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论文类型：期刊论文

发表时间：2016-01-01

发表刊物：JOURNAL OF COMPUTATIONAL MATHEMATICS

收录刊物：SCIE

卷号：34

期号：3

页面范围：317-338

ISSN号：0254-9409

关键字：Levenberg-Marquardt method; Nonsmooth equations; Nonlinear complementarity problems

摘要：A parameter-self-adjusting Levenberg-Marquardt method (PSA-LMM) is proposed for solving a nonlinear system of equations F (x) = 0, where F : R-n -> Rn is a semismooth mapping. At each iteration, the LM parameter mu k is automatically adjusted based on the ratio between actual reduction and predicted reduction. The global convergence of PSA-LMM for solving semismooth equations is demonstrated. Under the BD-regular condition, we prove that PSA-LMM is locally superlinearly convergent for semismooth equations and locally quadratically convergent for strongly semismooth equations. Numerical results for solving nonlinear complementarity problems are presented.