Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold
نویسندگان
چکیده
Optimization problems on the generalized Stiefel manifold (and products of it) are prevalent across science and engineering. For example, in computational they arise symmetric (generalized) eigenvalue problems, nonlinear electronic structures computations, to name a few problems. In statistics machine learning, arise, for various dimensionality reduction techniques such as canonical correlation analysis. deep regularization improved stability can be obtained by constraining some layers have parameter matrices that belong manifold. Solving approached via tools Riemannian optimization. However, using standard geometric components has two possible shortcomings: computing too expensive convergence rather slow certain cases. Both shortcomings addressed technique called preconditioning, which amounts derived preconditioner defines metric constraint this paper we develop required perform optimization equipped with non-standard metric, illustrate theoretically numerically use those effect preconditioning solving
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ژورنال
عنوان ژورنال: Journal of Computational and Applied Mathematics
سال: 2023
ISSN: ['0377-0427', '1879-1778', '0771-050X']
DOI: https://doi.org/10.1016/j.cam.2022.114953