COMPUTING SYMPLECTIC EIGENPAIRS OF SYMMETRIC POSITIVE-DEFINITE MATRICES VIA TRACE MINIMIZATION AND RIEMANNIAN OPTIMIZATION

We address the problem of computing the smallest symplectic eigenvalues and the corresponding eigenvectors of symmetric positive-definite matrices in the sense of Williamson's theorem. It is formulated as minimizing a trace cost function over the symplectic Stiefel manifold. We first investigate various theoretical aspects of this optimization problem such as characterizing the sets of critical points, saddle points, and global minimizers as well as proving that nonglobal local minimizers do not exist. Based on our recent results on constructing Riemannian structures on the symplectic Stiefel manifold and the associated optimization algorithms, we then propose a numerical procedure for computing symplectic eigenpairs in the framework of Riemannian optimization. Moreover, a connection of the sought solution with the eigenvalues of a special class of Hamiltonian matrices is discussed. Numerical examples are presented.

Automated summary

This paper addresses the problem of computing the smallest symplectic eigenvalues and corresponding eigenvectors of symmetric positive-definite matrices. It formulates the problem as minimizing a trace cost function over the symplectic Stiefel manifold. The authors propose a numerical procedure for computing the symplectic eigenpairs and discuss the connection of the sought solution with the eigenvalues of a special class of Hamiltonian matrices.