The symplectic Stiefel manifold, denoted by ${Sp}(2p,2n)$, 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 ${Sp}(2p,2n)$ 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.


  1. Riemannian optimization
  2. symplectic Stiefel manifold
  3. quasi-geodesic
  4. Cayley transform

MSC codes

  1. 65K05
  2. 70G45
  3. 90C48

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Information & Authors


Published In

cover image SIAM Journal on Optimization
SIAM Journal on Optimization
Pages: 1546 - 1575
ISSN (online): 1095-7189


Submitted: 26 June 2020
Accepted: 23 March 2021
Published online: 22 June 2021


  1. Riemannian optimization
  2. symplectic Stiefel manifold
  3. quasi-geodesic
  4. Cayley transform

MSC codes

  1. 65K05
  2. 70G45
  3. 90C48



Funding Information

Fonds De La Recherche Scientifique - FNRS https://doi.org/10.13039/501100002661

Funding Information

Fonds Wetenschappelijk Onderzoek https://doi.org/10.13039/501100003130 : 30468160

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