• Previous Article
    Dynamic optimal decision making for manufacturers with limited attention based on sparse dynamic programming
  • JIMO Home
  • This Issue
  • Next Article
    The optimal pricing and ordering policy for temperature sensitive products considering the effects of temperature on demand
doi: 10.3934/jimo.2018049

A potential reduction method for tensor complementarity problems

1. 

College of Applied Sciences, Beijing University of Technology, Beijing 100124, China

2. 

School of Management Science, Qufu Normal University, Rizhao, Shandong 276800, China

3. 

College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China

* Corresponding author: Haibin Chen, chenhaibin508@163.com

Received  June 2017 Revised  December 2017 Published  April 2018

Fund Project: The authors' work are supported by the Natural Science Foundation of China (Grant No. 11601261, 11671228, 11771003), the Natural Science Foundation of Shandong Province (Grant No. ZR2016AQ12), and the China Postdoctoral Science Foundation (Grant No. 2017M622163)

As an extension of linear complementary problem, tensor complementary problem has been effectively applied in $ n $-person noncooperative game. And a multitude of researchers have focused on its properties and theories, while the valid algorithms for tensor complementary problem is still deficient. In this paper, stimulated by the potential reduction method for linear complementarity problem, we present a new algorithm for the tensor complementarity problem, which combines the idea of damped Newton method and the interior point method. Utilizing the new algorithm, we settle the tensor complementary problem with the underlying tensor being diagonalizable and positive definite. Furthermore, the global convergence of the iterative scheme is theoretically guaranteed and the given preliminary numerical experiments indicate the efficiency of the method.

Citation: Kaili Zhang, Haibin Chen, Pengfei Zhao. A potential reduction method for tensor complementarity problems. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2018049
References:
[1]

X. L. BaiZ. H. Huang and Y. Wang, Global uniqueness and solvability for tensor complementarity problems, J. Optim. Theory Appl., 170 (2016), 72-84. doi: 10.1007/s10957-016-0903-4.

[2]

L. Castello and H. Clercx, Geometrical statistics of the vorticity vector and the strain rate tensor in rotating turbulence, J. Turbul., 14 (2013), 19-36. doi: 10.1080/14685248.2013.866241.

[3]

M. L. CheL. Q. Qi and Y. M. Wei, Positive-definite tensors to nonlinear complementarity problems, J. Optim. Theory Appl., 168 (2016), 475-487. doi: 10.1007/s10957-015-0773-1.

[4]

H. B. Chen, Y. N. Chen, G. Y. Li and L. Q. Qi, A semi-definite program approach for computing the maximum eigenvalue of a class of structured tensors and its applications in hypergraphs and copositivity test, Numer. Lin. Alg. Appl., 25 (2018), e2125.

[5]

H. B. ChenZ. H. Huang and L. Q. Qi, Copositivity detection of tensors: Theory and algorithm, J. Optim. Theory Appl., 174 (2017), 746-761. doi: 10.1007/s10957-017-1131-2.

[6]

H. B. ChenZ. H. Huang and L. Q. Qi, Copositive tensor detection and its applications in physics and hypergraphs, Comput. Optim. Appl., 69 (2018), 133-158. doi: 10.1007/s10589-017-9938-1.

[7]

H. B. ChenL. Q. Qi and Y. S. Song, Column sufficient tensors and tensor complementarity problems, Front. Math. China, (2018), 255-276. doi: 10.1007/s11464-018-0681-4.

[8]

H. B. Chen and Y. J. Wang, On computing minimal H-eigenvalue of sign-structured tensors, Front. Math. China., 12 (2017), 1289-1302. doi: 10.1007/s11464-017-0645-0.

[9]

R. W. Cottle, J. S. Pang and R. E. Stone, The Linear Complementarity Problem, SIAM Series in Classics in Applied Mathematics, 2009.

[10]

W. Y. Ding, Z. Y. Luo and L. Q. Qi, $ P $-tensors, $ P_0 $-tensors, and tensor complementarity problem, preprint, arXiv: 1507.06731.

[11]

F. Facchinei and J. S. Pang, Finite-dimensional Variational Inequalities and Complementarity Problems, Springer Series in Operations Research and Financial Engineering, 2003.

[12]

G. Golub and C. Loan, Matrix Computations. Johns Hopkins series in the mathematical sciences, Johns Hopkins University Press, Baltimore, MD, 1989.

[13]

M. S. Gowda, Z. Y. Luo, L. Q. Qi and N. H. Xiu, $ Z $-tensors and complementarity problems, preprint, arXiv: 1510.07933.

[14]

Z. H. Huang and L. Q. Qi, Formulating an n-person noncooperative game as a tensor complementarity problem, Comput. Optim. Appl., 66 (2017), 557-576. doi: 10.1007/s10589-016-9872-7.

[15]

Z. H. Huang, Y. Y. Suo and J. Wang, On $ Q $-Tensors, preprint, arXiv: 1509.03088.

[16]

M. Kojima, N. Megiddo and T. Noma, A Unified Approach to Interior-point Algorithms for Linear Complementarity Problems, in: Lecture Notes in Computer Science, vol. 538, Springer Verlag, Berlin, Germany, 1991.

[17]

M. KojimaT. Noma and A. Yoshise, Global convergence in infeasible-interior-point algorithms, Math. Program., 65 (1994), 43-72. doi: 10.1007/BF01581689.

[18]

T. Kolda and B. Bader, Tensor decompositions and applications, SIAM Rev., 51 (2009), 455-500. doi: 10.1137/07070111X.

[19]

Z. Y. LuoL. Q. Qi and N. H. Xiu, The sparsest solutions to $ Z $-tensor complementarity problems, Optim. Lett., 11 (2017), 471-482. doi: 10.1007/s11590-016-1013-9.

[20]

F. M. MaY. J. Wang and H. Zhao, A potential reduction algorithm for generalized linear complementarity problem over a polyhedral cone, J. Ind. Manag. Optim., 6 (2010), 259-267.

[21]

H. Mansouri and M. Pirhaji, An adaptive infeasible interior-point algorithm for linear complementarity problems, J. Oper. Res. Soc., 1 (2013), 523-536. doi: 10.1007/s40305-013-0031-x.

[22]

M. Preiß and J. Stoer, Analysis of infeasible-interior-point paths arising with semidefinite linear complementarity problems, Math. Program., 99 (2004), 499-520. doi: 10.1007/s10107-003-0463-x.

[23]

L. Q. Qi, Eigenvalues of a real supersymmetric tensor, J. Symb. Comput., 40 (2005), 1302-1324. doi: 10.1016/j.jsc.2005.05.007.

[24]

L. Q. QiY. J. Wang and E. Wu, D-eigenvalues of diffusion kurtosis tensors, J. Comput. Appl. Math., 221 (2008), 150-157. doi: 10.1016/j.cam.2007.10.012.

[25]

L. Q. QiF. Wang and Y. J. Wang, Z-eigenvalue methods for a global polynomial optimization problem, Math. Program., 118 (2009), 301-316. doi: 10.1007/s10107-007-0193-6.

[26]

L. Q. QiG. H. Yu and E. Wu, Higher order positive semidefinite diffusion tensor imaging, SIAM J. Imaging Sci., 3 (2010), 416-433. doi: 10.1137/090755138.

[27]

D. Savostyanov, Tensor algorithms of blind separation of electromagnetic signals, Russ. J. Numer. Anal. M., 25 (2010), 375-393.

[28]

E. Simantiraki and D. Shanno, An infeasible-interior-point method for linear complementarity problems, SIAM J. Optim., 7 (1997), 620-640. doi: 10.1137/S1052623495282882.

[29]

Y. S. Song and L. Q. Qi, Properties of tensor complementarity problem and some classes of structured tensors, Ann. Appl. Math., 33 (2017), 308-323. doi: 10.1007/s10957-014-0616-5.

[30]

Y. S. Song and L. Q. Qi, Properties of some classes of structured tensors, J. Optim. Theory Appl., 165 (2015), 854-873. doi: 10.1007/s10957-014-0616-5.

[31]

Y. S. Song and L. Q. Qi, Strictly semi-positive tensors and the boundedness of tensor complementarity problems, Optim. Lett., 11 (2017), 1407-1426. doi: 10.1007/s11590-016-1104-7.

[32]

Y. S. Song and G. H. Yu, Properties of solution set of tensor complementarity problem, J. Optim. Theory Appl., 170 (2016), 85-96. doi: 10.1007/s10957-016-0907-0.

[33]

K. Tanabe, Centered Newton method for mathematical programming, System Modelling Opt., 113 (1988), 197-206.

[34]

M. Todd and Y. Ye, A centered projective algorithm for linear programming, Math. Oper. Res., 15 (1990), 508-529. doi: 10.1287/moor.15.3.508.

[35]

T. WangR. Monteiro and J. S. Pang, An interior point potential reduction method for constrained equations, Math. Program., 74 (1996), 159-195. doi: 10.1007/BF02592210.

[36]

Y. J. WangL. Caccetta and G. L. Zhou, Convergence analysis of a block improvement method for polynomial optimization over unit spheres, Numer. Lin. Alg. Appl., 22 (2015), 1059-1076. doi: 10.1002/nla.1996.

[37]

Y. J. WangL. Q. Qi and X. Z. Zhang, A practical method for computing the largest M-eigenvalue of a fourth-order partially symmetric tensor, Numer. Lin. Alg. Appl., 16 (2009), 589-601.

[38]

Y. J. WangG. Zhou and L. Caccetta, Nonsingular $ H $-tensor and its cariteria, J. Ind. Manag. Optim., 12 (2016), 1173-1186. doi: 10.3934/jimo.2016.12.1173.

[39]

Y. J. WangK. L. Zhang and H. C. Sun, Criteria for strong $ H $-tensors, Front. Math. China, 11 (2016), 577-592. doi: 10.1007/s11464-016-0525-z.

[40]

Y. WangZ. H. Huang and X. L. Bai, Exceptionally regular tensors and tensor complementarity problems, Optim. Methods Softw., 31 (2016), 815-828. doi: 10.1080/10556788.2016.1180386.

[41]

S. L. XieD. H. Li and H. R. Xu, An iterative method for finding the least solution of the tensor complementarity problem with $ Z $-Tensor, J. Optim. Theory Appl., 175 (2017), 119-136. doi: 10.1007/s10957-017-1157-5.

[42]

K. L. Zhang and Y. J. Wang, An $ H $-tensor based iterative scheme for identifying the positive definiteness of multivariate homogeneous forms, J. Comput. Appl. Math., 305 (2016), 1-10. doi: 10.1016/j.cam.2016.03.025.

[43]

G. ZouX. Chen and Z. J. Wang, Underdetermined joint blind source separation for two datasets based on tensor decomposition, IEEE Signal Proc. Lett., 23 (2016), 673-677. doi: 10.1109/LSP.2016.2546687.

show all references

References:
[1]

X. L. BaiZ. H. Huang and Y. Wang, Global uniqueness and solvability for tensor complementarity problems, J. Optim. Theory Appl., 170 (2016), 72-84. doi: 10.1007/s10957-016-0903-4.

[2]

L. Castello and H. Clercx, Geometrical statistics of the vorticity vector and the strain rate tensor in rotating turbulence, J. Turbul., 14 (2013), 19-36. doi: 10.1080/14685248.2013.866241.

[3]

M. L. CheL. Q. Qi and Y. M. Wei, Positive-definite tensors to nonlinear complementarity problems, J. Optim. Theory Appl., 168 (2016), 475-487. doi: 10.1007/s10957-015-0773-1.

[4]

H. B. Chen, Y. N. Chen, G. Y. Li and L. Q. Qi, A semi-definite program approach for computing the maximum eigenvalue of a class of structured tensors and its applications in hypergraphs and copositivity test, Numer. Lin. Alg. Appl., 25 (2018), e2125.

[5]

H. B. ChenZ. H. Huang and L. Q. Qi, Copositivity detection of tensors: Theory and algorithm, J. Optim. Theory Appl., 174 (2017), 746-761. doi: 10.1007/s10957-017-1131-2.

[6]

H. B. ChenZ. H. Huang and L. Q. Qi, Copositive tensor detection and its applications in physics and hypergraphs, Comput. Optim. Appl., 69 (2018), 133-158. doi: 10.1007/s10589-017-9938-1.

[7]

H. B. ChenL. Q. Qi and Y. S. Song, Column sufficient tensors and tensor complementarity problems, Front. Math. China, (2018), 255-276. doi: 10.1007/s11464-018-0681-4.

[8]

H. B. Chen and Y. J. Wang, On computing minimal H-eigenvalue of sign-structured tensors, Front. Math. China., 12 (2017), 1289-1302. doi: 10.1007/s11464-017-0645-0.

[9]

R. W. Cottle, J. S. Pang and R. E. Stone, The Linear Complementarity Problem, SIAM Series in Classics in Applied Mathematics, 2009.

[10]

W. Y. Ding, Z. Y. Luo and L. Q. Qi, $ P $-tensors, $ P_0 $-tensors, and tensor complementarity problem, preprint, arXiv: 1507.06731.

[11]

F. Facchinei and J. S. Pang, Finite-dimensional Variational Inequalities and Complementarity Problems, Springer Series in Operations Research and Financial Engineering, 2003.

[12]

G. Golub and C. Loan, Matrix Computations. Johns Hopkins series in the mathematical sciences, Johns Hopkins University Press, Baltimore, MD, 1989.

[13]

M. S. Gowda, Z. Y. Luo, L. Q. Qi and N. H. Xiu, $ Z $-tensors and complementarity problems, preprint, arXiv: 1510.07933.

[14]

Z. H. Huang and L. Q. Qi, Formulating an n-person noncooperative game as a tensor complementarity problem, Comput. Optim. Appl., 66 (2017), 557-576. doi: 10.1007/s10589-016-9872-7.

[15]

Z. H. Huang, Y. Y. Suo and J. Wang, On $ Q $-Tensors, preprint, arXiv: 1509.03088.

[16]

M. Kojima, N. Megiddo and T. Noma, A Unified Approach to Interior-point Algorithms for Linear Complementarity Problems, in: Lecture Notes in Computer Science, vol. 538, Springer Verlag, Berlin, Germany, 1991.

[17]

M. KojimaT. Noma and A. Yoshise, Global convergence in infeasible-interior-point algorithms, Math. Program., 65 (1994), 43-72. doi: 10.1007/BF01581689.

[18]

T. Kolda and B. Bader, Tensor decompositions and applications, SIAM Rev., 51 (2009), 455-500. doi: 10.1137/07070111X.

[19]

Z. Y. LuoL. Q. Qi and N. H. Xiu, The sparsest solutions to $ Z $-tensor complementarity problems, Optim. Lett., 11 (2017), 471-482. doi: 10.1007/s11590-016-1013-9.

[20]

F. M. MaY. J. Wang and H. Zhao, A potential reduction algorithm for generalized linear complementarity problem over a polyhedral cone, J. Ind. Manag. Optim., 6 (2010), 259-267.

[21]

H. Mansouri and M. Pirhaji, An adaptive infeasible interior-point algorithm for linear complementarity problems, J. Oper. Res. Soc., 1 (2013), 523-536. doi: 10.1007/s40305-013-0031-x.

[22]

M. Preiß and J. Stoer, Analysis of infeasible-interior-point paths arising with semidefinite linear complementarity problems, Math. Program., 99 (2004), 499-520. doi: 10.1007/s10107-003-0463-x.

[23]

L. Q. Qi, Eigenvalues of a real supersymmetric tensor, J. Symb. Comput., 40 (2005), 1302-1324. doi: 10.1016/j.jsc.2005.05.007.

[24]

L. Q. QiY. J. Wang and E. Wu, D-eigenvalues of diffusion kurtosis tensors, J. Comput. Appl. Math., 221 (2008), 150-157. doi: 10.1016/j.cam.2007.10.012.

[25]

L. Q. QiF. Wang and Y. J. Wang, Z-eigenvalue methods for a global polynomial optimization problem, Math. Program., 118 (2009), 301-316. doi: 10.1007/s10107-007-0193-6.

[26]

L. Q. QiG. H. Yu and E. Wu, Higher order positive semidefinite diffusion tensor imaging, SIAM J. Imaging Sci., 3 (2010), 416-433. doi: 10.1137/090755138.

[27]

D. Savostyanov, Tensor algorithms of blind separation of electromagnetic signals, Russ. J. Numer. Anal. M., 25 (2010), 375-393.

[28]

E. Simantiraki and D. Shanno, An infeasible-interior-point method for linear complementarity problems, SIAM J. Optim., 7 (1997), 620-640. doi: 10.1137/S1052623495282882.

[29]

Y. S. Song and L. Q. Qi, Properties of tensor complementarity problem and some classes of structured tensors, Ann. Appl. Math., 33 (2017), 308-323. doi: 10.1007/s10957-014-0616-5.

[30]

Y. S. Song and L. Q. Qi, Properties of some classes of structured tensors, J. Optim. Theory Appl., 165 (2015), 854-873. doi: 10.1007/s10957-014-0616-5.

[31]

Y. S. Song and L. Q. Qi, Strictly semi-positive tensors and the boundedness of tensor complementarity problems, Optim. Lett., 11 (2017), 1407-1426. doi: 10.1007/s11590-016-1104-7.

[32]

Y. S. Song and G. H. Yu, Properties of solution set of tensor complementarity problem, J. Optim. Theory Appl., 170 (2016), 85-96. doi: 10.1007/s10957-016-0907-0.

[33]

K. Tanabe, Centered Newton method for mathematical programming, System Modelling Opt., 113 (1988), 197-206.

[34]

M. Todd and Y. Ye, A centered projective algorithm for linear programming, Math. Oper. Res., 15 (1990), 508-529. doi: 10.1287/moor.15.3.508.

[35]

T. WangR. Monteiro and J. S. Pang, An interior point potential reduction method for constrained equations, Math. Program., 74 (1996), 159-195. doi: 10.1007/BF02592210.

[36]

Y. J. WangL. Caccetta and G. L. Zhou, Convergence analysis of a block improvement method for polynomial optimization over unit spheres, Numer. Lin. Alg. Appl., 22 (2015), 1059-1076. doi: 10.1002/nla.1996.

[37]

Y. J. WangL. Q. Qi and X. Z. Zhang, A practical method for computing the largest M-eigenvalue of a fourth-order partially symmetric tensor, Numer. Lin. Alg. Appl., 16 (2009), 589-601.

[38]

Y. J. WangG. Zhou and L. Caccetta, Nonsingular $ H $-tensor and its cariteria, J. Ind. Manag. Optim., 12 (2016), 1173-1186. doi: 10.3934/jimo.2016.12.1173.

[39]

Y. J. WangK. L. Zhang and H. C. Sun, Criteria for strong $ H $-tensors, Front. Math. China, 11 (2016), 577-592. doi: 10.1007/s11464-016-0525-z.

[40]

Y. WangZ. H. Huang and X. L. Bai, Exceptionally regular tensors and tensor complementarity problems, Optim. Methods Softw., 31 (2016), 815-828. doi: 10.1080/10556788.2016.1180386.

[41]

S. L. XieD. H. Li and H. R. Xu, An iterative method for finding the least solution of the tensor complementarity problem with $ Z $-Tensor, J. Optim. Theory Appl., 175 (2017), 119-136. doi: 10.1007/s10957-017-1157-5.

[42]

K. L. Zhang and Y. J. Wang, An $ H $-tensor based iterative scheme for identifying the positive definiteness of multivariate homogeneous forms, J. Comput. Appl. Math., 305 (2016), 1-10. doi: 10.1016/j.cam.2016.03.025.

[43]

G. ZouX. Chen and Z. J. Wang, Underdetermined joint blind source separation for two datasets based on tensor decomposition, IEEE Signal Proc. Lett., 23 (2016), 673-677. doi: 10.1109/LSP.2016.2546687.

Table 5.1.  Numerical Results for Example 1
$\mathit{z}^0$ $\varepsilon$IterTime(s)
$(0.1, 0.1, 0.1, 0.1)^\top$ $10^{-5}$160.128738
$(0.1, 0.2, 0.6, 0.5)^\top$ $10^{-5}$200.181242
$(0.3, 0.5, 0.1, 0.7)^\top$ $10^{-5}$230.168388
$(0.2, 0.6, 0.4, 0.3)^\top$ $10^{-5}$230.186210
$(0.7, 0.3, 0.2, 0.5)^\top$ $10^{-5}$250.182582
$(0.2, 0.4, 0.6, 0.5)^\top$ $10^{-8}$350.194419
$(0.7, 0.5, 0.8, 0.9)^\top$ $10^{-8}$380.189600
$(1, 2, 5, 3)^\top$ $10^{-8}$430.184916
$(12, 7, 14, 35)^\top$ $10^{-8}$500.185770
$(24, 37, 56, 45)^\top$ $10^{-8}$550.221645
$(67, 52, 89, 93)^\top$ $10^{-8}$580.186433
$\mathit{z}^0$ $\varepsilon$IterTime(s)
$(0.1, 0.1, 0.1, 0.1)^\top$ $10^{-5}$160.128738
$(0.1, 0.2, 0.6, 0.5)^\top$ $10^{-5}$200.181242
$(0.3, 0.5, 0.1, 0.7)^\top$ $10^{-5}$230.168388
$(0.2, 0.6, 0.4, 0.3)^\top$ $10^{-5}$230.186210
$(0.7, 0.3, 0.2, 0.5)^\top$ $10^{-5}$250.182582
$(0.2, 0.4, 0.6, 0.5)^\top$ $10^{-8}$350.194419
$(0.7, 0.5, 0.8, 0.9)^\top$ $10^{-8}$380.189600
$(1, 2, 5, 3)^\top$ $10^{-8}$430.184916
$(12, 7, 14, 35)^\top$ $10^{-8}$500.185770
$(24, 37, 56, 45)^\top$ $10^{-8}$550.221645
$(67, 52, 89, 93)^\top$ $10^{-8}$580.186433
Table 5.2.  Numerical Results for Example 2
$\mathit{z}^0$ $\varepsilon$ $\beta_0$IterTime(s)
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.7910.311439
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.6680.187435
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.5540.219218
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.4440.181922
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.3370.176951
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.2320.245196
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.1280.330788
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.7910.280699
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.6670.232597
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.5530.219453
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.4440.210625
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.3370.197738
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.2320.169909
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.1280.161405
$\mathit{z}^0$ $\varepsilon$ $\beta_0$IterTime(s)
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.7910.311439
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.6680.187435
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.5540.219218
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.4440.181922
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.3370.176951
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.2320.245196
$(0.8147, 0.9058, 0.1270, 0.9137)^\top$ $10^{-6}$0.1280.330788
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.7910.280699
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.6670.232597
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.5530.219453
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.4440.210625
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.3370.197738
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.2320.169909
$(0.9572, 0.4854, 0.8003, 0.1419)^\top$ $10^{-10}$0.1280.161405
Table 5.3.  Numerical Results for Example 3
$\mathit{x}^*$IterTime(s)
$(0.0120, 0.0045, 0.0168, 0.0091, 0.0070, 0.0062)^\top$2327.810409
$(0.0166, 0.0052, 0.0137, 0.0137, 0.0069, 0.0103)^\top$2330.307993
$(0.0005, 0.0007, 0.0004, 0.0004, 0.0023, 0.0015)^\top$3431.502331
$(0.0012, 0.0007, 0.0017, 0.0004, 0.0015, 0.0004)^\top$3525.909628
$(0.0016, 0.0026, 0.0038, 0.0017, 0.0038, 0.0045)^\top$2936.274344
$ 1.0e-003\times(0.4151, 0.1557, 0.3255, 0.0922, 0.0142, 0.3467)^\top$4432.017439
$ 1.0e-003\times(0.0399, 0.2636, 0.3479, 0.2752, 0.4337, 0.4146)^\top$4229.446150
$\mathit{x}^*$IterTime(s)
$(0.0120, 0.0045, 0.0168, 0.0091, 0.0070, 0.0062)^\top$2327.810409
$(0.0166, 0.0052, 0.0137, 0.0137, 0.0069, 0.0103)^\top$2330.307993
$(0.0005, 0.0007, 0.0004, 0.0004, 0.0023, 0.0015)^\top$3431.502331
$(0.0012, 0.0007, 0.0017, 0.0004, 0.0015, 0.0004)^\top$3525.909628
$(0.0016, 0.0026, 0.0038, 0.0017, 0.0038, 0.0045)^\top$2936.274344
$ 1.0e-003\times(0.4151, 0.1557, 0.3255, 0.0922, 0.0142, 0.3467)^\top$4432.017439
$ 1.0e-003\times(0.0399, 0.2636, 0.3479, 0.2752, 0.4337, 0.4146)^\top$4229.446150
Table 5.4.  Numerical Results for Example 4
$m$ $n$IterTime(s)
410450.195758
420461.025905
4404914.254993
4505034.858052
4605090.753663
48051465.798026
4100512702.279664
610101332.915881
6201233420.345758
$m$ $n$IterTime(s)
410450.195758
420461.025905
4404914.254993
4505034.858052
4605090.753663
48051465.798026
4100512702.279664
610101332.915881
6201233420.345758
[1]

Fengming Ma, Yiju Wang, Hongge Zhao. A potential reduction method for the generalized linear complementarity problem over a polyhedral cone. Journal of Industrial & Management Optimization, 2010, 6 (1) : 259-267. doi: 10.3934/jimo.2010.6.259

[2]

Pilar Bayer, Dionís Remón. A reduction point algorithm for cocompact Fuchsian groups and applications. Advances in Mathematics of Communications, 2014, 8 (2) : 223-239. doi: 10.3934/amc.2014.8.223

[3]

Hirofumi Notsu, Masato Kimura. Symmetry and positive definiteness of the tensor-valued spring constant derived from P1-FEM for the equations of linear elasticity. Networks & Heterogeneous Media, 2014, 9 (4) : 617-634. doi: 10.3934/nhm.2014.9.617

[4]

Zheng-Hai Huang, Shang-Wen Xu. Convergence properties of a non-interior-point smoothing algorithm for the P*NCP. Journal of Industrial & Management Optimization, 2007, 3 (3) : 569-584. doi: 10.3934/jimo.2007.3.569

[5]

Behrouz Kheirfam, Morteza Moslemi. On the extension of an arc-search interior-point algorithm for semidefinite optimization. Numerical Algebra, Control & Optimization, 2018, 8 (2) : 261-275. doi: 10.3934/naco.2018015

[6]

Shenglong Hu, Zheng-Hai Huang, Hong-Yan Ni, Liqun Qi. Positive definiteness of Diffusion Kurtosis Imaging. Inverse Problems & Imaging, 2012, 6 (1) : 57-75. doi: 10.3934/ipi.2012.6.57

[7]

Haibin Chen, Liqun Qi. Positive definiteness and semi-definiteness of even order symmetric Cauchy tensors. Journal of Industrial & Management Optimization, 2015, 11 (4) : 1263-1274. doi: 10.3934/jimo.2015.11.1263

[8]

Behrouz Kheirfam, Guoqiang Wang. An infeasible full NT-step interior point method for circular optimization. Numerical Algebra, Control & Optimization, 2017, 7 (2) : 171-184. doi: 10.3934/naco.2017011

[9]

Fang Zeng, Pablo Suarez, Jiguang Sun. A decomposition method for an interior inverse scattering problem. Inverse Problems & Imaging, 2013, 7 (1) : 291-303. doi: 10.3934/ipi.2013.7.291

[10]

Behrouz Kheirfam. A full Nesterov-Todd step infeasible interior-point algorithm for symmetric optimization based on a specific kernel function. Numerical Algebra, Control & Optimization, 2013, 3 (4) : 601-614. doi: 10.3934/naco.2013.3.601

[11]

Yanqin Bai, Lipu Zhang. A full-Newton step interior-point algorithm for symmetric cone convex quadratic optimization. Journal of Industrial & Management Optimization, 2011, 7 (4) : 891-906. doi: 10.3934/jimo.2011.7.891

[12]

Yinghong Xu, Lipu Zhang, Jing Zhang. A full-modified-Newton step infeasible interior-point algorithm for linear optimization. Journal of Industrial & Management Optimization, 2016, 12 (1) : 103-116. doi: 10.3934/jimo.2016.12.103

[13]

Zhongyi Huang. Tailored finite point method for the interface problem. Networks & Heterogeneous Media, 2009, 4 (1) : 91-106. doi: 10.3934/nhm.2009.4.91

[14]

Boshi Tian, Xiaoqi Yang, Kaiwen Meng. An interior-point $l_{\frac{1}{2}}$-penalty method for inequality constrained nonlinear optimization. Journal of Industrial & Management Optimization, 2016, 12 (3) : 949-973. doi: 10.3934/jimo.2016.12.949

[15]

Yanqin Bai, Pengfei Ma, Jing Zhang. A polynomial-time interior-point method for circular cone programming based on kernel functions. Journal of Industrial & Management Optimization, 2016, 12 (2) : 739-756. doi: 10.3934/jimo.2016.12.739

[16]

Nikolaos S. Papageorgiou, Vicenţiu D. Rădulescu, Dušan D. Repovš. Positive solutions for perturbations of the Robin eigenvalue problem plus an indefinite potential. Discrete & Continuous Dynamical Systems - A, 2017, 37 (5) : 2589-2618. doi: 10.3934/dcds.2017111

[17]

John R. Graef, Bo Yang. Multiple positive solutions to a three point third order boundary value problem. Conference Publications, 2005, 2005 (Special) : 337-344. doi: 10.3934/proc.2005.2005.337

[18]

John R. Graef, Johnny Henderson, Bo Yang. Positive solutions to a fourth order three point boundary value problem. Conference Publications, 2009, 2009 (Special) : 269-275. doi: 10.3934/proc.2009.2009.269

[19]

Manfred Einsiedler, Elon Lindenstrauss. On measures invariant under diagonalizable actions: the Rank-One case and the general Low-Entropy method. Journal of Modern Dynamics, 2008, 2 (1) : 83-128. doi: 10.3934/jmd.2008.2.83

[20]

David Colton, Lassi Päivärinta, John Sylvester. The interior transmission problem. Inverse Problems & Imaging, 2007, 1 (1) : 13-28. doi: 10.3934/ipi.2007.1.13

2017 Impact Factor: 0.994

Article outline

Figures and Tables

[Back to Top]