In this paper, we proposed various types of synchronous and asynchronous twostage multisplitting iterative methods for the parallel solution of linear algebraic systems. Convergence theories were given and relaxed for...In this paper, we proposed various types of synchronous and asynchronous twostage multisplitting iterative methods for the parallel solution of linear algebraic systems. Convergence theories were given and relaxed forms of these methodswere investigated when the coefficient matrix is either monotone or an H-matrix.Computational experiments on CHALLENGE-L were presented.展开更多
Asynchronous parallel multisplitting relaxation methods for solving large sparse linear complementarity problems are presented, and their convergence is proved when the system matrices are H-matrices having positive d...Asynchronous parallel multisplitting relaxation methods for solving large sparse linear complementarity problems are presented, and their convergence is proved when the system matrices are H-matrices having positive diagonal elements. Moreover, block and multi-parameter variants of the new methods, together with their convergence properties, are investigated in detail. Numerical results show that these new methods can achieve high parallel efficiency for solving the large sparse linear complementarity problems on multiprocessor systems.展开更多
A class of asynchronous nested matrix multisplitting methods for solving large-scale systems of linear equations are proposed, and their convergence characterizations are studied in detail when the coefficient matrice...A class of asynchronous nested matrix multisplitting methods for solving large-scale systems of linear equations are proposed, and their convergence characterizations are studied in detail when the coefficient matrices of the linear systems are monotone matrices and H-matrices, respectively.展开更多
Presents a class of relaxed asynchronous parallel multisplitting iterative methods for solving the linear complementarity problem on multiprocessor systems. Establishment of the methods; Convergence theories; Numerica...Presents a class of relaxed asynchronous parallel multisplitting iterative methods for solving the linear complementarity problem on multiprocessor systems. Establishment of the methods; Convergence theories; Numerical results.展开更多
Focuses on a study which presented a parallel chaotic multisplitting method for solving the large sparse linear complementarity problem. Preliminaries of the study; Equations of the parallel chaotic multisplitting met...Focuses on a study which presented a parallel chaotic multisplitting method for solving the large sparse linear complementarity problem. Preliminaries of the study; Equations of the parallel chaotic multisplitting method; Information on the convergence theories; Details on the parallel chaotic multisplitting relaxation methods.展开更多
Presents a study of the numerical behaviors of the relaxed asynchronous multisplitting methods for linear complementarity problems by solving typical problems from practical applications on a real multiprocessor syste...Presents a study of the numerical behaviors of the relaxed asynchronous multisplitting methods for linear complementarity problems by solving typical problems from practical applications on a real multiprocessor system. Description of the tested problems and computing environment used in the computations; Description of the asynchronous multisplitting unsymmetric accelerated overrelaxation method; Discussion of results.展开更多
The convergence of the parallel matrix multisplitting relaxation methods presented by Wang (Linear Algebra and Its Applications 154/156 (1991) 473 486) is further investigated. The investigations show that these relax...The convergence of the parallel matrix multisplitting relaxation methods presented by Wang (Linear Algebra and Its Applications 154/156 (1991) 473 486) is further investigated. The investigations show that these relaxation methods really have considerably larger convergence domains.展开更多
A class of asynchronous matrix multi-splitting multi-parameter relaxation methods, including the asynchronous matrix multisplitting SAOR, SSOR and SGS methods as well. as the known asynchronous matrix multisplitting A...A class of asynchronous matrix multi-splitting multi-parameter relaxation methods, including the asynchronous matrix multisplitting SAOR, SSOR and SGS methods as well. as the known asynchronous matrix multisplitting AOR, SOR and GS methods, etc., is proposed for solving the large sparse systems of linear equations by making use of the principle of sufficiently using the delayed information. These new methods can greatly execute the parallel computational efficiency of the MIMD-systems, and are shown to be convergent when the coefficient matrices are H-matrices. Moreover, necessary and sufficient conditions ensuring the convergence of these methods are concluded for the case that the coefficient matrices are L-matrices.展开更多
Abstract In this paper,a class of generalized parallel matrix multisplitting relaxation methods for solving linear complementarity problems on the high speed multiprocessor systems is set up.This class of methods not ...Abstract In this paper,a class of generalized parallel matrix multisplitting relaxation methods for solving linear complementarity problems on the high speed multiprocessor systems is set up.This class of methods not only includes all the existing relaxation methods for the linear complementarity problems,but also yields a lot of novel ones in the sense of multisplitting.We establish the convergence theories of this class of generalized parallel multisplitting relaxation methods under the condition that the system matrix is an H matrix with positive diagonal elements.展开更多
Parallel multisplitting nonlinear iterative methods are established for the system of nonlinear algebraic equations Aψ (x)+Tψ(x) = b, with A, T L(Rn) beingmatrices of particular properties, : Rn→ Rn being diagonal ...Parallel multisplitting nonlinear iterative methods are established for the system of nonlinear algebraic equations Aψ (x)+Tψ(x) = b, with A, T L(Rn) beingmatrices of particular properties, : Rn→ Rn being diagonal and continuousmappings, and b ∈ Rn a known vector; and their global convergence are investigated in detail under weaker conditions. Some numerical computations show thatthe new methods have better convergence properties than the known ones in theliterature.展开更多
Asynchronous parallel multisplitting nonlinear symmetric Gauss-Seidel methods are established for the system of nonlinear equations , withA, B∈L(Rn) being matrices of particular properties, being diagonal and continu...Asynchronous parallel multisplitting nonlinear symmetric Gauss-Seidel methods are established for the system of nonlinear equations , withA, B∈L(Rn) being matrices of particular properties, being diagonal and continuous mappings, and b ∈Rn a known vector. The establishments of these new methods are according to the principle of sufficiently using the delayed information and are concerning about the concrete characteristics of the multiprocessor systems. Therefore, they have considerably higher parallel computingefficiency. The global convergenge as well as the asymptotic convergence rates of these new methods are investigated in detail under suitable conditions.展开更多
文摘In this paper, we proposed various types of synchronous and asynchronous twostage multisplitting iterative methods for the parallel solution of linear algebraic systems. Convergence theories were given and relaxed forms of these methodswere investigated when the coefficient matrix is either monotone or an H-matrix.Computational experiments on CHALLENGE-L were presented.
基金Subsidized by The Special Funds For Major State Basic Research Projects G1999032803.
文摘Asynchronous parallel multisplitting relaxation methods for solving large sparse linear complementarity problems are presented, and their convergence is proved when the system matrices are H-matrices having positive diagonal elements. Moreover, block and multi-parameter variants of the new methods, together with their convergence properties, are investigated in detail. Numerical results show that these new methods can achieve high parallel efficiency for solving the large sparse linear complementarity problems on multiprocessor systems.
文摘A class of asynchronous nested matrix multisplitting methods for solving large-scale systems of linear equations are proposed, and their convergence characterizations are studied in detail when the coefficient matrices of the linear systems are monotone matrices and H-matrices, respectively.
基金The Special Funds For Major State Basic Research Project G1999032803.
文摘Presents a class of relaxed asynchronous parallel multisplitting iterative methods for solving the linear complementarity problem on multiprocessor systems. Establishment of the methods; Convergence theories; Numerical results.
基金the National Natural Science Foundation of China (19601036) and Subsidized by the SpecialFunds for Major State Basic Research
文摘Focuses on a study which presented a parallel chaotic multisplitting method for solving the large sparse linear complementarity problem. Preliminaries of the study; Equations of the parallel chaotic multisplitting method; Information on the convergence theories; Details on the parallel chaotic multisplitting relaxation methods.
基金the Special Funds for Major State Basic Research Projects G1999032803Supported by the National Natural Science Foundation of China (19601036).
文摘Presents a study of the numerical behaviors of the relaxed asynchronous multisplitting methods for linear complementarity problems by solving typical problems from practical applications on a real multiprocessor system. Description of the tested problems and computing environment used in the computations; Description of the asynchronous multisplitting unsymmetric accelerated overrelaxation method; Discussion of results.
文摘The convergence of the parallel matrix multisplitting relaxation methods presented by Wang (Linear Algebra and Its Applications 154/156 (1991) 473 486) is further investigated. The investigations show that these relaxation methods really have considerably larger convergence domains.
基金Project 19601036 supported by the National Natural Science Foundation of China.
文摘A class of asynchronous matrix multi-splitting multi-parameter relaxation methods, including the asynchronous matrix multisplitting SAOR, SSOR and SGS methods as well. as the known asynchronous matrix multisplitting AOR, SOR and GS methods, etc., is proposed for solving the large sparse systems of linear equations by making use of the principle of sufficiently using the delayed information. These new methods can greatly execute the parallel computational efficiency of the MIMD-systems, and are shown to be convergent when the coefficient matrices are H-matrices. Moreover, necessary and sufficient conditions ensuring the convergence of these methods are concluded for the case that the coefficient matrices are L-matrices.
文摘Abstract In this paper,a class of generalized parallel matrix multisplitting relaxation methods for solving linear complementarity problems on the high speed multiprocessor systems is set up.This class of methods not only includes all the existing relaxation methods for the linear complementarity problems,but also yields a lot of novel ones in the sense of multisplitting.We establish the convergence theories of this class of generalized parallel multisplitting relaxation methods under the condition that the system matrix is an H matrix with positive diagonal elements.
文摘Parallel multisplitting nonlinear iterative methods are established for the system of nonlinear algebraic equations Aψ (x)+Tψ(x) = b, with A, T L(Rn) beingmatrices of particular properties, : Rn→ Rn being diagonal and continuousmappings, and b ∈ Rn a known vector; and their global convergence are investigated in detail under weaker conditions. Some numerical computations show thatthe new methods have better convergence properties than the known ones in theliterature.
文摘Asynchronous parallel multisplitting nonlinear symmetric Gauss-Seidel methods are established for the system of nonlinear equations , withA, B∈L(Rn) being matrices of particular properties, being diagonal and continuous mappings, and b ∈Rn a known vector. The establishments of these new methods are according to the principle of sufficiently using the delayed information and are concerning about the concrete characteristics of the multiprocessor systems. Therefore, they have considerably higher parallel computingefficiency. The global convergenge as well as the asymptotic convergence rates of these new methods are investigated in detail under suitable conditions.
