory Appl., 2012)

A semismooth Newton method for nonlinear symmetric cone programming. - Lingchen Kong; Qingmin Meng (Math. Methods Oper. Res., 2012)

Monotonicity of Löwner operators and its applications to symmetric cone complementarity problems. - Lingchen Kong; Levent Tunçel; Naihua Xiu (Math. Program., 2012)

Exact Low-Rank Matrix Recovery via Nonconvex Schatten P-Minimization. - Lingchen Kong; Naihua Xiu (Asia Pac. J. Oper. Res., 2013)

New RIC Bounds via l_q-minimization with 0<q<=1 in Compressed Sensing. - Shenglong Zhou 001; Lingchen Kong; Ziyan Luo; Naihua Xiu (CoRR, 2013)

Exact Recovery for Sparse Signal via Weighted l_1 Minimization. - Shenglong Zhou 001; Naihua Xiu; Yingnan Wang; Lingchen Kong (CoRR, 2013)

The Nonnegative Zero-Norm Minimization Under Generalized Z-Matrix Measurement. - Ziyan Luo; Linxia Qin; Lingchen Kong; Naihua Xiu (J. Optim. Theory Appl., 2014)

Improved Complexity Analysis of Full Nesterov-Todd Step Feasible Interior-Point Method for Symmetric Optimization. - Guo-Qiang Wang; Lingchen Kong; Jiyuan Tao; Goran Lesaja (J. Optim. Theory Appl., 2015)

The Uniqueness and Greedy Method for Quadratic Compressive Sensing. - Jun Fan; Lingchen Kong; Liqun Wang; Naihua Xiu (DSAA, 2016)

A note on an inequality involving Jordan product in Euclidean Jordan algebras. - Guo-Qiang Wang; Jiyuan Tao; Lingchen Kong (Optim. Lett., 2016)

Erratum to: Improved Complexity Analysis of Full Nesterov-Todd Step Feasible Interior-Point Method for Symmetric Optimization. - G. Q. Wang; Lingchen Kong; Jiyuan Tao; Goran Lesaja (J. Optim. Theory Appl., 2017)

Iterative reweighted methods for ( ell _1- ell _p ) minimization. - Xianchao Xiu; Lingchen Kong; Yan Li; Houduo Qi (Comput. Optim. Appl., 2018)

ADMM for High-Dimensional Sparse Penalized Quantile Regression. - Yuwen Gu; Jun Fan; Lingchen Kong; Shiqian Ma; Hui Zou (Technometrics, 2018)

Double fused Lasso penalized LAD for matrix regression. - Mei Li; Lingchen Kong (Appl. Math. Comput., 2019)

Alternating direction method of multipliers for nonconvex fused regression problems. - Xianchao Xiu; Wanquan Liu; Ling Li 006; Lingchen Kong (Comput. Stat. Data Anal., 2019)

Nonconvex mixed matrix minimization. - Meijuan Shang; Yanan Liu; Lingchen Kong; Xianchao Xiu; Ying Yang 002 (Math. Found. Comput., 2019)

The linearized alternating direction method of multipliers for sparse group LAD model. - Huifang Wang; Lingchen Kong; Jiyuan Tao (Optim. Lett., 2019)

Classical Multidimensional Scaling: A Subspace Perspective, Over-Denoising, and Outlier Detection. - Lingchen Kong; Chuanqi Qi; Hou-Duo Qi (IEEE Trans. Signal Process., 2019)

Safe feature screening rules for the regularized Huber regression. - Huangyue Chen; Lingchen Kong; Pan Shang; Shanshan Pan (Appl. Math. Comput., 2020)

tSSNALM: A fast two-stage semi-smooth Newton augmented Lagrangian method for sparse CCA. - Xianchao Xiu; Ying Yang 002; Lingchen Kong; Wanquan Liu (Appl. Math. Comput., 2020)

Adaptive trajectory tracking control strategy of intelligent vehicle. - Shuo Zhang; Xuan Zhao 001; Guohua Zhu; Peilong Shi; Yue Hao; Lingchen Kong (Int. J. Distributed Sens. Networks, 2020)

Optimal portfolio selections via ℓ 1, 2-norm regularization. - Hongxin Zhao; Lingchen Kong; Hou-Duo Qi (Comput. Optim. Appl., 2021)

Regularization Parameter Selection for the Low Rank Matrix Recovery. - Pan Shang; Lingchen Kong (J. Optim. Theory Appl., 2021)

ℓ2, 0-norm based selection and estimation for multivariate generalized linear models. - Yang Chen; Ziyan Luo; Lingchen Kong (J. Multivar. Anal., 2021)

Data-Driven Process Monitoring Using Structured Joint Sparse Canonical Correlation Analysis. - Xianchao Xiu; Ying Yang 002; Lingchen Kong; Wanquan Liu (IEEE Trans. Circuits Syst. II Express Briefs, 2021)

$ ell _{1}$ℓ1-Norm Quantile Regression Screening Rule via the Dual Circumscribed Sphere. - Pan Shang; Lingchen Kong (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Nonconvex clustering via ℓ0 fusion penalized regression. - Huangyue Chen; Lingchen Kong; Yan Li (Pattern Recognit., 2022)

A Safe Feature Screening Rule for Rank Lasso. - Pan Shang; Lingchen Kong; Dashuai Liu (IEEE Signal Process. Lett., 2022)

Fast Algorithms for LS and LAD-Collaborative Regression. - Jun Sun; Lingchen Kong; Mei Li (Asia Pac. J. Oper. Res., 2022)

Gradient projection Newton algorithm for sparse collaborative learning using synthetic and real datasets of applications. - Jun Sun; Lingchen Kong; Shenglong Zhou 001 (J. Comput. Appl. Math., 2023)

An Enhanced Regularized Clustering Method With Adaptive Spurious Connection Detection. - Huangyue Chen; Lingchen Kong; Wentao Qu; Xianchao Xiu (IEEE Signal Process. Lett., 2023)

A Greedy Newton-Type Method for Multiple Sparse Constraint Problem. - Jun Sun; Lingchen Kong; Biao Qu (J. Optim. Theory Appl., 2023)

A new formulation of sparse multiple kernel k$$ k $$-means clustering and its applications. - Wentao Qu; Xianchao Xiu; Jun Sun; Lingchen Kong (Stat. Anal. Data Min., 2023)

Algorithmic generalization ability of PALM for double sparse regularized regression. - Mei Li; Lingchen Kong; Bo Pan; Linglong Kong (Appl. Intell., 2023)

Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data. - Yafei Wang; Bo Pan; Mei Li; Jianya Lu; Lingchen Kong; Bei Jiang; Linglong Kong (ICML, 2024)

Low-rank tensor regression for selection of grouped variables. - Yang Chen; Ziyan Luo; Lingchen Kong (J. Multivar. Anal., 2024)

Estimation of sparse covariance matrix via non-convex regularization. - Xin Wang; Lingchen Kong; Liqun Wang (J. Multivar. Anal., 2024)

Robust multitask learning in high dimensions under memory constraint. - Canyi Chen; Bingzhen Chen; Lingchen Kong; Liping Zhu (Stat. Anal. Data Min., 2024)

An efficient Lagrange-Newton algorithm for long-only cardinality constrained portfolio selection on real data sets. - Yingxiao Wang; Lingchen Kong; Houduo Qi (J. Comput. Appl. Math., 2025)