Estimations on upper and lower bounds of solutions to a class of tensor complementarity problems

Yang XU, Weizhe GU, Zheng-Hai HUANG

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PDF(260 KB)
Front. Math. China ›› 2019, Vol. 14 ›› Issue (3) : 661-671. DOI: 10.1007/s11464-019-0770-z
RESEARCH ARTICLE
RESEARCH ARTICLE

Estimations on upper and lower bounds of solutions to a class of tensor complementarity problems

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Abstract

We introduce a class of structured tensors, called generalized row strictly diagonally dominant tensors, and discuss some relationships between it and several classes of structured tensors, including nonnegative tensors, B-tensors, and strictly copositive tensors. In particular, we give estimations on upper and lower bounds of solutions to the tensor complementarity problem (TCP) when the involved tensor is a generalized row strictly diagonally dominant tensor with all positive diagonal entries. The main advantage of the results obtained in this paper is that both bounds we obtained depend only on the tensor and constant vector involved in the TCP; and hence, they are very easy to calculate.

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School of Mathematics / Tianjin University / Tianjin 300350 / China

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Yang XU, Weizhe GU, Zheng-Hai HUANG. Estimations on upper and lower bounds of solutions to a class of tensor complementarity problems. Front. Math. China, 2019, 14(3): 661‒671 https://doi.org/10.1007/s11464-019-0770-z

References

[1]
Bai X L, Huang Z H, Li X. Stability of solutions and continuity of solution maps of tensor complementarity problems. Asia-Pac J Oper Res, 2019, 36(2): 1940002 (19pp)
CrossRef Google scholar
[2]
Bai X L, Huang Z H, Wang Y. Global uniqueness and solvability for tensor complementarity problems. J Optim Theory Appl, 2016, 170(1): 72–84
CrossRef Google scholar
[3]
Balaji R, Palpandi K. Positive definite and Gram tensor complementarity problems. Optim Lett, 2018, 12(3): 639–648
CrossRef Google scholar
[4]
Che M L, Qi L, Wei Y M. Positive-definite tensors to nonlinear complementarity problems. J Optim Theory Appl, 2016, 168(2): 475–487
CrossRef Google scholar
[5]
Chen H, Qi L, Song Y. Column sufficient tensors and tensor complementarity problems. Front Math China, 2018, 13(2): 255–276
CrossRef Google scholar
[6]
Ding W, Luo Z, Qi L. P-tensors, P0-tensors, and their applications. Linear Algebra Appl, 2018, 555: 336–354
CrossRef Google scholar
[7]
Du S, Zhang L. A mixed integer programming approach to the tensor complementarity problem. J Global Optim, 2019, 73(4): 789–800
CrossRef Google scholar
[8]
Gowda MS. Polynomial complementarity problems. Pac J Optim, 2017, 13(2): 227–241
[9]
Gowda M S, Luo Z, Qi L, Xiu N H. Z-tensors and complementarity problems. arXiv: 1510.07933v2
[10]
Guo Q, Zheng M M, Huang Z H. Properties of S-tensors. Linear Multilinear Algebra, 2019, 67(4): 685–696
CrossRef Google scholar
[11]
Han L X. A continuation method for tensor complementarity problems. J Optim Theory Appl, 2019, 180(3): 949–963
CrossRef Google scholar
[12]
Hieu Vu T. On the R0-tensors and the solution map of tensor complementarity problems. J Optim Theory Appl, 2019, 181(1): 163–183
CrossRef Google scholar
[13]
Hu S, Huang Z H, Wang J. Error bounds for the solution set of quadratic complementarity problems. J Optim Theory Appl, 2018, 179(3): 983–1000
CrossRef Google scholar
[14]
Huang Z H, Qi L. Formulating an n-person noncooperative game as a tensor complementarity problem. Comput Optim Appl, 2017, 66(3): 557–576
CrossRef Google scholar
[15]
Huang Z H, Suo Y Y, Wang J. On Q-tensors. arXiv: 1509.03088v1
[16]
Ling L, He H, Ling C. On error bounds of polynomial complementarity problems with structured tensors. Optimization, 2018, 67(2): 341–358
CrossRef Google scholar
[17]
Liu D, Li W, Vong S W. Tensor complementarity problems: the GUS-property and an algorithm. Linear Multilinear Algebra, 2018, 66(9): 1726–1749
CrossRef Google scholar
[18]
Luo Z, Qi L, Xiu N. The sparsest solutions to Z-tensor complementarity problems. Optim Lett, 2017, 11(3): 471–482
CrossRef Google scholar
[19]
Qi L. Symmetric nonnegative tensors and copositive tensors. Linear Algebra Appl, 2013, 439(1): 228–238
CrossRef Google scholar
[20]
Song Y, Mei W. Structural properties of tensors and complementarity problems. J Optim Theory Appl, 2018, 176(2): 289–305
CrossRef Google scholar
[21]
Song Y, Qi L. Properties of some classes of structured tensors. J Optim Theory Appl, 2015, 165(3): 854–873
CrossRef Google scholar
[22]
Song Y, Qi L. Tensor complementarity problem and semi-positive tensors. J Optim Theory Appl, 2016, 169(3): 1069–1078
CrossRef Google scholar
[23]
Song Y, Qi L. Strictly semi-positive tensors and the boundedness of tensor complementarity problems. Optim Lett, 2017, 11(7): 1407–1426
CrossRef Google scholar
[24]
Song Y, Qi L. Properties of tensor complementarity problem and some classes of structured tensors. Ann Appl Math, 2017, 33(3): 308–323
[25]
Song Y, Yu G. Properties of solution set of tensor complementarity problem. J Optim Theory Appl, 2016, 170(1): 85–96
CrossRef Google scholar
[26]
Tawhid M A, Rahmati, S. Complementarity problems over a hypermatrix (tensor) set. Optim Lett, 2018, 12(6): 1443–1454
CrossRef Google scholar
[27]
Wang J, Hu S, Huang Z H. Solution sets of quadratic complementarity problems. J Optim Theory Appl, 2018, 176(1): 120–136
CrossRef Google scholar
[28]
Wang X, Chen H, Wang Y. Solution structures of tensor complementarity problem. Front Math China, 2018, 13(4): 935–945
CrossRef Google scholar
[29]
Wang Y, Huang Z H, Bai X L. Exceptionally regular tensors and tensor complementarity problems. Optim Methods Softw, 2016, 31(4): 815–828
CrossRef Google scholar
[30]
Wang Y, Huang Z H, Qi L. Global uniqueness and solvability of tensor variational inequalities. J Optim Theory Appl, 2018, 177(1): 137–152
CrossRef Google scholar
[31]
Xie S L, Li D H, Xu H R. An iterative method for finding the least solution to the tensor complementarity problem. J Optim Theory Appl, 2017, 175(1): 119–136
CrossRef Google scholar
[32]
Zhang K, Chen H, Zhao P. A potential reduction method for tensor complementarity problems. J Ind Manag Optim, 2019, 15(2): 429–443
CrossRef Google scholar
[33]
Zhang L, Qi L, Zhou G. M-tensors and some applications. SIAM J Matrix Anal Appl, 2014, 35(2): 437–452
CrossRef Google scholar
[34]
Zhao X, Fan J. A semidefinite method for tensor complementarity problems. Optim Methods Softw, 2018,
CrossRef Google scholar
[35]
Zheng M M, Zhang Y, Huang Z H. Global error bounds for the tensor complementarity problem with a P-tensor. J Ind Manag Optim, 2019, 15(2): 933–946
CrossRef Google scholar

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2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
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