A New Smoothing Conjugate Gradient Method for Solving Nonlinear Nonsmooth Complementarity Problems. - Ajie Chu; Shouqiang Du; Yixiao Su (Algorithms, 2015)

Nonsmooth Levenberg-Marquardt Type Method for Solving a Class of Stochastic Linear Complementarity Problems with Finitely Many Elements. - Zhimin Liu; Shouqiang Du; Ruiying Wang (Algorithms, 2016)

A mixed integer programming approach to the tensor complementarity problem. - Shouqiang Du; Liping Zhang (J. Glob. Optim., 2019)

Stochastic structured tensors to stochastic complementarity problems. - Shouqiang Du; Maolin Che; Yimin Wei 001 (Comput. Optim. Appl., 2020)

A sufficient descent nonlinear conjugate gradient method for solving M-tensor equations. - Jiankun Liu; Shouqiang Du; Yuanyuan Chen (J. Comput. Appl. Math., 2020)

Acceptable Solutions and Backward Errors for Tensor Complementarity Problems. - Shouqiang Du; Weiyang Ding; Yimin Wei 001 (J. Optim. Theory Appl., 2021)

On Exchange Methods for Nonlinear Semi-Infinite Programs. - Liping Zhang; Shouqiang Du (Asia Pac. J. Oper. Res., 2022)

Stochastic Tensor Complementarity Problem with Discrete Distribution. - Shouqiang Du; Liyuan Cui; Yuanyuan Chen; Yimin Wei 001 (J. Optim. Theory Appl., 2022)

Stochastic absolute value equations. - Shouqiang Du; Jingjing Sun; Shengqun Niu; Liping Zhang (J. Appl. Math. Comput., 2023)

A smoothing projected HS method for solving stochastic tensor complementarity problem. - Mengdie Lu; Shouqiang Du (J. Appl. Math. Comput., 2023)

An effective smoothing Newton projection algorithm for finding sparse solutions to NP-hard tensor complementarity problems. - Jingjing Sun; Shouqiang Du (J. Comput. Appl. Math., 2024)