A Riemannian Inexact Newton-CG Method for Nonnegative Inverse Eigenvalue Problems: Nonsymmetric Case
Provides a novel algorithmic approach for constructing nonnegative matrices with prescribed spectra, a problem relevant to matrix analysis and applications.
The paper solves the nonnegative inverse eigenvalue problem by reformulating it as a constrained nonlinear matrix equation over matrix manifolds and proposes a Riemannian inexact Newton-CG method with global and quadratic convergence. Numerical experiments demonstrate efficiency.
This paper is concerned with the nonnegative inverse eigenvalue problem of finding a nonnegative matrix such that its spectrum is the prescribed self-conjugate set of complex numbers. We first reformulate the nonnegative inverse eigenvalue problem as an under-determined constrained nonlinear matrix equation over several matrix manifolds. Then we propose a Riemannian inexact Newton-CG method for solving the nonlinear matrix equation. The global and quadratic convergence of the proposed method is established under some mild conditions. We also extend the proposed method to the case of prescribed entries. Finally, numerical experiments are reported to illustrate the efficiency of the proposed method.