alphaFeedback
PDF

WorksMathematics

Spectra of stochastic matrices with a Toeplitz interior

Publication details
License
CC BY 4.0
Status
Published
AI contributions
  • AI tools
  • GPT-6 Astra
  • Codex
View details
Abstract · v1

For stochastic matrices of order nn, the Karpelevič region is the set of all possible eigenvalues. We study matrices whose first and last mm rows are arbitrary probability vectors, while the remaining middle rows come from one probability vector by successive cyclic shifts to the right, moving the last entry to the first position each time. As mm increases, their eigenvalue regions expand from the polygon spanned by the nnth roots of unity. For n≥3n\ge3, they first equal the full Karpelevič region at m=⌈(n−1)/4⌉m=\lceil(n-1)/4\rceil. Reproducing all nn eigenvalues, counted with multiplicity, of every stochastic matrix of the same order requires m=⌊n/2⌋m=\lfloor n/2\rfloor. Thus, in even order n≥4n\ge4, retaining two consecutive rows with the Toeplitz pattern can exclude eigenvalue lists even when every possible eigenvalue occurs individually. The boundary of the Karpelevič region consists of arcs joining roots of unity. We determine the least mm for which every point of each arc occurs as an eigenvalue in our family. For arcs whose nonreal points first become possible eigenvalues at order n≥4n\ge4, and for m≥1m\ge1 below this minimum, no point on the arc other than its endpoints occurs in the family. At the minimum, every point on the arc becomes attainable. At the smallest order n≥4n\ge4 at which a nonreal point on a boundary arc away from its endpoints can occur as an eigenvalue, that point determines the characteristic polynomial of every stochastic matrix of order nn having it as an eigenvalue. Further results describe the smaller regions and the angles at which their boundaries approach roots of unity.

Review conversation

Reviews are not available for this work yet.