Research on Iterative Method in Solving Linear Equations on the Hadoop Platform

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Solving linear equations is ubiquitous in many engineering problems, and iterative method is an efficient way to solve this question. In this paper, we propose a general iteration method for solving linear equations. Our general iteration method doesnt contain denominators in its iterative formula, and this relaxes the limits that traditional iteration methods require the coefficient aii to be non-zero. Moreover, as there is no division operation, this method is more efficient. We implement this method on the Hadoop platform, and compare it with the Jacobi iteration, the Guass-Seidel iteration and the SOR iteration. Experiments show that our proposed general iteration method is not only more efficient, but also has a good scalability.

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2763-2768

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] C. Qiu, M. Diao and Shubo Yue, A New Algebra Method and Its Applications for Nonlinear Evolution Equations, IJACT: International Journal of Advancements in Computing Technology, Vol. 4, No. 2, pp.41-49, (2012).

DOI: 10.4156/ijact.vol4.issue2.6

Google Scholar

[2] T. Li, A New Algorithm for Triangular Factorization of the Inversion of Toeplitz Type Matrix, JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 6, No. 23, pp.698-706, (2012).

DOI: 10.4156/jdcta.vol6.issue23.81

Google Scholar

[3] L. Feng, S. Yan, Y. He, Y. Yang and Ping Li, Inverse Eigenvalue Problem for Jacobi Matrix, JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 6, No. 16, pp.395-402, (2012).

DOI: 10.4156/jdcta.vol6.issue16.48

Google Scholar

[4] H. Bing, C. Heshan and Li Tianzeng, The Mathematical Modeling for a Game of Bears-change color, JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 6, No. 12, pp.141-148, (2012).

DOI: 10.4156/jdcta.vol6.issue12.17

Google Scholar

[5] S. Sun, S. Wang, W. Shen, W. Xu and Y. Zheng, A Study of the Memory Wall within the Jacobi Iteration Method, pp.964-969, (2012).

Google Scholar

[6] H. Yan and Q. Zheng, A LOOP DISTRIBUTED GUASS-SEIDEL ITERATIVE ALGORITHM FOR SOLVING LINEAR EQUATIONS, Jisuanji Yingyong yu Ruanjian, Vol. 28, No. 7, pp.262-263, (2011).

Google Scholar

[7] W. Zheng and Z. Zhao, Analysis of Block-SOR Iteration for the three dimensional Laplacian, Anziam Journal, Vol. 50, (2009).

DOI: 10.21914/anziamj.v50i0.402

Google Scholar

[8] J. Dean and S. Ghemawat, Mapreduce: simplified data processing on large clusters, in OSDI, pp.1-10, (2004).

Google Scholar

[9] L. Page, S. Brin, R. Motwani and T. Winograd, The PageRank citation ranking: bringing order to the web, (1999).

Google Scholar

[10] T.H. Haveliwala, Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search, Knowledge and Data Engineering, IEEE Transactions on, Vol. 15, No. 4, pp.784-796, (2003).

DOI: 10.1109/tkde.2003.1208999

Google Scholar

[11] M. Richardson and P. Domingos, The intelligent surfer: Probabilistic combination of link and content information in pagerank, MIT Press, Cambridge, MA, (2002).

Google Scholar

[12] A.N. Langville and C.D. Meyer, Deeper inside pagerank, Internet Mathematics, Vol. 1, No. 3, pp.335-380, (2004).

DOI: 10.1080/15427951.2004.10129091

Google Scholar

[13] J.M. Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM (JACM), Vol. 46, No. 5, pp.604-632, (1999).

DOI: 10.1145/324133.324140

Google Scholar

[14] H. Deng, M.R. Lyu and I. King, A generalized Co-HITS algorithm and its application to bipartite graphs, pp.239-248, (2009).

Google Scholar

[15] L. Li, Y. Shang and W. Zhang, Improvement of HITS-based algorithms on web documents, Proceedings of the 11th international conference on World Wide Web, pp.527-535, (2002).

DOI: 10.1145/511446.511514

Google Scholar

[16] R. Lempel and S. Moran, The stochastic approach for link-structure analysis (SALSA) and the TKC effect, Computer Networks, Vol. 33, No. 1, pp.387-401, (2000).

DOI: 10.1016/s1389-1286(00)00034-7

Google Scholar

[17] Hadoop, http: /hadoop. apache. org/common/docs/r0. 16. 4/hdfsdesign. html.

Google Scholar

[18] Pig, http: /hadoop. apache. org/pig.

Google Scholar

[19] R. Chaiken, B. Jenkins, P.A. Larson, B. Ramsey and D. Shakib, et al., SCOPE: easy and efficient parallel processing of massive data sets, Proceedings of the VLDB Endowment, Vol. 1, No. 2, pp.1265-1276, (2008).

DOI: 10.14778/1454159.1454166

Google Scholar

[20] C. Olston, B. Reed, U. Srivastava, R. Kumar and A. Tomkins, Pig latin: a not-so-foreign language for data processing, SIGMOD, pp.1099-1110, (2008).

DOI: 10.1145/1376616.1376726

Google Scholar

[21] H. Yang, A. Dasdan, R. Hsiao and D. Parker, Map-reduce-merge: simplified relational data processing on large clusters, SIGMOD, pp.1029-1040, (2007).

DOI: 10.1145/1247480.1247602

Google Scholar