Relevant bibliographies by topics / Gauss-Seidel method

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Gauss-Seidel method'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Gauss-Seidel method"

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S., Sathya, and Ramesh T. "Comparison of Gauss Jacobi Method and Gauss Seidel Method using Scilab." International Journal of Trend in Scientific Research and Development 3, no. 6 (2019): 1051–53. https://doi.org/10.5281/zenodo.3589302.

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Numerical Method is the important aspects in solving real world problems that are related to Mathematics, science, medicine, business etc. In this paper, We comparing the two methods by using the scilab 6.0.2 software coding to solve the iteration problem. which are Gauss Jacobi and Gauss Seidel methods of linear equations. S. Sathya | T. Ramesh "Comparison of Gauss Jacobi Method and Gauss Seidel Method using Scilab" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.
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Tuljaram Meghwar, Sher khan Awan, Muhammad Tariq, Muhammad Suleman, and Asif Ali Shaikh. "Substitutional Based Gauss-Seidel Method For Solving System of Linear Algebraic Equations." Babylonian Journal of Mathematics 2024 (January 10, 2024): 1–12. http://dx.doi.org/10.58496/bjm/2024/001.

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In this research paper a new modification of Gauss-Seidel method has been presented for solving the system of linear algebraic equations. The systems of linear algebraic equations have an important role in the field of science and engineering. This modification has been developed by using the procedure of Gauss-Seidel method and the concept of substitution techniques. Developed modification of Gauss-Seidel method is a fast convergent as compared to Gauss Jacobi’s method, Gauss-Seidel method and successive over-relaxation (SOR) method. It works on the diagonally dominant as well as positive def
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Enyew, Tesfaye Kebede, Gurju Awgichew, Eshetu Haile, and Gashaye Dessalew Abie. "Second-refinement of Gauss-Seidel iterative method for solving linear system of equations." Ethiopian Journal of Science and Technology 13, no. 1 (2020): 1–15. http://dx.doi.org/10.4314/ejst.v13i1.1.

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Although large and sparse linear systems can be solved using iterative methods, its number of iterations is relatively large. In this case, we need to modify the existing methods in order to get approximate solutions in a small number of iterations. In this paper, the modified method called second-refinement of Gauss-Seidel method for solving linear system of equations is proposed. The main aim of this study was to minimize the number of iterations, spectral radius and to increase rate of convergence. The method can also be used to solve differential equations where the problem is transformed
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Vinsensia, Desi, Yulia Utami, Fathia Siregar, and Muhammad Arifin. "Improve refinement approach iterative method for solution linear equition of sparse matrices." Jurnal Teknik Informatika C.I.T Medicom 15, no. 6 (2024): 306–13. http://dx.doi.org/10.35335/cit.vol15.2024.721.pp306-313.

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In this paper, systems of linear equations on sparse matrices investigated through modified improve method using Gauss-Seidel and successive overrelaxation (SOR) approach. Taking into adapted convergence rate on the Improve refinement Gauss-seidel outperformed the prior two Gauss-Seidel methods in terms of rate of convergence and number of iterations required to solve the problem by applying a modified version of the Gauss-Seidel approach. to observe the effectiveness of this method, the numerical example is given. The main findings in this study, that Gauss seidel improvement refinement gives
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Duan, Ban Xiang, Wen Ying Zeng, and Xiao Ping Zhu. "A Preconditioned Gauss-Seidel Iterative Method for Linear Complementarity Problem in Intelligent Materials System." Advanced Materials Research 340 (September 2011): 3–8. http://dx.doi.org/10.4028/www.scientific.net/amr.340.3.

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In this paper, the authors first set up new preconditioned Gauss-Seidel iterative method for solving the linear complementarity problem, whose preconditioned matrix is introduced. Then certain elementary operations row are performed on system matrix before applying the Gauss-Seidel iterative method. Moreover the sufficient conditions for guaranteeing the convergence of the new preconditioned Gauss-Seidel iterative method are presented. Lastly we report some computational results with the proposed method.
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SEBRO, AMRACH, HAILU MULETA, and SOLOMON GEBREGIORGIS. "Extrapolated refinement of generalized gauss- seidel scheme for solving system of linear equations." Berhan International Research Journal of Science and Humanities 4 (March 22, 2020): 12–23. http://dx.doi.org/10.61593/dbu.birjsh.01.01.55.

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In this paper, Extrapolated Refinement of Generalized Gauss-Seidel scheme for solving system of linear equations has been presented. In order to accelerate the rate of convergence of the scheme, the one-parameter family of splitting procedure has been introduced and the convergence of the method is well established. To validate the proposed method, three numerical examples were considered. Comparisons were made among Refinement of Generalized Jacobi, Generalized Gauss-Seidel, Refinement of Generalized Gauss-Seidel and Extrapolated Refinement of Generalized Gauss-Seidel schemes with respect to
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Wang, Yang, Jie Liu, Xiaoxiong Zhu, Qingyang Zhang, Shengguo Li, and Qinglin Wang. "Improving Structured Grid-Based Sparse Matrix-Vector Multiplication and Gauss–Seidel Iteration on GPDSP." Applied Sciences 13, no. 15 (2023): 8952. http://dx.doi.org/10.3390/app13158952.

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Structured grid-based sparse matrix-vector multiplication and Gauss–Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general purpose digital signal processor, which is a very significant embedded processor that has been introduced into high-performance computing. In this paper, we designed various optimization methods, which included a blocking method to improve data locality and increase memory access efficiency, a multicolor reordering method to develop Gauss–Seidel fine-grai
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Sukarna, S., Muhammad Abdy, and R. Rahmat. "Perbandingan Metode Iterasi Jacobi dan Metode Iterasi Gauss-Seidel dalam Menyelesaikan Sistem Persamaan Linear Fuzzy." Journal of Mathematics, Computations, and Statistics 2, no. 1 (2020): 1. http://dx.doi.org/10.35580/jmathcos.v2i1.12447.

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Penelitian ini mengkaji tentang menyelesaian Sistem Persamaan Linear Fuzzy dengan Membanding kan Metode Iterasi Jacobi dan Metode Iterasi Gauss-Seidel. Metode iterasi Jacobi merupakan salah satu metode tak langsung, yang bermula dari suatu hampiran Metode iterasi Jacobi ini digunakan untuk menyelesaikan persamaan linier yang proporsi koefisien nol nya besar. Iterasi dapat diartikan sebagai suatu proses atau metode yang digunakan secara berulang-ulang (pengulangan) dalam menyelesaikan suatu permasalahan matematika ditulis dalam bentuk . Pada metode iterasi Gauss-Seidel, nilai-nilai yang paling
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Daniel, E. E., D. O. Oyewole, S. A. Akinwunmi, and D. B. Awudang. "COMPARATIVE ANALYSIS OF GAUSS SEIDEL, CONJUGATE GRADIENT AND SUCCESSIVEOVER RELAXATION FOR THE SOLUTION OF NONSYMETRIC LINEAR EQUATIONS." FULafia Journal of Science and Technology 9, no. 1 (2025): 1–6. https://doi.org/10.62050/fjst2025.v9n1.349.

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This study undertakes a comparative analysis of three widely used computational methods namely; Gauss-Seidel, Conjugate Gradient, and Successive Over-Relaxation (SOR) for solving nonsymmetric linear equations. The main goal is to assess the effectiveness, efficiency and convergence rates of these methods when applied to nonsymmetric linear systems, which frequently occurs in scientific and engineering problems. The Gauss-Seidel method, knownfor its iterative simplicity and straightforward implementation, is compared with the Conjugate Gradient method, which is acclaimed for its robustness and
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Zhou, Bo Jian, Xu Hong Li, and Jie He. "A Comparative Study of Two Alternative Methods for the Path-Based Logit Stochastic User Equilibrium Problem." Advanced Materials Research 756-759 (September 2013): 1433–36. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1433.

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in this paper, a computational study of two alternative methods for the path-based logit stochastic user equilibrium model is conducted. The two methods under investigation are the Jacobi gradient projection method and the Gauss-Seidel gradient projection method. We compare the two methods on the Sioux Falls network. Numerical results indicate that for the path-based logit SUE problem, Jacobi gradient projection method is more efficient than Gauss-Seidel gradient projection method.
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Dissertations / Theses on the topic "Gauss-Seidel method"

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Wu, Wei. "Paving the Randomized Gauss-Seidel." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/scripps_theses/1074.

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The Randomized Gauss-Seidel Method (RGS) is an iterative algorithm that solves overdetermined systems of linear equations Ax = b. This paper studies an update on the RGS method, the Randomized Block Gauss-Seidel Method. At each step, the algorithm greedily minimizes the objective function L(x) = kAx bk2 with respect to a subset of coordinates. This paper describes a Randomized Block Gauss-Seidel Method (RBGS) which uses a randomized control method to choose a subset at each step. This algorithm is the first block RGS method with an expected linear convergence rate which can be described by the
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Liang, Yunxu. "Improved Gauss-Seidel iterative method on power networks." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0008140.

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Bokka, Naveen. "Comparison of Power Flow Algorithms for inclusion in On-line Power Systems Operation Tools." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1237.

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The goal of this thesis is to develop a new, fast, adaptive load flow algorithm that "automatically alternates" numerical methods including Newton-Raphson method, Gauss-Seidel method and Gauss method for a load flow run to achieve less run time. Unlike the proposed method, the traditional load flow analysis uses only one numerical method at a time. This adaptive algorithm performs all the computation for finding the bus voltage angles and magnitudes, real and reactive powers for the given generation and load values, while keeping track of the proximity to convergence of a solution. This work f
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Anderson, Curtis James. "Estimating the Optimal Extrapolation Parameter for Extrapolated Iterative Methods When Solving Sequences of Linear Systems." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1383826559.

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Jurča, Ondřej. "Ustálený chod a zkratové poměry v síti 110 kV E.ON napájené z rozvodny 110 kV Otrokovice v roce 2011." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219016.

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Distribution network 110 kV owned by E. ON in the area Otrokovice; powered by 110 kV and two variants of involvement contained.The first option is basic involvement, without the use of the bridge. The second option includes involvement with the bridge. The aim of this study is to compare; by calculating the steady-state network operation and short circuit conditions of the network, the involvement of these two options. The thesis is divided into two parts, theoretical and practical. The theoretical part consists of a description of the steady operation of networks of high-voltage and short cir
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Simonis, Joseph P. "Newton-Picard Gauss-Seidel." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-051305-162036/unrestricted/simonis.pdf.

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Simonis, Joseph P. "Newton-Picard Gauss-Seidel." Digital WPI, 2005. https://digitalcommons.wpi.edu/etd-dissertations/285.

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Newton-Picard methods are iterative methods that work well for computing roots of nonlinear equations within a continuation framework. This project presents one of these methods and includes the results of a computation involving the Brusselator problem performed by an implementation of the method. This work was done in collaboration with Andrew Salinger at Sandia National Laboratories.
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Coskun, Korhan. "Three Dimensional Laminar Compressible Navier Stokes Solver For Internal Rocket Flow Applications." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609086/index.pdf.

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A three dimensional, Navier-Stokes finite volume flow solver which uses Roe&rsquo<br>s upwind flux differencing scheme for spatial and Runge-Kutta explicit multi-stage time stepping scheme and implicit Lower-Upper Symmetric Gauss Seidel (LU-SGS) iteration scheme for temporal discretization on unstructured and hybrid meshes is developed for steady rocket internal viscous flow applications. The spatial accuracy of the solver can be selected as first or second order. Second order accuracy is achieved by piecewise linear reconstruction. Gradients of flow variables required for piecewise linear rec
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Wang, Da. "Accelerated granular matter simulation." Doctoral thesis, Umeå universitet, Institutionen för fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-110164.

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Modeling and simulation of granular matter has important applications in both natural science and industry. One widely used method is the discrete element method (DEM). It can be used for simulating granular matter in the gaseous, liquid as well as solid regime whereas alternative methods are in general applicable to only one. Discrete element analysis of large systems is, however, limited by long computational time. A number of solutions to radically improve the computational efficiency of DEM simulations are developed and analysed. These include treating the material as a nonsmooth dynamical
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Marquez, Damian Jose Ignacio. "Multilevel acceleration of neutron transport calculations." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19731.

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Thesis (M.S.)--Nuclear and Radiological Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Stacey, Weston M.; Committee Co-Chair: de Oliveira, Cassiano R.E.; Committee Member: Hertel, Nolan; Committee Member: van Rooijen, Wilfred F.G.
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Book chapters on the topic "Gauss-Seidel method"

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Nishida, Hiroshi, and Hairong Kuang. "Experiments on Asynchronous Partial Gauss-Seidel Method." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573937_14.

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Hladík, Milan. "Optimal Preconditioning for the Interval Parametric Gauss–Seidel Method." In Scientific Computing, Computer Arithmetic, and Validated Numerics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31769-4_10.

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Freundl, Christoph, and Ulrich Rüde. "Gauss-Seidel Iterative Method for the Computation of Physical Problems." In Algorithms Unplugged. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15328-0_30.

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Guo, Peng, and Shi-liang Wu. "A Modified Gauss-Seidel Iteration Method for Solving Absolute Value Equations." In Simulation Tools and Techniques. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72792-5_13.

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Koh, W. S., R. R. Ahmad, S. H. Jaaman, and J. Sulaiman. "Pricing Asian Option by Solving Black–Scholes PDE Using Gauss–Seidel Method." In Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017). Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7279-7_18.

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Silcowitz, Morten, Sarah Niebe, and Kenny Erleben. "Interactive Rigid Body Dynamics Using a Projected Gauss–Seidel Subspace Minimization Method." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25382-9_15.

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Faruqi, Abdul Hannan, M. Hamid Siddique, Abdus Samad, and Syed Fahad Anwer. "On the Recommendations for Reducing CPU Time of Multigrid Preconditioned Gauss–Seidel Method." In Trends in Mathematics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68281-1_21.

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Yang, Liu, and Jian Yang. "GPU-Accelerated Flow Simulations on Unstructured Grids Using a Multi-colored Gauss-Seidel Method." In 2023 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2023) Proceedings. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3998-1_56.

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Auslender, Alfred, Marc Teboulle, and Sami Ben-Tiba. "Coupling the Logarithmic-Quadratic Proximal Method and the Block Nonlinear Gauss-Seidel Algorithm for Linearly Constrained Convex Minimization." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-45780-7_3.

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Yoon, S. "Solution of three-dimensional Navier-Stokes equations using an implicit Gauss-Seidel scheme." In Thirteenth International Conference on Numerical Methods in Fluid Dynamics. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56394-6_275.

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Conference papers on the topic "Gauss-Seidel method"

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Sanitha Michail, C., M. R. Rashmi, and Vigna K. Ramachandaramurthy. "Power Flow Analysis in Microgrid Using Gauss-Seidel Method." In 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC). IEEE, 2024. https://doi.org/10.1109/icicec62498.2024.10808319.

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Thanuush, V., M. R. Rashmi, and Hoong Pin Lee. "A Comparative Study of Gauss-Seidel, Fast Decoupled, and Newton-Raphson Methods for Power Flow Analysis in Microgrids." In 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC). IEEE, 2024. https://doi.org/10.1109/icicec62498.2024.10808719.

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Hhonghao, He, Yuan Dongjin, Hou Yi, and Xu Jinqiu. "Preconditioned Gauss-Seidel Iterative Method for Linear Systems." In 2009 International Forum on Information Technology and Applications (IFITA). IEEE, 2009. http://dx.doi.org/10.1109/ifita.2009.339.

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Zhonghua Jiang, Ning Xu, Li Gao, Yuchun Ma, and Xianlong Hong. "Hierarchical thermal model using gauss-seidel method in floorplanning." In 2007 7th International Conference on ASIC. IEEE, 2007. http://dx.doi.org/10.1109/icasic.2007.4415819.

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Yan, Zhihui, Zeyuan Feng, and Mingjie Guo. "Improved Gauss‐Seidel method in near‐surface statics correction." In Beijing 2009 International Geophysical Conference and Exposition. Society of Exploration Geophysicists, 2009. http://dx.doi.org/10.1190/1.3603543.

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"PROJECTED GAUSS–SEIDEL SUBSPACE MINIMIZATION METHOD FOR INTERACTIVE RIGID BODY DYNAMICS - Improving Animation Quality using a Projected Gauss–Seidel Subspace Minimization Method." In International Conference on Computer Graphics Theory and Applications. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002830700380045.

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Liu, Qingbing. "Convergence of the Modified Gauss-Seidel Method for H-matrices." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.317.

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Darvishi, M. T., M. Azimbeigi, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "A New Preconditioner on Gauss-Seidel Method for H-Matrices." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2009: Volume 1 and Volume 2. AIP, 2009. http://dx.doi.org/10.1063/1.3241458.

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Martynenko, Sergey, Vadim Volokhov, and Pavel Toktaliev. "PSEUDOMULTIGRID GAUSS–SEIDEL METHOD FOR LARGE SCALE AND HIGH PERFORMANCE COMPUTING." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.1882.4478.

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Liu, Yang, Fei Gao, and Liang Zhang. "Multigrid Method with Adaptive Gauss-Seidel Smoother for Solving Poisson Equations." In 2010 2nd International Conference on Information Engineering and Computer Science (ICIECS). IEEE, 2010. http://dx.doi.org/10.1109/iciecs.2010.5677679.

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