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应用数学学报  2013, Vol. 36 Issue (5): 862-869    DOI:
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求解二次Lagrangian有限元方程的代数两水平方法
李明1, 李郴良2, 崔向照3
1. 红河学院数学学院, 蒙自 661199;
2. 桂林电子科技大学数学与计算科学学院, 桂林 541004;
3. 红河学院数学学院, 蒙自 661199
Algebraic Two Level Method for a Quadratic Lagrangian Finite Element Equation
LI Ming1, LI Chenliang2, CUI Xiangzhao3
1. Department of Mathematics, Honghe University, Mengzi 661199;
2. School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004;
3. Department of Mathematics, Honghe University, Mengzi 661199
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摘要 基于矩阵图集的粗化算法, 构造一种新的插值算子, 提出了瀑布型代数两重网格法; 然后结合部分几何信息, 提出了求解二次Lagrangian 有限元方程的代数两水平方法. 数值实验表明该算法稳健性强、计算量更少.
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李明
李郴良
崔向照
关键词二次Lagrangian有限元方程   插值算子   瀑布型代数两重网格法   代数两水平方法     
Abstract: A new interpolation operator is proposed by using the coarsening method only based on the graph of the matrix. Then a new cascadic algebraic two grid method is presented by using the new interpolation operator. And a new algebraic two level method for the quadratic Lagrangian finite element equation is given by combining the cascadic algebraic two grid method and some geometric information of the grids. The numerical experiment results show that the new methods are more efficient and robust.
Key wordsquadratic Lagrangian finite element equation   interpolation operator   cascadic algebraic two grid method   algebraic two level method   
收稿日期: 2011-01-09;
基金资助:国家自然科学基金(11161014);云南省科技厅青年(2012FD054)和红河学院硕博(XJ1S0925)资助项目.
引用本文:   
李明,李郴良,崔向照. 求解二次Lagrangian有限元方程的代数两水平方法[J]. 应用数学学报, 2013, 36(5): 862-869.
LI Ming,LI Chenliang,CUI Xiangzhao. Algebraic Two Level Method for a Quadratic Lagrangian Finite Element Equation[J]. Acta Mathematicae Applicatae Sinica, 2013, 36(5): 862-869.
 
[1] Maller R A. Asymptotics of Regressions with Stationary and Nonstationary Residuals. Stochastic Processes and their Applications, 2003, 105: 33-67
[2] Pere P. Adjusted Estimates and Wald Ststistics for the AR(1) Model with Constant. Journal of Econometrics, 2000, 98(2): 335-363
[3] Fuller W A. Introduction to Statistical Time Series. New York: John Wiley & Sons, 2nd Edition, 1996
[4] Carsoule F, Franses P H. A Note on Monitoring Time-varying Parameters in an Autoregression. Metrika, 2003, 57(1): 51-62
[5] Azrak R, Melaed G. Asymptotic Properties of Quasi-maximum Likelihood Estimators for ARMA Models with Time-dependent Coefficients. Statistical Inference for Stochastic Processes, 2006, 9(3): 279-330
[6] Dahlhaus R. Fitting Time Series Models to Nonstationary Processes. The Annals of Statistics, 1997, 25(1): 1-37
[7] Kwoun G H, Yajima Y. On an Autoregressive Model with Time-dependent Coefficients. Annals of the Institute of Statistical Mathematics, 1986, 35(2): 297-309
[8] Hu Hongchang. QML Estimators in Linear Regression Models with Functional Coefficient Autoregressive Trocesses. Mathematical Problems in Engineering, 2010, http://dx.doi.org/10.1155/2010/956907
[9] Song Lei, Hu Hongchang, Cheng Xiaosheng. Hypothesis Testing in Geueralized Models with Functional Coefficient Autoregressive Processes. Mathematical Problems in Engineering, 2012,http://dx.doi.org/10.1155/2012/862398
[10] Silvapulle J. Asymptotic Behavior of Robust Estimators of Regression and Scale Parameters with Fixed Carriers. The Annals of Statistics, 1985,13(4): 1490-1497
[11] Tong Xingwei, Cui Hengjian, Yu Peng. Consistency and Normality of Huber-Dutter Estimators for Partial Linear Model. Science in China (Series A: Mathematics), 2008, 51(10): 1831-1842
[12] Douglas P W. Asymptotics of Generalized M-estimation of Regression and Scale with Fixed Carriers. in an Approximately Luinear Model. Statistics & Probability Letters, 1996, 30: 271-285
[13] Frank H, Christian H, Elvezio R. A Smoothing Principle for the Huber and Other Location M-estimators. Computational Statistics and Data Analysis, 2011, 55: 324-337
[14] Arcones M A. The Bahadur-kiefer Repesentation of L_p Regression Estimators. Econometric Theory, 1996, 12: 257-283
[15] Ronner A E. Asymptotic Normality of P-norm Estimators in Multiple Regression. Wahrscheinlichkeitstheorie und Verwandte Gebiete, 1984, 66: 620-631
[16] Kickinger F. Algebraic Multigrid for Discrete Elliptic Second-order Problems. Multigrid Methods V. Proceedings of the 5th European Multigrid Conference, Hackbusch W(ed.), Springer Lecture Notes in Computational Science and Engineering, Vol. 3., Berlin: Springer-Verlag, 1998, 157-172
[17] 谭敏, 肖映雄, 舒适. 一种各向异性四边形网格下的代数多重网格法. 湘潭大学自然科学学报, 2005, 27(1): 78-84 (Tan M, Xiao Y X, Shu S. An Algebraic Multigrid Method on Anisotropic Quadrilateral Grid. Natu. Sci. J. Xiangtan Univ., 2005, 27(1): 78-84)
[1] 陈焕祯. 最低次混合有限元方法的最优L~∞误差估计[J]. 应用数学学报, 1998, 21(4): 0-0.
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