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Bin Gao(bin.gaouclouvain.be) 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 wellknown 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 gradientdescent 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 quasigeodesic 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. Keywords: Riemannian optimization, symplectic Stiefel manifold, quasigeodesic, Cayley transform Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization ) Citation: Tech. report UCLINMA2020.04v1, ICTEAM, UCLouvain, 06/2020 Download: [PDF] Entry Submitted: 06/26/2020 Modify/Update this entry  
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