A perturbation approach for an inverse linear second-order cone programming

Yi Zhang; Yong Jiang; Liwei Zhang; Jiangzhong Zhang

doi:10.3934/jimo.2013.9.171

Article Contents

2013, Volume 9, Issue 1: 171-189. Doi: 10.3934/jimo.2013.9.171

This issue Previous Article Proximal point algorithm for nonlinear complementarity problem based on the generalized Fischer-Burmeister merit function Next Article Threshold value of the penalty parameter in the minimization of $L_1$-penalized conditional value-at-risk

A perturbation approach for an inverse linear second-order cone programming

1.
Department of Mathematics, School of Science, East China University of Science and Technology, Shanghai, 200237
2.
School of Science, Shenyang Aerospace University, Shenyang, 110136
3.
Institute of ORCT, School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024
4.
Division of Science and Technology, Beijing Normal University-Hong Kong, Baptist University United International College, Zhuhai, 519085

Received: April 2011

Revised: June 2012

Published: January 2013

Abstract / Introduction Related Papers Cited by

Abstract

A type of inverse linear second-order cone programming problems is discussed, in which the parameters in both the objective function and the constraint set of a given linear second-order cone programming need to be adjusted as little as possible so that a known feasible solution becomes optimal. This inverse problem can be formulated as a minimization problem with second-order cone complementarity constraints. With the help of the smoothed Fischer-Burmeister function over second-order cones, we construct a smoothing approximation of the formulated problem whose feasible set and optimal solution set are demonstrated to be continuous and outer semicontinuous respectively at the perturbed parameter $\varepsilon=0$. A damped Newton method is employed to solve the perturbed problem and its global convergence and local quadratic convergence rate are shown. Finally, the numerical results are reported to show the effectiveness of the damped Newton method for solving the inverse linear second-order cone programming.

Keywords:

Mathematics Subject Classification: Primary: 90C26, 90C33; Secondary: 49M15, 49M20.

Citation:

Related Papers

Cited by

References

[1]	R. Ahuja and J. Orlin, Inverse optimization, Operations Research, 49 (2001), 771-783.doi: 10.1287/opre.49.5.771.10607.
[2]	R. Ahuja and J. Orlin, Combinatorial algorithms for inverse network flow problems, Networks, 40 (2002), 181-187.doi: 10.1002/net.10048.
[3]	F. Alizadeh and D. Goldfarb, Second order cone programming, Mathematical Programming, 95 (2003), 3-51.doi: 10.1007/s10107-002-0339-5.
[4]	W. Burton and P. Toint, On an instance of the inverse shortest paths problem, Mathematical Programming, 53 (1992), 45-61.doi: 10.1007/BF01585693.
[5]	M. Cai, X. Yang and J. Zhang, The complexity analysis of the inverse center location problem, Journal of Global Optimization, 15 (1999), 213-218.doi: 10.1023/A:1008360312607.
[6]	J. Chen, D. Sun and J. Sun, The $SC^1$ property of the squared norm of the SOC Fischer-Burmeister function, Operations Research Letters, 36 (2008), 385-392.doi: 10.1016/j.orl.2007.08.005.
[7]	X. Chen and M. Fukushima, A smoothing method for a mathematical program with P-matrix linear complementarity constraints, Computational Optimization and Applications, 27 (2004), 223-246.doi: 10.1023/B:COAP.0000013057.54647.6d.
[8]	Ejiri Takeshi, "A Smoothing Method for Mathematical Programs with Second-Order Cone Complementarity Constraints," Master thesis, Kyoto University in Kyoto, 2007.
[9]	F. Facchinei, H. Jiang and L. Qi, A smoothing method for mathematical programs with equilibrium constraints, Mathematical Programming, 85 (1999), 107-134.doi: 10.1007/s101070050048.
[10]	M. Fukushima, Z. Luo and J. Pang, A globally convergent sequential quadratic programming algorithm for mathematical programs with linear complementarity constraints, Computational Optimization and Applications, 10 (1998), 5-34.doi: 10.1023/A:1018359900133.
[11]	M. Fukushima, Z. Luo and P. Tseng, Smoothing functions for second-order cone complimentarity problems, SIAM Journal on Optimization, 12 (2001), 436-460.doi: 10.1137/S1052623400380365.
[12]	M. Fukushima and J. Pang, Convergence of a smoothing continuation method for mathematical problems with complementarity constraints, Lecture Notes in Economics and Mathematical Systems, 477 (1999), 105-116.doi: 10.1007/978-3-642-45780-7_7.
[13]	M. Grant and S. Boyd, "CVX Users' Guide," Available from: http://cvxr.com/cvx/doc/.
[14]	C. Heuberger, Inverse combinatorial optimization: a survey on problems, methods and results, Journal of Combinatorial Optimization, 8 (2004), 329-361.doi: 10.1023/B:JOCO.0000038914.26975.9b.
[15]	G. Iyengar and W. Kang, Inverse conic programming and applications, Operations Research Letters, 33 (2005), 319-330.doi: 10.1016/j.orl.2004.04.007.
[16]	H. Jiang and D. Ralph, Smooth SQP methods for mathematical programs with nonlinear complementarity constraints, SIAM Journal on Optimization, 10 (2000), 779-808.doi: 10.1137/S1052623497332329.
[17]	G. Lin and M. Fukushima, Some exact penalty results for nonlinear programs and their applications to mathematical programs with equilibrium constraints, Journal of Optimization Theory and Applications, 118 (2003), 67-80.doi: 10.1023/A:1024787424532.
[18]	G. Lin and M. Fukushima, A modified relaxation scheme for mathematical prgrams with complementarity constraints, Annals of Operations Research, 133 (2005), 63-84.doi: 10.1007/s10479-004-5024-z.
[19]	Z. Luo, J. Pang and D. Ralph, "Mathematical Programs with Equilibrium Constraints," Cambridge University Press, Cambridge, United Kingdom, 1996.doi: 10.1017/CBO9780511983658.
[20]	L. Qi, Convergence analysis of some algorithms for solving nonsmooth equations, Mathematics of Operations Research, 18 (1993), 227-244.doi: 10.1287/moor.18.1.227.
[21]	R. Rockafellar and R. Wets, "Variational Analysis," Springer-Verlag, New York, 1998.doi: 10.1007/978-3-642-02431-3.
[22]	S. Scholtes and M. Stöhr, Exact penalization of mathematical programs with equilibrium constraints, SIAM Journal on Control and Optimization, 37 (1999), 617-652.doi: 10.1137/S0363012996306121.
[23]	S. Scholtes, Convergence properties of a regularization scheme for mathematical programs with complementarity constraints, SIAM Journal on Optimization, 25 (2001), 1-22.doi: 10.1137/S1052623499361233.
[24]	X. Xiao, L. Zhang and J. Zhang, A smoothing Newton method for a type of inverse semi-definite quadratic programming problem, Journal of Computational and Applied Mathematics, 223 (2009), 485-498.doi: 10.1016/j.cam.2008.01.028.
[25]	J. Zhang and Z. Liu, Calculating some inverse linear programming problems, Journal of Computational and Applied Mathematics, 72 (1996), 261-273.doi: 10.1016/0377-0427(95)00277-4.
[26]	J. Zhang and Z. Liu, A further study on inverse linear programming problems, Journal of Computational and Applied Mathematics, 106 (1999), 345-359.doi: 10.1016/S0377-0427(99)00080-1.
[27]	J. Zhang, Z. Liu and Z. Ma, Some reverse location problems, European Journal of Operations Research, 124 (2000), 77-88.doi: 10.1016/S0377-2217(99)00122-8.
[28]	J. Zhang and Z. Ma, Solution structure of some inverse combinatorial optimization problems, Journal of Combinatorial Optimization, 3 (1999), 127-139.doi: 10.1023/A:1009829525096.
[29]	J. Zhang and L. Zhang, An augmented Lagrangian method for a class of inverse quadratic programming problems, Applied Mathematics and Optimization, 61 (2010), 57-83.doi: 10.1007/s00245-009-9075-z.
[30]	J. Zhang, L. Zhang and X. Xiao, A Perturbation approach for an inverse quadratic programming problem, Mathematical Methods of Operations Research, 72 (2010), 379-404.doi: 10.1007/s00186-010-0323-4.
[31]	Y. Zhang, L. Zhang and J. Wu, Convergence properties of a smoothing approach for mathematical programs with second-order cone complementarity constraints, Set-Valued and Variational Analysis, 19 (2011), 609-646.doi: 10.1007/s11228-011-0190-z.