TY - JOUR
T1 - The Nonsmooth Stochastic Steffensen Type Methods and Their Applications to the Tensor Complementarity Problem
AU - Fan, Mengxiao
AU - Li, Jicheng
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
PY - 2025/8
Y1 - 2025/8
N2 - In this paper, we promote the stochastic Steffensen type method to solve a system of semismooth equations, and improve this algorithm from the perspectives of different selection criteria, minibatching, and Barzilai-Borwein step size. Finally, we propose several different modulus equations for the tensor complementarity problem and apply this series of nonsmooth stochastic Steffensen type methods to solve the tensor complementarity problem and the linear complementarity problem.
AB - In this paper, we promote the stochastic Steffensen type method to solve a system of semismooth equations, and improve this algorithm from the perspectives of different selection criteria, minibatching, and Barzilai-Borwein step size. Finally, we propose several different modulus equations for the tensor complementarity problem and apply this series of nonsmooth stochastic Steffensen type methods to solve the tensor complementarity problem and the linear complementarity problem.
KW - Linear complementarity problem
KW - Modulus equations
KW - Stochastic Steffensen method
KW - System of semismooth equations
KW - Tensor complementarity problem
UR - https://www.scopus.com/pages/publications/105009399522
U2 - 10.1007/s10915-025-02967-1
DO - 10.1007/s10915-025-02967-1
M3 - 文章
AN - SCOPUS:105009399522
SN - 0885-7474
VL - 104
JO - Journal of Scientific Computing
JF - Journal of Scientific Computing
IS - 2
M1 - 57
ER -