Solving a sequence of eigenvalue problems using snapshot and row subsampling

A sequence of eigenvalue problems requires solving $A_i{\vec{x}}_i={\lambda }_i{\vec{x}}_i$ where $i\in \{1,\dots ,N\}$, $N\gg 1$ and (usually) ${\lambda }_i$ is the largest or smallest eigenvalue of $A_i$. Often, the matrices $A_i$ come from a discretization of a smooth map depending on a parameter $p\to A(p)$, that is $A_i=A(p_i)$, so the matrices $A_i$ change gradually along the sequence. Under this latter assumption, we introduce an efficient algorithm for solving sequences of eigenvalue problems. Our algorithm combines the ideas of reduced order model and row subsampling, as done in [E. Jones and Y. Nakatsukasa, arXiv, 2025] for linear systems. In addition, in our approach, the same set of rows is guaranteed to work well independently of the value of the parameter $p$ or the matrix $A(p)$ as long as the snapshot matrix is unchanged and given that the row subset was chosen well at the beginning. Furthermore, we implement an update strategy of the snapshot matrix. Along these results, we present theoretical analysis of the snapshot matrix for eigenvalue problems, showing an exponential decay of the singular values, and we give bounds on the error on eigenvectors for unseen value of the parameter. This analysis generalizes that in [M. Guido, D. Kressner, and P. Ricci, J. Sci. Comput., 2024] and shows that, regardless of the distribution of the snapshot points, the convergence is always (asymptotically) exponential in the number of snapshot points if the function $A(p)$ admits an analytical extension. We further generalize our algorithm for computing largest or smallest singular values and relative singular vectors and to generalized eigenvalue problems. We conclude with numerical results showing the efficiency of our approach.