Chaotic iterative methods for the linear complementarity problems
نویسندگان
چکیده
منابع مشابه
Improved infeasible-interior-point algorithm for linear complementarity problems
We present a modified version of the infeasible-interior- We present a modified version of the infeasible-interior-point algorithm for monotone linear complementary problems introduced by Mansouri et al. (Nonlinear Anal. Real World Appl. 12(2011) 545--561). Each main step of the algorithm consists of a feasibility step and several centering steps. We use a different feasibility step, which tar...
متن کاملIterative Methods for a Class of Complementarity Problems
In this paper, we propose and study an algorithm for a new class of complementarity problems of finding u E R” such that u > 0, Tu + A(u) > 0; (u, Tu + A(u)) = 0, where T is a continuous mapping and A is a nonlinear transformation from R” into itself. It is proved that the approximate solution obtained from the iterative scheme converges to the exact solution. Several special cases are also dis...
متن کاملOn the modified iterative methods for $M$-matrix linear systems
This paper deals with scrutinizing the convergence properties of iterative methods to solve linear system of equations. Recently, several types of the preconditioners have been applied for ameliorating the rate of convergence of the Accelerated Overrelaxation (AOR) method. In this paper, we study the applicability of a general class of the preconditioned iterative methods under certain conditio...
متن کاملCorrector-predictor methods for sufficient linear complementarity problems
We present a new corrector-predictor method for solving sufficient linear complementarity problems for which a sufficiently centered feasible starting point is available. In contrast with its predictor-corrector counterpart proposed by Miao, the method does not depend on the handicap κ of the problem. The method has O((1+ κ)√nL)-iteration complexity, the same as Miao’s method, but our error est...
متن کاملSmoothing methods for convex inequalities and linear complementarity problems
A smooth approximation p(x;) to the plus function: maxfx; 0g, is obtained by integrating the sigmoid function 1=(1 + e ?x), commonly used in neural networks. By means of this approximation, linear and convex inequalities are converted into smooth, convex unconstrained minimization problems, the solution of which approximates the solution of the original problem to a high degree of accuracy for ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Applied Mathematics
سال: 1998
ISSN: 0377-0427
DOI: 10.1016/s0377-0427(98)00111-3