Scaled Companion Linearizations for Coefficientwise Backward Stability

Abstract

A central goal in solving a polynomial eigenvalue problem is to compute eigenpairs that are exact for a nearby polynomial, with each coefficient perturbation bounded by a modest multiple of unit roundoff times that coefficient's norm. A backward stable eigensolver for a linearization does not automatically provide this coefficientwise relative guarantee. Scaling can improve accuracy, but a single scaling may not work across a widely spread spectrum. We construct a family of scaled companion linearizations that covers the nonzero complex plane. Each linearization is paired with a region and an eigenvector recovery rule for which the backward-error amplification factor is bounded at exact pencil eigenvectors, independently of coefficient norm disparities. Tropical roots guide the construction, and their multiplicities reduce the number of required solves. These results provide a basis for coefficientwise relative backward stability across the spectrum.