For a given interval matrix, it would be valuable to have a practical method for determining the family of matrices which are inverses of its members. Since the exact family of inverse matrices can be difficult to find or to describe, effort is often applied to developing methods for determining matrix families with interval structure which "best" approximate or contain it. A common approach is to seek exact bounds on individual elements. In this paper, we show that computing exact bounds is NP-hard; therefore any algorithm will have at least exponential-time worst-case computational cost unless P = NP.

Recently Rohn and Poljak proved that for interval matrices with rank-one radius matrices testing singularitY is NP-complete. This paper will show that given any matrix family belonging to the class of matrix polytopes with hypercube domains and rank-one perturbation matrices, a class which contains the interval matrices, testing singularity reduces to testing whether a certain matrix is not a P-matrix. It follows from this result that the problem of testing whether a given matrix is a P-matrix is co-NP-complete.

An interval matrix can be represented in terms of a “center” matrix and a nonnegative error matrix, specifying maximum elementwise perturbations from the center matrix. A commonly proposed robust stability (regularity) characterization for an interval matrix with a stable (nonsingular) center matrix identifies the minimum scaling of this error matrix for which instability (singularity) is achieved. In this paper it is shown that approximating this minimum scaling is a MAX-SNP-hard problem. This implies that in the general case, unless the class of deterministic polynomial-time decision problems, P, equals the class of nondeterministic polynomial-time decision problems, NP, thought to be highly unlikely, this minimum scaling cannot be approximated with a ratio arbitrarily close to unity in polynomial time.