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In this paper we propose new primal-dual interior point methods (IPMs) for P*(κ) linear complementarity problems (LCPs) and analyze the iteration complexity of the algorithm. New search directions and proximity measures are defined based on a class of kernel functions,[수식] If a strictly feasible starting point is available and the parameter [수식] where [수식], then new large-update primal-dual interior point algorithms have [수식] iteration complexity which is the best known result for this method. For small-update methods, we have [수식] iteration complexity.
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참고문헌 (15)
참고문헌 신청
C. Roos / 1997 / Theory and Algorithms for Linear Optimization, An interior point approach. Wiley-Interscience Series in Discrete Mathematics and Optimization / John Wiley & Sons, L
G. M. Cho / 2007 / Complexity of large-update interior point algorithm for P＊(·) linear complementarity problems / Comput. Math. Appl. 53 (6) : 948 ~ 960
J. Miao / 1995 / A quadratically convergent O((· + 1)pnL)-iteration algorithm for the P＊(·)- matrix linear complementarity problem / Math. Programming 69 (3) : 355 ~ 368
J. Peng / 2001 / A new and efficient large-update interior-point method for linear optimization / Vychisl. Tekhnol. 6 (4) : 61 ~ 80
J. Peng / 2002 / Self-regular functions and new search directions for linear and semidefinite optimization / Math. Program. 93 (1) : 129 ~ 171
G. M. Cho / 2007 / Complexity of large-update interior point algorithm for P＊(·) linear complementarity problems / Comput. Math. Appl. 53 (6) : 948 ~ 960
J. Miao / 1995 / A quadratically convergent O((· + 1)pnL)-iteration algorithm for the P＊(·)- matrix linear complementarity problem / Math. Programming 69 (3) : 355 ~ 368
J. Peng / 2001 / A new and efficient large-update interior-point method for linear optimization / Vychisl. Tekhnol. 6 (4) : 61 ~ 80
J. Peng / 2002 / Self-regular functions and new search directions for linear and semidefinite optimization / Math. Program. 93 (1) : 129 ~ 171
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