Article ID Journal Published Year Pages File Type
4602920 Linear Algebra and its Applications 2008 13 Pages PDF
Abstract

In the first part, we obtain two easily calculable lower bounds for ‖A-1‖, where ‖·‖ is an arbitrary matrix norm, in the case when A is an M-matrix, using first row sums and then column sums. Using those results, we obtain the characterization of M-matrices whose inverses are stochastic matrices. With different approach, we give another easily calculable lower bounds for ‖A-1‖∞ and ‖A-1‖1 in the case when A is an M-matrix. In the second part, using the results from the first part, we obtain our main result, an easily calculable upper bound for ‖A-1‖1 in the case when A is an SDD matrix, thus improving the known bound. All mentioned norm bounds can be used for bounding the smallest singular value of a matrix.

Related Topics
Physical Sciences and Engineering Mathematics Algebra and Number Theory