TY - JOUR
T1 - A General Preconditioner for Tensor Complementarity Problems
AU - Dai, Ping Fan
AU - Bai, Jianchao
AU - Li, Jicheng
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2024/1
Y1 - 2024/1
N2 - Preconditioning techniques have been focused on multi-linear systems or tensor equations. However, to our knowledge, there is no relevant research on tensor complementarity problems. In this paper, we present a general preconditioner P for solving the tensor complementarity problem (TCP) involved with an L -tensor A and a vector q, and we prove that the TCP (A, q) is equivalent to the TCP (PA, Pq) under the assumption that PA is an L -tensor. Based on this equivalence, a preconditioned fixed point iteration method is proposed for solving the tensor complementarity problem and its convergence analysis is given. For actual computations, we provide a concrete choice for the preconditioner P associated with a parameter satisfying above-mentioned hypothesis. In addition, it is proved theoretically that the convergence rate of the fixed point method with the chosen preconditioner P is at least as fast as that of the corresponding method without preprocessing. Meanwhile, we also obtain the monotony of the parameter on the performance of the preconditioned iterative method. Lastly, numerical examples are used to demonstrate the theoretical results.
AB - Preconditioning techniques have been focused on multi-linear systems or tensor equations. However, to our knowledge, there is no relevant research on tensor complementarity problems. In this paper, we present a general preconditioner P for solving the tensor complementarity problem (TCP) involved with an L -tensor A and a vector q, and we prove that the TCP (A, q) is equivalent to the TCP (PA, Pq) under the assumption that PA is an L -tensor. Based on this equivalence, a preconditioned fixed point iteration method is proposed for solving the tensor complementarity problem and its convergence analysis is given. For actual computations, we provide a concrete choice for the preconditioner P associated with a parameter satisfying above-mentioned hypothesis. In addition, it is proved theoretically that the convergence rate of the fixed point method with the chosen preconditioner P is at least as fast as that of the corresponding method without preprocessing. Meanwhile, we also obtain the monotony of the parameter on the performance of the preconditioned iterative method. Lastly, numerical examples are used to demonstrate the theoretical results.
KW - Comparison theorem
KW - Fixed point iteration method
KW - Preconditioner
KW - Tensor complementarity problems
UR - https://www.scopus.com/pages/publications/85177450335
U2 - 10.1007/s10915-023-02391-3
DO - 10.1007/s10915-023-02391-3
M3 - 文章
AN - SCOPUS:85177450335
SN - 0885-7474
VL - 98
JO - Journal of Scientific Computing
JF - Journal of Scientific Computing
IS - 1
M1 - 3
ER -