Q#-matrices and Q†-matrices: two extensions of the Q-matrix concept
K. C. Sivakumar, P. Sushmitha, Michael J. Tsatsomeros
Abstract
K. C. Sivakumar, P. Sushmitha, Michael J. Tsatsomeros
Abstract
A real square matrix A is called a Q-matrix if the linear complementarity problem LCP(A,q) has a solution for all q∈Rn. This means that for every vector q, there exists a vector x≥0 such that y=Ax+q≥0 and xTy=0. Two new classes of matrices are studied, namely the Q#-matrices and Q†-matrices. If for every vector q∈R(A), there exists a vector x∈R(A) satisfying x≥0, y=Ax+q≥0 and xTy=0, then A is a Q#-matrix. If the vector x satisfying the above properties is instead required to be in R(AT), then A is a Q†-matrix. Properties of these classes of matrices and their relationship with the class of Q-matrices are studied.
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A real square matrix A is called a Q-matrix if the linear complementarity problem LCP(A,q) has a solution for all q∈Rn. This means that for every vector q, there exists a vector x≥0 such that y=Ax+q≥0 and xTy=0. Two new classes of matrices are studied, namely the Q#-matrices and Q†-matrices. If for every vector q∈R(A), there exists a vector x∈R(A) satisfying x≥0, y=Ax+q≥0 and xTy=0, then A is a Q#-matrix. If the vector x satisfying the above properties is instead required to be in R(AT), then A is a Q†-matrix. Properties of these classes of matrices and their relationship with the class of Q-matrices are studied.
Key concepts: Mathematics, Matrix (chemical analysis), Combinatorics, Square matrix, Integer matrix, Symmetric matrix, Nonnegative matrix, Eigenvalues and eigenvectors