2018/08/12 by Javier Pérez, Perez, Javier
Computer Science · Mathematics · #15A18 #15A22 #65F15 #Advanced Optimization Algorithms Research #FOS: Mathematics #Matrix Theory and Algorithms #Numerical Analysis (math.NA) #Numerical methods for differential equations
paper · pdf · doi:10.48550/arxiv.1808.04012
openalex publication_date 2018/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The standard approach for finding eigenvalues and eigenvectors of matrix polynomials starts by embedding the coefficients of the polynomial into a matrix pencil, known as linearization. Building on the pioneering work of Nakatsukasa and Tisseur, we present error bounds for the computed eigenvectors of matrix polynomials. Our error bounds are applicable to any linearization satisfying two properties. First, eigenvectors of the original matrix polynomial can be recovered from those of the linearization without any arithmetic computation. Second, the linearization presents one-sided factorizations, which relate the residual for the linearization with the residual for the polynomial. Linearizations satisfying these two properties include the family of block Kronecker linearizations. The error bounds imply that an eigenvector has been computed accurately when the residual norm is small, provided that the computed associated eigenvalue is well-separated from the rest of the spectrum of the linearization. The theory is illustrated by numerical examples.