An adaptive regularized proximal Newton-type methods for composite optimization over the Stiefel manifold

Qinsi Wang () and Wei Hong Yang ()
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Qinsi Wang: Fudan University
Wei Hong Yang: Fudan University

Computational Optimization and Applications, 2024, vol. 89, issue 2, No 5, 419-457

Abstract: Abstract Recently, the proximal Newton-type method and its variants have been generalized to solve composite optimization problems over the Stiefel manifold whose objective function is the summation of a smooth function and a nonsmooth function. In this paper, we propose an adaptive quadratically regularized proximal quasi-Newton method, named ARPQN, to solve this class of problems. Under some mild assumptions, the global convergence, the local linear convergence rate and the iteration complexity of ARPQN are established. Numerical experiments and comparisons with other state-of-the-art methods indicate that ARPQN is very promising. We also propose an adaptive quadratically regularized proximal Newton method, named ARPN. It is shown the ARPN method has a local superlinear convergence rate under certain reasonable assumptions, which demonstrates attractive convergence properties of regularized proximal Newton methods.

Keywords: Proximal Newton-type method; Regularized quasi-Newton method; Stiefel manifold; Linear convergence; Superlinear convergence; 90C30; 90C53; 65K05 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10589-024-00595-3

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