We propose an interior point method to compute solution of linear complementarity problem LCP (q, A) given that A is a real square hidden Z-matrix (generalization of Z-matrix) and q is a real vector. The class of hidden Z-matrix is important in the context of mathematical programming and game theory. We study the solution aspects of linear complementarity problem with $$A \in$$
 hidden Z-matrix. We observe that our proposed algorithm can process LCP (q, A) in polynomial time under some assumptions. Two numerical examples are illustrated to support our result.

On hidden Z-matrix and interior point algorithm

In this article, we study linear complementarity problem with hidden Z-matrix. We extend the results of Fiedler and Ptak for the linear system in complementarity problem using game theoretic approa...

More on hidden Z-matrices and linear complementarity problem

In this article, we show that under certain condition the generalized Leontief model is solvable by iterative descent method based on infeasible interior point algorithm. We prove the convergence of the method from strictly positive starting point. A numerical example is presented to demonstrate the performance of the algorithm.

Iterative Descent Method for Generalized Leontief Model

In this article, we introduce the class of semimonotone star ($E_0^s$) matrices. We establish the importance of the class of $E_0^s$-matrices in the context of complementarity theory. We show that the principal pivot transform of $E_0^s$-matrix is not necessarily $E_0^s$ in general. However, we prove that $\tilde{E_0^s}$-matrices, a subclass of the $E_0^s$-matrices with some additional conditions, is in $E_0^f$ by showing this class is in $P_0.$ We prove that LCP$(q, A)$ can be processable by Lemke's algorithm if $A\in \tilde{E_0^s}\cap P_0.$ We find some conditions for which the solution set of LCP$(q, A)$ is bounded and stable under the $\tilde{E^s_0}$-property. We propose an algorithm based on an interior point method to solve LCP$(q, A)$ given $A \in \tilde{E^{s}_{0}}.$

On Semimonotone Star Matrices and Linear Complementarity Problem

More on matrix splitting modulus-based iterative methods for solving linear complementarity problem

In this paper we consider criss-cross method for finding solution of a linear complementarity problem. The criss-cross method is a pivoting procedure. We show that the criss-cross method is able to compute solution of a linear complementarity problem in finite steps in case of some new matrix classes. We present a numerical illustration to show a comparison between criss-cross method and Lemke’s algorithm with respect to number of iterations before finding a solution. Finally we raise an open problem in the context of criss-cross method.

Finiteness of Criss-Cross Method in Complementarity Problem

In this paper, we propose an iterative and descent type interior point method to compute solution of linear complementarity problem LCP(q, A) given that A is real square matrix and q is a real vector. The linear complementarity problem includes many of the optimization problems and applications. In this context, we consider the class of generalized positive subdefinite matrices (GPSBD) which is a generalization of the class of positive subdefinite (PSBD) matrices. Though Lemke’s algorithm is frequently used to solve small and medium-size LCP(q, A), Lemke’s algorithm does not compute solution of all problems. It is known that Lemke’s algorithm is not a polynomial time bound algorithm. We show that the proposed algorithm converges to the solution of LCP(q, A) where A belongs to GPSBD class. We provide the complexity analysis of the proposed algorithm. A numerical example is illustrated to show the performance of the proposed algorithm.

Deepmala

Papers

Finiteness of Criss-Cross Method in Complementarity Problem

On Generalized Positive Subdefinite Matrices and Interior Point Algorithm