DocumentCode :
523559
Title :
Predictor-Corrector Smoothing Newton Method for Solving the Second-Order Cone Complementarity
Author :
Zhao, Huali ; Liu, Hongwei
Author_Institution :
Appl. Math. Dept., Xidian Univ., X´´ian, China
Volume :
2
fYear :
2010
fDate :
11-12 May 2010
Firstpage :
927
Lastpage :
930
Abstract :
In this paper we study predictor-corrector smoothing Newton method which were designed by Z. H. Huang, J. Han, and Z. Chen for nonlinear complementarity problem, we extends the Algorithm to second-order cone complementarity (SOCCP). Based on the Chen and Mangasarian smoothing function, we present a predictor-corrector smoothing Newton algorithm for solving the SOCCP. The neighbourhood of the path does not appear in the Algorithm. Thus, it does not need a few additional computations which keep the iteration sequence staying in the given neighbourhood. The algorithm is simpler than a predictor-corrector smoothing method by Chi Xiaoni, Liu Sanyang and this algorithm does not have restrictions regarding its starting point. The globally and locally superlinearly convergent under suitable assumptions are shown. Some preliminary computational results are reported.
Keywords :
Newton method; complementarity; predictor-corrector methods; smoothing methods; Chen smoothing function; Mangasarian smoothing function; nonlinear complementarity problem; predictor-corrector smoothing Newton method; second-order cone complementarity; Algebra; Automation; Convergence of numerical methods; Design methodology; Educational institutions; Information science; Mathematics; Newton method; Prediction algorithms; Smoothing methods; Predictor Corrector; Second-order cone Complementarity; Smoothing Newton Method;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
Type :
conf
DOI :
10.1109/ICICTA.2010.590
Filename :
5522599
Link To Document :
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