Eigenvalue regions and realising monotone stochastic matrices
Brando Vagenende, Brecht Verbeken, Marie-Anne Guerry

TL;DR
This paper explores the spectral properties of monotone stochastic matrices, characterizing their eigenvalue regions and providing conditions for their realization, revealing smaller eigenvalue regions compared to general stochastic matrices.
Contribution
It introduces a detailed analysis of eigenvalue regions for monotone stochastic matrices, including complete determination for sizes up to 3 and a reduction theorem for larger sizes.
Findings
Eigenvalue regions for monotone matrices up to order 3 are fully characterized.
Pairs of non-trivial eigenvalues for 3x3 monotone matrices are characterized with realising matrices.
Eigenvalue regions for n x n monotone matrices are contained within those of (n-1) x (n-1) stochastic matrices for n ≥ 4.
Abstract
Eigenvalues of stochastic matrices have been studied from two complementary perspectives. The individual eigenvalues are characterised through the well-established Karpelevich regions. The spectrum as a whole has also been analysed, yielding powerful results such as the Johnson-Loewy-London (JLL) inequalities. Current research now turns toward particular subsets of stochastic matrices, among others the doubly stochastic matrices. This paper studies spectral properties of monotone stochastic matrices which are characterised by the fact that each row stochastically dominates the preceding one, and which arise in contexts such as intergenerational mobility, equal-input models, and credit-rating systems. This paper analyses the dominance matrix associated with a monotone matrix, which is a non-negative matrix that preserves the non-trivial eigenvalues. Properties are established and the…
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Taxonomy
TopicsMatrix Theory and Algorithms · Point processes and geometric inequalities · Mathematics and Applications
