Brockett cost function for symplectic eigenvalues

TL;DR

This paper introduces a Brockett cost function for symplectic eigenvalues, establishing theoretical connections and proving that critical points correspond to symplectic eigenvectors, thus providing new insights into symplectic eigenvalue computation.

Contribution

It proposes a novel Brockett cost function for symplectic eigenvalues and proves key properties linking critical points to symplectic eigenvectors.

Findings

Critical points are symplectic eigenvectors

Trace minimization theorem can be derived from the results

New theoretical insights into symplectic eigenvalue computation

Abstract

The symplectic eigenvalues and corresponding eigenvectors of symmetric positive-definite matrices in the sense of Williamson's theorem can be computed via minimization of a trace cost function under the symplecticity constraint. The optimal solution to this problem only offers a symplectic basis for a symplectic eigenspace corresponding to the sought symplectic eigenvalues. In this paper, we introduce a Brockett cost function and investigate the connection between its properties and the symplectic eigenvalues and eigenvectors, specifically prove that any critical point consists of symplectic eigenvectors. Surprisingly, the trace minimization theorem for the symplectic eigenvalues can be deduced from our results.