Levenberg-Marquardt method for absolute value equation associated with second-order cone

Xin-He Miao; Kai Yao; Ching-Yu Yang; Jein-Shan Chen

doi:10.3934/naco.2021050

Article Contents

2022, Volume 12, Issue 1: 47-61. Doi: 10.3934/naco.2021050

This issue Previous Article A crowdsourced dynamic repositioning strategy for public bike sharing systems Next Article Weak convergence theorems for symmetric generalized hybrid mappings and equilibrium problems

Levenberg-Marquardt method for absolute value equation associated with second-order cone

1.
School of Mathematics, Tianjin University, Tianjin 300072, P.R. China
2.
Department of Mathematics, National Taiwan Normal University, Taipei 11677, Taiwan

^* Corresponding author: Jein-Shan Chen
^* Corresponding author: Jein-Shan Chen

Received: February 2020

Revised: January 2021

Early access: November 2021

Published: March 2022

The first author is supported by by National Natural Science Foundation of China (No. 11471241) and Natural Science Foundation of Inner Mongolia (No. 2019LH01001). The last author is supported by Ministry of Science and Technology, Taiwan

Abstract / Introduction Full Text(HTML) Figure(8) Related Papers Cited by

Abstract

In this paper, we suggest the Levenberg-Marquardt method with Armijo line search for solving absolute value equations associated with the second-order cone (SOCAVE for short), which is a generalization of the standard absolute value equation frequently discussed in the literature during the past decade. We analyze the convergence of the proposed algorithm. For numerical reports, we not only show the efficiency of the proposed method, but also present numerical comparison with smoothing Newton method. It indicates that the proposed algorithm could also be a good choice for solving the SOCAVE.

Keywords:

Mathematics Subject Classification: Primary: 26B05, 65K10; Secondary: 26B35, 90C33.

Citation:

FullText(HTML)

Figure 1. Performance profile of computing time of Problem 4.1 with different $ p $

Download: Full-size image PowerPoint slide

Figure 2. Performance profile of computing time of Problem 4.1 with LM and SN methods

Download: Full-size image PowerPoint slide

Figure 3. Performance profile of computing time of Problem 4.2 with different $ p $

Download: Full-size image PowerPoint slide

Figure 4. Performance profile of computing time of Problem 4.2 with LM and SN methods

Download: Full-size image PowerPoint slide

Figure 5. Performance profile of computing time of Problem 4.3 with different $ p $

Download: Full-size image PowerPoint slide

Figure 6. Performance profile of computing time of Problem 4.3 with LM and SN methods

Download: Full-size image PowerPoint slide

Figure 7. Performance profile of computing time of Problem 4.4 with different $ p $

Download: Full-size image PowerPoint slide

Figure 8. Performance profile of computing time of Problem 4.4 with LM and SN methods

Download: Full-size image PowerPoint slide

Related Papers

Cited by

References

[1]	D. P. Bertsekas, Nonlinear Programming, Athena Scientific, Massachusetts, 1995.
[2]	L. Caccetta, B. Qu and G.-L. Zhou, A globally and quadratically convergent method for absolute value equations, Computational Optimization and Applications, 48 (2011), 45-58. doi: 10.1007/s10589-009-9242-9.
[3]	J. S. Chen, The convex and monotone functions associated with second-order cone, Optimization, 55 (2006), 363-385. doi: 10.1080/02331930600819514.
[4]	J. S. Chen, SOC Functions and Their Applications, Springer Optimization and Its Applications 143, Springer, Singapore, 2019. doi: 10.1007/978-981-13-4077-2.
[5]	J. S. Chen, X. Chen and P. Tseng, Analysis of nonsmooth vector-valued functions associated with second-order cones, Mathematical Programming, 101 (2004), 95-117. doi: 10.1007/s10107-004-0538-3.
[6]	J. S. Chen and S. H. Pan, A survey on SOC complementarity functions and solution methods for SOCPs and SOCCPs, Pacific Journal of Optimization, 8 (2012), 33-74.
[7]	J. S. Chen and P. Tseng, An unconstrained smooth minimization reformulation of second-order cone complementarity problem, Mathematical Programming, 104 (2005), 293-327. doi: 10.1007/s10107-005-0617-0.
[8]	E. D. Dolan and J. J. More, Benchmarking optimization software with performance profiles, Mathematical Programming, 91 (2002), 201-213. doi: 10.1007/s101070100263.
[9]	U. Faraut and A. Koranyi, Anlysis on Symmetric Cones, Oxford Mathematical Monographs, Oxford University Press, New York, 1994.
[10]	J.-Y. Fan and Y.-X. Yuan, On the quadratic convergence of the Levenberg-Marquardt method without nonsingularity assumption, Computing, 74 (2005), 23-39. doi: 10.1007/s00607-004-0083-1.
[11]	S.-L. Hu and Z.-H. Huang, A note on absolute value equations, Optimization Letters, 4 (2010), 417-424. doi: 10.1007/s11590-009-0169-y.
[12]	S.-L. Hu, Z.-H. Huang and Q. Zhang, A generalized Newton method for absolute value equations associated with second order cones, Journal of Computational and Applied Mathematics, 235 (2011), 1490-1501. doi: 10.1016/j.cam.2010.08.036.
[13]	J. Iqbal, A. Iqbal and M. Arif, Levenberg-Marquardt method for solving systems of absolute value equations, Journal of Computational and Applied Mathematics, 282 (2015), 134-138. doi: 10.1016/j.cam.2014.11.062.
[14]	X.-Q. Jiang, A smoothing newton method for solving absolute value equations, Advanced Materials Research, 765-767 (2013), 703-708.
[15]	X.-Q. Jiang and Y. Zhang, A smoothing-type algorithm for absolute value equations, Journal of Industrial and Management Optimization, 9 (2013), 789-798. doi: 10.3934/jimo.2013.9.789.
[16]	S. Ketabchi and H. Moosaei, Minimum norm solution to the absolute value equation in the convex case, Journal of Optimization Theory and Applications, 154 (2012), 1080-1087. doi: 10.1007/s10957-012-0044-3.
[17]	K. Levenberg, A method for the solution of certain nonlinear problems in least squares, Quarterly of Applied Mathematics, 2 (1944), 164-168. doi: 10.1090/qam/10666.
[18]	O. L. Mangasarian, Absolute value programming, Computational Optimization and Applications, 36 (2007), 43-53. doi: 10.1007/s10589-006-0395-5.
[19]	O. L. Mangasarian, Absolute value equation solution via concave minimization, Optimization Letters, 1 (2007), 3-5. doi: 10.1007/s11590-006-0005-6.
[20]	O. L. Mangasarian, A generalized Newton method for absolute value equations, Optimization Letters, 3 (2009), 101-108. doi: 10.1007/s11590-008-0094-5.
[21]	O. L. Mangasarian, Absolute value equation solution via dual complementarity, Optimization Letters, 7 (2013), 625-630. doi: 10.1007/s11590-012-0469-5.
[22]	O. L. Mangasarian, Linear complementarity as absolute value equation solution, Optimization Letters, 8 (2014), 1529-1534. doi: 10.1007/s11590-013-0656-z.
[23]	O. L. Mangasarian, Absolute value equation solution via linear programming, Journal of Optimization Theory and Applications, 161 (2014), 870-876. doi: 10.1007/s10957-013-0461-y.
[24]	O. L. Mangasarian and R. R. Meyer, Absolute value equation, Linear Algebra and Its Applications, 419 (2006), 359-367. doi: 10.1016/j.laa.2006.05.004.
[25]	D. W. Marquardt, An algorithm for least squares estimation of nonlinear inequalities, Journal of the Society for Industrial and Applied Mathematics, 11 (1963), 431-441.
[26]	X.-H. Miao, W.-M. Hsu, C. T. Nguyen and J.-S. Chen, The solvabilities of three optimization problems associated with second-order cone, Journal of Nonlinear and Convex Analysis, 22 (2021), 937-967.
[27]	X.-H. Miao, J.-T. Yang, B. Saheya and J.-S. Chen, A smoothing Newton method for absolute value equation associated with second-order cone, Applied Numerical Mathematics, 120 (2017), 82-96. doi: 10.1016/j.apnum.2017.04.012.
[28]	C. T. Nguyen, B. Saheya, Y.-L. Chang and J.-S. Chen, Unified smoothing functions for absolute value equation associated with second-order cone, Applied Numerical Mathematics, 135 (2019), 206-227. doi: 10.1016/j.apnum.2018.08.019.
[29]	O. A. Prokopyev, On equivalent reformulations for absolute value equations, Computational Optimization and Applications, 44 (2009), 363-372. doi: 10.1007/s10589-007-9158-1.
[30]	J. Rohn, A theorem of the alternative for the equation $Ax+B\|x\| = b$, Linear and Multilinear Algebra, 52 (2004), 421-426. doi: 10.1080/0308108042000220686.
[31]	J. Rohn, An algorithm for solving the absolute value equation, Eletronic Journal of Linear Algebra, 18 (2009), 589-599. doi: 10.13001/1081-3810.1332.
[32]	S. Yamanaka and M. Fukushima, A brancd and bound method for the absolute value programs, Optimization, 63 (2014), 305-319. doi: 10.1080/02331934.2011.644289.
[33]	N. Yamashita and M. Fukushima, On the rate of convergence of the Levenberg-Marquardt method, in Topics in Nonlinear Analysis (eds. by G. Alefeld and X. Chen), Springer-Verlag, (2001), 239–249. doi: 10.1007/978-3-7091-6217-0_18.
[34]	X. Zhu and G.-H. Lin, Improved convergence results for a modified Levenberg-Marquardt method for nonlinear equations and applications in MPCC, Optimization Methods and Software, 31 (2016), 791-804. doi: 10.1080/10556788.2016.1171863.
[35]	C. Zhang and Q.-J. Wei, Global and finite convergence of a generalized Newton method for absolute value equations, Journal of Optimization Theory and Applications, 143 (2009), 391-403. doi: 10.1007/s10957-009-9557-9.