Shouqiang Du

14 papers Journal 14
YearRankTypeTitle / Venue / Authors
2025 J jnl
Extended Vertical Stochastic $\varvec{R}_{0}$-Tensor Complementarity Problem.
J. Optim. Theory Appl.
Shouqiang Du, Duan Song, Guangxuan Lin, Yimin Wei
2024 J jnl
An effective smoothing Newton projection algorithm for finding sparse solutions to NP-hard tensor complementarity problems.
J. Comput. Appl. Math.
Jingjing Sun, Shouqiang Du
2023 J jnl
A smoothing projected HS method for solving stochastic tensor complementarity problem.
J. Appl. Math. Comput.
Mengdie Lu, Shouqiang Du
2023 J jnl
Stochastic absolute value equations.
J. Appl. Math. Comput.
Shouqiang Du, Jingjing Sun, Shengqun Niu, Liping Zhang
2022 J jnl
On Exchange Methods for Nonlinear Semi-Infinite Programs.
Asia Pac. J. Oper. Res.
Liping Zhang, Shouqiang Du
2022 J jnl
Stochastic Tensor Complementarity Problem with Discrete Distribution.
J. Optim. Theory Appl.
Shouqiang Du, Liyuan Cui, Yuanyuan Chen, Yimin Wei
2021 J jnl
Acceptable Solutions and Backward Errors for Tensor Complementarity Problems.
J. Optim. Theory Appl.
Shouqiang Du, Weiyang Ding, Yimin Wei
2020 J jnl
A sufficient descent nonlinear conjugate gradient method for solving M-tensor equations.
J. Comput. Appl. Math.
Jiankun Liu, Shouqiang Du, Yuanyuan Chen
2020 J jnl
Stochastic structured tensors to stochastic complementarity problems.
Comput. Optim. Appl.
Shouqiang Du, Maolin Che, Yimin Wei
2019 J jnl
A mixed integer programming approach to the tensor complementarity problem.
J. Glob. Optim.
Shouqiang Du, Liping Zhang
2016 J jnl
Nonsmooth Levenberg-Marquardt Type Method for Solving a Class of Stochastic Linear Complementarity Problems with Finitely Many Elements.
Algorithms
Zhimin Liu, Shouqiang Du, Ruiying Wang
2015 J jnl
A New Smoothing Conjugate Gradient Method for Solving Nonlinear Nonsmooth Complementarity Problems.
Algorithms
Ajie Chu, Shouqiang Du, Yixiao Su
2008 J jnl
Global convergence of a modified spectral FR conjugate gradient method.
Appl. Math. Comput.
Shouqiang Du, Yuanyuan Chen
2006 J jnl
Further insight into the Shamanskii modification of Newton method.
Appl. Math. Comput.
Chang-yu Wang, Yuanyuan Chen, Shouqiang Du