May 18 – 22, 2026
Virginia Tech
America/New_York timezone

New Results on the Doubly Stochastic Inverse Eigenvalue Problem

May 21, 2026, 11:00 AM
25m
Goodwin Hall 115 (Virginia Tech)

Goodwin Hall 115

Virginia Tech

Minisymposium Talk Eigenvalues of Nonnegative and Stochastic Matrices Eigenvalues of Nonnegative and Stochastic Matrices

Speaker

Ludovick Bouthat (Université Laval)

Description

Stochastic matrices are matrices with nonnegative entries whose rows each sum to $1$. When a matrix and its transpose are both stochastic, it is said to be \emph{doubly stochastic}. In 1938, Kolmogorov proposed the problem of characterizing the region of possible eigenvalues of an $n \times n$ stochastic matrix, and Karpelevich gave a complete description thirteen years later. This talk concerns the doubly stochastic analogue: characterizing the region $\omega_n$ of eigenvalues of $n \times n$ doubly stochastic matrices, which is contained in the unit disk.

Perfect and Mirsky (1965) conjectured that $\omega_n$ is the union of the regions $\Pi_k$ (the convex hulls of the $k$-th roots of unity) for $k = 1,\dots,n$. This conjecture holds for $n = 1,2,3,4$, but fails for $n = 5$. The case $n \ge 6$ remains open. In response to the scarcity of progress over the past 60 years, Levick, Pereira, and Kribs proposed a related conjecture. In this talk, I prove a stronger version of this conjecture.

The approach, based on majorization theory and geometric properties, also provides a potential general framework for characterizing $\omega_5$, as well as a numerical method for testing the conjecture for various values of $n$.

Author

Ludovick Bouthat (Université Laval)

Presentation materials

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