The inverse eigenvalue problem of reflexive matrices with a submatrix constraint and its approximation

In this paper, we first consider the existence of and the general expression for the solution to the constrained inverse eigenvalue problem defined as follows: given a generalized reflection matrix P∈R ^n×n , a set of complex n-vectors {x _i } _i=1 ^m , a set of complex numbers {λ _i } _i=1 ^m , and an s-by-s real matrix C ₀, find an n-by-n real reflexive matrix C such that the s-by-s leading principal submatrix of C is C ₀, and {x _i } _i=1 ^m and {λ _i } _i=1 ^m are the eigenvectors and eigenvalues of C, respectively. We are then concerned with the best approximation problem for the constrained inverse problem whose solution set is nonempty. That is, given an arbitrary real n-by-n matrix $\tilde{C}$ , find a matrix C which is the solution to the constrained inverse problem such that the distance between C and $\tilde{C}$ is minimized in the Frobenius norm. We give an explicit solution and a numerical algorithm to the best approximation problem. An illustrative experiment is also presented.