A note on a relationship between the inverse eigenvalue problems for nonnegative and doubly stochastic matrices and some applications

In this note, we establish some connection between the nonnegative inverse eigenvalue problem and that of doubly stochastic one. More precisely, we prove that if $(r; {\lambda}_2, ..., {\lambda}_n)$ is the spectrum of an $n\times n$ nonnegative matrix A with Perron eigenvalue r, then there exists a least real number $k_A\geq -r$ such that $(r+\epsilon; {\lambda}_2, ..., {\lambda}_n)$ is the spectrum of an $n\times n$ nonnegative generalized doubly stochastic matrix for all $\epsilon\geq k_A.$ As a consequence, any solutions for the nonnegative inverse eigenvalue problem will yield solutions to the doubly stochastic inverse eigenvalue problem. In addition, we give a new sufficient condition for a stochastic matrix A to be cospectral to a doubly stochastic matrix B and in this case B is shown to be the unique closest doubly stochastic matrix to A with respect to the Frobenius norm. Some related results are also discussed.Comment: 8 page

On the Distributions of the Lengths of the Longest Monotone Subsequences in Random Words

We consider the distributions of the lengths of the longest weakly increasing and strongly decreasing subsequences in words of length N from an alphabet of k letters. We find Toeplitz determinant representations for the exponential generating functions (on N) of these distribution functions and show that they are expressible in terms of solutions of Painlev\'e V equations. We show further that in the weakly increasing case the generating function gives the distribution of the smallest eigenvalue in the k x k Laguerre random matrix ensemble and that the distribution itself has, after centering and normalizing, an N -> infinity limit which is equal to the distribution function for the largest eigenvalue in the Gaussian Unitary Ensemble of k x k hermitian matrices of trace zero.Comment: 30 pages, revised version corrects an error in the statement of Theorem

Centrosymmetric Matrices in the Sinc Collocation Method for Sturm-Liouville Problems

Recently, we used the Sinc collocation method with the double exponential transformation to compute eigenvalues for singular Sturm-Liouville problems. In this work, we show that the computation complexity of the eigenvalues of such a differential eigenvalue problem can be considerably reduced when its operator commutes with the parity operator. In this case, the matrices resulting from the Sinc collocation method are centrosymmetric. Utilizing well known properties of centrosymmetric matrices, we transform the problem of solving one large eigensystem into solving two smaller eigensystems. We show that only 1/(N+1) of all components need to be computed and stored in order to obtain all eigenvalues, where (2N+1) corresponds to the dimension of the eigensystem. We applied our result to the Schr\"odinger equation with the anharmonic potential and the numerical results section clearly illustrates the substantial gain in efficiency and accuracy when using the proposed algorithm.Comment: 11 pages, 4 figure