Riemannian Optimization on the Symplectic Stiefel Manifold

TL;DR

This paper introduces gradient-descent optimization methods on the symplectic Stiefel manifold using a novel Riemannian metric, with proven convergence and demonstrated efficiency in applications like physics and linear algebra.

Contribution

It proposes new Riemannian gradient-descent algorithms on the symplectic Stiefel manifold utilizing quasi-geodesic and Cayley transform strategies, with convergence analysis.

Findings

Algorithms converge globally to critical points.

Numerical experiments show high efficiency.

Methods are applicable to physics and linear algebra problems.

Abstract

The symplectic Stiefel manifold, denoted by $\mathrm{Sp}(2p,2n)$, is the set of linear symplectic maps between the standard symplectic spaces $\mathbb{R}^{2p}$ and $\mathbb{R}^{2n}$. When $p=n$, it reduces to the well-known set of $2n\times 2n$ symplectic matrices. Optimization problems on $\mathrm{Sp}(2p,2n)$ find applications in various areas, such as optics, quantum physics, numerical linear algebra and model order reduction of dynamical systems. The purpose of this paper is to propose and analyze gradient-descent methods on $\mathrm{Sp}(2p,2n)$, where the notion of gradient stems from a Riemannian metric. We consider a novel Riemannian metric on $\mathrm{Sp}(2p,2n)$ akin to the canonical metric of the (standard) Stiefel manifold. In order to perform a feasible step along the antigradient, we develop two types of search strategies: one is based on quasi-geodesic curves, and the other one on the symplectic Cayley transform. The resulting optimization algorithms are proved to converge globally to critical points of the objective function. Numerical experiments illustrate the efficiency of the proposed methods.