Riemannian Optimization on the Symplectic Stiefel Manifold
arXiv:2006.15226 · doi:10.1137/20M1348522
Abstract
The symplectic Stiefel manifold, denoted by , is the set of linear symplectic maps between the standard symplectic spaces and . When , it reduces to the well-known set of symplectic matrices. Optimization problems on 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 , where the notion of gradient stems from a Riemannian metric. We consider a novel Riemannian metric on 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.
arXiv comment28 pages, 7 figures, 5 tables
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Cited by in corpus (9)
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- Symplectic eigenvalues of positive-semidefinite matrices and the trace minimization theorem
- Constraint optimization and quantum control landscapes
- Geometry of the symplectic Stiefel manifold endowed with the Euclidean metric
- The real symplectic Stiefel and Grassmann manifolds: metrics, geodesics and applications
- A Riemannian gradient descent method for optimization on the indefinite Stiefel manifold
- A generalized canonical metric for optimization on the indefinite Stiefel manifold