Randomized methods for computing joint eigenvalues, with applications to multiparameter eigenvalue problems and root finding
<|reference_start|>Randomized methods for computing joint eigenvalues, with applications to multiparameter eigenvalue problems and root finding: It is well known that a family of $n\times n$ commuting matrices can be simultaneously triangularized by a unitary similarity transformation. The diagonal entries of the triangular matrices define the $n$ joint eigenvalues of the family. In this work, we consider the task of numerically computing approximations to such joint eigenvalues for a family of (nearly) commuting matrices. This task arises, for example, in solvers for multiparameter eigenvalue problems and systems of multivariate polynomials, which are our main motivations. We propose and analyze a simple approach that computes eigenvalues as one-sided or two-sided Rayleigh quotients from eigenvectors of a random linear combination of the matrices in the family. We provide some analysis and numerous numerical examples, showing that such randomized approaches can compute semisimple joint eigenvalues accurately and lead to improved performance of existing solvers.<|reference_end|>
@article{he2024randomized,
    title={Randomized methods for computing joint eigenvalues, with applications to
  multiparameter eigenvalue problems and root finding},
    author={Haoze He, Daniel Kressner, Bor Plestenjak},
    journal={Numerical Algorithms (2024)},
    year={2024},
    doi={10.1007/s11075-024-01971-0},
    archivePrefix={arXiv},
    eprint={2409.00500},
    primaryClass={math.NA cs.NA}
}