Thời gian: 9h30, Thứ 3, ngày 3/11/2020

Địa điểm: Phòng 302, Nhà A5, Viện Toán học

Tóm tắt: The symplectic Stiefel manifold, denoted by S_p(2p,2n, is the set of linear symplectic maps between the standard symplectic spaces R^2p and R^{2n}. When p=n, it reduces to the well-known set of 2n×2nsymplectic matrices. Optimization problems on S_p(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 S_p(2p,2n), where the notion of gradient stems from a Riemannian metric. We consider a novel Riemannian metric on S_p(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.