Riemannian Optimization on the Symplectic Stiefel Manifold

The symplectic Stiefel manifold, denoted by Sp(2p,2n), is the set of linear symplectic maps between the standard symplectic spaces ℝ2p and ℝ2n. When p=n, it reduces to the well-known set of 2n× 2n symplectic matrices. Optimization problems on 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 Sp(2p,2n), where the notion of gradient stems from a Riemannian metric. We consider a novel Riemannian metric on 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.