A smoothing Newton algorithm for weighted linear complementarity problem

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• 作者：Zhang Jian
• 关键词：Weighted linear complementarity problem ; Smoothing Newton method ; Locally quadratic convergence
• 刊名：Optimization Letters
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：10
• 期：3
• 页码：499-509
• 全文大小：395 KB
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• 作者单位：Zhang Jian (1)
  
  1. School of Science, Chang’an University, Xi’an, 710064, China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Optimization
  Operation Research and Decision Theory
  Numerical and Computational Methods in Engineering
  Numerical and Computational Methods
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1862-4480

文摘

In this paper, we present a new smoothing Newton method for solving monotone weighted linear complementarity problem (WCP). Our algorithm needs only to solve one linear system of equation and performs one line search per iteration. Any accumulation point of the iteration sequence generated by our algorithm is a solution of WCP. Under suitable conditions, our algorithm has local quadratic convergence rate. Numerical experiments show the feasibility and efficiency of the algorithm.