Expansions of the Characteristic Polynomial of a Perturbed Positive Semidefinite Matrix and Convergence Analysis of Alternating Projections
Hiroyuki Ochiai, Yoshiyuki Sekiguchi, Hayato Waki
TL;DR
This work studies convergence of alternating projections between the positive semidefinite cone and a line E = {A + tB} under the singleton intersection condition E ∩ S_+^n = {A}. By expanding the characteristic polynomial of the perturbed matrix A + tB via adjugates and compound matrices and applying the Newton diagram/polytope framework, the authors bound the leading eigenvalue terms that govern convergence. They prove that the iteration converges with rate ∥U_k − A∥ = O(k^{-1/2}) independent of singularity degree, and they provide a sufficient rank-condition for linear convergence, with a discussion of tightness in singularity-degree-2 cases. The approach bypasses conventional error-bound arguments and directly analyzes the defining projection equations through eigenvalue dynamics, offering a precise link between perturbation theory, Newton-type diagrams, and projection methods in semidefinite settings.
Abstract
We observe that the characteristic polynomial of a linearly perturbed semidefinite matrix can be used to give the convergence rate of alternating projections for the positive semidefinite cone and a line. As a consequence, we show that such alternating projections converge at $O(k^{-1/2})$, independently of the singularity degree. A sufficient condition for the linear convergence is also obtained. Our method directly analyzes the defining equation for an alternating projection sequence and does not use error bounds.
