A multisplitting method for symmetric linear complementarity problems
Journal of Computational and Applied Mathematics
Integer Solution for Linear Complementarity Problem
Mathematics of Operations Research
Chaotic iterative methods for the linear complementarity problems
Journal of Computational and Applied Mathematics
On the Convergence of the Multisplitting Methods for the Linear Complementarity Problem
SIAM Journal on Matrix Analysis and Applications
Modified AOR methods for linear complementarity problem
Applied Mathematics and Computation
Improving projected successive overrelaxation method for linear complementarity problems
Applied Numerical Mathematics
On the solution of large, structured linear complementarity problems: III.
On the solution of large, structured linear complementarity problems: III.
Preconditioned AOR iterative method for linear systems
Applied Numerical Mathematics
Convergence of SSOR multisplitting method for an H-matrix
Journal of Computational and Applied Mathematics
Inexact multisplitting methods for linear complementarity problems
Journal of Computational and Applied Mathematics
On convergence of two-stage splitting methods for linear complementarity problems
Journal of Computational and Applied Mathematics
Conjugate gradient method for the linear complementarity problem with S-matrix
Mathematical and Computer Modelling: An International Journal
A power penalty method for linear complementarity problems
Operations Research Letters
Hi-index | 7.29 |
Many problems in the areas of scientific computing and engineering applications can lead to the solution of the linear complementarity problem LCP (M,q). It is well known that the matrix multisplitting methods have been found very useful for solving LCP (M,q). In this article, by applying the generalized accelerated overrelaxation (GAOR) and the symmetric successive overrelaxation (SSOR) techniques, we introduce two class of synchronous matrix multisplitting methods to solve LCP (M,q). Convergence results for these two methods are presented when M is an H-matrix (and also an M-matrix). Also the monotone convergence of the new methods is established. Finally, the numerical results show that the introduced methods are effective for solving the large and sparse linear complementary problems.