Volume 22, Issue 4
A Constrained Optimization Approach for LCP

Ju-liang Zhang, Jian Chen & Xin-jian Zhuo

DOI:

J. Comp. Math., 22 (2004), pp. 509-522

Published online: 2004-08

Preview Full PDF 89 1668
Export citation
  • Abstract

In this paper, LCP is converted to an equivalent nonsmooth nonlinear equation system H(x, y) 0 by using the famous NCP function–Fischer-Burmeister function. Note that some equations in H(X, Y) = 0 are nonsmooth and nonlinear hence difficult to solve while the others are linear hence easy to solve. Then we further convert the nonlinear equation system H(X, Y) = 0 to an optimization problem with linear equality constraints. After that we study the conditions under which the K–T points of the optimization problem are the solutions of the original LCP and propose a method to solve the optimization problem. In this algorithm, the search direction is obtained by solving a strict convex programming at each iterative point. However, our algorithm is essentially different from traditional SQP method. The global convergence of the method is proved under mild conditions. In addition, we can prove that the algorithm is convergent superlinearly under the conditions: M is $P_0$ matrix and the limit point is a strict complementarity solution of LCP. Preliminary numerical experiments are reported with this method.

  • Keywords

LCP Strict complementarity Nonsmooth equation system Po matrix Superlinear convergence

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JCM-22-509, author = {}, title = {A Constrained Optimization Approach for LCP}, journal = {Journal of Computational Mathematics}, year = {2004}, volume = {22}, number = {4}, pages = {509--522}, abstract = { In this paper, LCP is converted to an equivalent nonsmooth nonlinear equation system H(x, y) 0 by using the famous NCP function–Fischer-Burmeister function. Note that some equations in H(X, Y) = 0 are nonsmooth and nonlinear hence difficult to solve while the others are linear hence easy to solve. Then we further convert the nonlinear equation system H(X, Y) = 0 to an optimization problem with linear equality constraints. After that we study the conditions under which the K–T points of the optimization problem are the solutions of the original LCP and propose a method to solve the optimization problem. In this algorithm, the search direction is obtained by solving a strict convex programming at each iterative point. However, our algorithm is essentially different from traditional SQP method. The global convergence of the method is proved under mild conditions. In addition, we can prove that the algorithm is convergent superlinearly under the conditions: M is $P_0$ matrix and the limit point is a strict complementarity solution of LCP. Preliminary numerical experiments are reported with this method. }, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/8860.html} }
TY - JOUR T1 - A Constrained Optimization Approach for LCP JO - Journal of Computational Mathematics VL - 4 SP - 509 EP - 522 PY - 2004 DA - 2004/08 SN - 22 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/8860.html KW - LCP KW - Strict complementarity KW - Nonsmooth equation system KW - Po matrix KW - Superlinear convergence AB - In this paper, LCP is converted to an equivalent nonsmooth nonlinear equation system H(x, y) 0 by using the famous NCP function–Fischer-Burmeister function. Note that some equations in H(X, Y) = 0 are nonsmooth and nonlinear hence difficult to solve while the others are linear hence easy to solve. Then we further convert the nonlinear equation system H(X, Y) = 0 to an optimization problem with linear equality constraints. After that we study the conditions under which the K–T points of the optimization problem are the solutions of the original LCP and propose a method to solve the optimization problem. In this algorithm, the search direction is obtained by solving a strict convex programming at each iterative point. However, our algorithm is essentially different from traditional SQP method. The global convergence of the method is proved under mild conditions. In addition, we can prove that the algorithm is convergent superlinearly under the conditions: M is $P_0$ matrix and the limit point is a strict complementarity solution of LCP. Preliminary numerical experiments are reported with this method.
Ju-liang Zhang, Jian Chen & Xin-jian Zhuo . (1970). A Constrained Optimization Approach for LCP. Journal of Computational Mathematics. 22 (4). 509-522. doi:
Copy to clipboard
The citation has been copied to your clipboard