599 research outputs found
New Relaxation Modulus Based Iterative Method for Large and Sparse Implicit Complementarity Problem
This article presents a class of new relaxation modulus-based iterative
methods to process the large and sparse implicit complementarity problem (ICP).
Using two positive diagonal matrices, we formulate a fixed-point equation and
prove that it is equivalent to ICP. Also, we provide sufficient convergence
conditions for the proposed methods when the system matrix is a -matrix or
an -matrix.
Keyword: Implicit complementarity problem, -matrix, -matrix, matrix
splitting, convergenceComment: arXiv admin note: substantial text overlap with arXiv:2303.1251
Applications of a splitting algorithm to decomposition in convex programming and variational inequalities
Cover title.Includes bibliographical references.Partially supported by the U.S. Army Research Office (Center for Intelligent Control Systems) DAAL03-86-K-0171 Partially supported by the National Science Foundation. NSF-ECS-8519058by Paul Tseng
On preconditioned SSOR methods for the linear complementarity problem
In this paper, we consider the preconditioned iterative methods for solving the linear complementarity problem associated with an M-matrix. Two preconditioned SSOR methods for solving the linear complementarity problem are proposed. The convergence of the proposed methods are analyzed, and the comparison results are derived. The comparison results show that the proposed preconditioned SSOR methods accelerate the convergent rate of the SSOR method. Numerical experiments verify the theory results
Further applications of a splitting algorithm to decomposition in variational inequalities and convex programming
Cover title.Includes bibliographical references.Partially supported by the U.S. Army Research Office (Center for Intelligent Control Systems) DAAL03-86-K-0171 Partially supported by the National Science Foundation. NSF-ECS-8519058Paul Tseng
On the initial estimate of interface forces in FETI methods
The Balanced Domain Decomposition (BDD) method and the Finite Element Tearing
and Interconnecting (FETI) method are two commonly used non-overlapping domain
decomposition methods. Due to strong theoretical and numerical similarities,
these two methods are generally considered as being equivalently efficient.
However, for some particular cases, such as for structures with strong
heterogeneities, FETI requires a large number of iterations to compute the
solution compared to BDD. In this paper, the origin of the bad efficiency of
FETI in these particular cases is traced back to poor initial estimates of the
interface stresses. To improve the estimation of interface forces a novel
strategy for splitting interface forces between neighboring substructures is
proposed. The additional computational cost incurred is not significant. This
yields a new initialization for the FETI method and restores numerical
efficiency which makes FETI comparable to BDD even for problems where FETI was
performing poorly. Various simple test problems are presented to discuss the
efficiency of the proposed strategy and to illustrate the so-obtained numerical
equivalence between the BDD and FETI solvers
The Reduced Order Method for Solving the Linear Complementarity Problem with an M-Matrix
In this paper, by seeking the zero and the positive entry positions of the solution, we provide a direct method, called the reduced order method, for solving the linear complementarity problem with an M-matrix. By this method, the linear complementarity problem is transformed into a low order linear complementarity problem with some low order linear equations and the solution is constructed by the solution of the low order linear complementarity problem and the solutions of these low order linear equations in the transformations. In order to show the accuracy and the effectiveness of the method, the corresponding numerical experiments are performed
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