English

The Doubly Stochastic Single Eigenvalue Problem: A Computational Approach

Spectral Theory 2020-04-07 v2

Abstract

The problem of determining DSnDS_n, the complex numbers that occur as an eigenvalue of an nn-by-nn doubly stochastic matrix, has been a target of study for some time. The Perfect-Mirsky region, PMnPM_n, is contained in DSnDS_n, and is known to be exactly DSnDS_n for n4n \leq 4, but strictly contained within DSnDS_n for n=5n = 5. Here, we present a Boundary Conjecture that asserts that the boundary of DSnDS_n is achieved by eigenvalues of convex combinations of pairs of (or single) permutation matrices. We present a method to efficiently compute a portion of DSnDS_n, and obtain computational results that support the Boundary Conjecture. We also give evidence that DSnDS_n is equal to PMnPM_n for certain n>5n > 5.

Keywords

Cite

@article{arxiv.1908.03647,
  title  = {The Doubly Stochastic Single Eigenvalue Problem: A Computational Approach},
  author = {Amit Harlev and Charles R. Johnson and Derek Lim},
  journal= {arXiv preprint arXiv:1908.03647},
  year   = {2020}
}
R2 v1 2026-06-23T10:44:09.089Z