arXiv subjects

Francesco Hrobat

Publications and source records attributed to Francesco Hrobat.

3 recordsLinked to original sources

arXiv2026

Matrix Perturbation Theory in the Tangent Space of Isospectral Matrices

Eigenvalue and eigenvector perturbation theory is a fundamental topic in several disciplines, including numerical linear algebra, quantum physics, and related fields. The central problem is to understand how the eigenvalues and eigenvectors of a matrix $A \in \mathbb{C}^{n \times n}$ change under the addition of a perturbation matrix $E \in \mathbb{C}^{n \times n}$. Much of the existing literature focuses on structured perturbations. For example, in [C.-K. Li and R.-C. Li, Linear Algebra Appl. 2005], the matrix $A$ is assumed to be Hermitian and block diagonal, while the perturbation $E$ is Hermitian and block off-diagonal. In this work, we investigate a different structured setting in which the perturbation has the commutator form $E = AB - BA$ for some matrix $B$, which we show to be a generalization of the block diagonal structure considered by Li and Li. First, we extend their main result by showing that the perturbation of the $i$-th eigenvalue of $A$, denoted by $\lambda_i$, is of order $\|E\|^2 / \eta_i$, where $\eta_i = \min_{j \neq i} |\lambda_i - \lambda_j|$ is the spectral gap associated with $\lambda_i$. Second, we provide a detailed analysis of the role played by the matrix $B$ in the perturbation of the eigenvectors. This analysis is further generalized to the case of block-diagonal matrices with multiple eigenvalues, as well as to perturbed singular values and eigenvalues of Jordan blocks.

math.NA↗

arXiv2025

Generalized Friendship Paradoxes in Network Science

Generalized friendship paradoxes occur when, on average, our friends have more of some attribute than us. These paradoxes are relevant to many aspects of human interaction, notably in social science and epidemiology. Here, we derive new theoretical results concerning the inevitability of a paradox arising, using a linear algebra perspective. Following the seminal 1991 work of Scott L. Feld, we consider two distinct ways to measure and compare averages, which may be regarded as global and local. For global averaging, we show that a generalized friendship paradox holds for a large family of walk-based centralities, including Katz centrality and total subgraph communicability, and also for nonbacktracking eigenvector centrality. However, we also find counterexamples for centralities based on walks of even length. For local averaging we establish a paradox for nonbacktracking eigenvector centrality and we characterize the cases where the paradox holds with equality for the walk-based case. Defining loneliness as the reciprocal of the number of friends, we show that for this attribute the generalized and local friendship paradoxes always hold in reverse. In this sense, we are always more lonely, on average, than our friends. We also derive global and local averaging paradoxes for the case where the arithmetic mean is replaced by the geometric mean. As well as unifying and adding to the literature in this area, we highlight some open questions.

physics.soc-ph↗

arXiv2025

Lanczos with compression for symmetric matrix Lyapunov equations

This work considers large-scale Lyapunov matrix equations of the form $AX + XA = \boldsymbol{c}\boldsymbol{c}^T$, where $A$ is a symmetric positive definite matrix and $\boldsymbol{c}$ is a vector. Motivated by the need to solve such equations in a wide range of applications, various numerical methods have been developed to compute low-rank approximations of the solution matrix $X$. In this work, we focus on the Lanczos method, which has the distinct advantage of requiring only matrix-vector products with $A$, making it broadly applicable. However, the Lanczos method may suffer from slow convergence when $A$ is ill-conditioned, leading to excessive memory requirements for storing the Krylov subspace basis generated by the algorithm. To address this issue, we propose a novel compression strategy for the Krylov subspace basis that significantly reduces memory usage without hindering convergence. This is supported by both numerical experiments and a convergence analysis. Our analysis also accounts for the loss of orthogonality due to round-off errors in the Lanczos process.

math.NA↗