The Proximal Gradient Method for Composite Optimization Problems on Riemannian Manifolds

In this paper, the composite optimization problem is studied on Riemannian manifolds. To tackle this problem, the proximal gradient method to solve composite optimization problems is proposed on Riemannian manifolds. Under some reasonable conditions, the convergence of the proximal gradient method with the backtracking procedure in the nonconvex case is presented. Furthermore, a sublinear convergence rate and the complexity result of the proximal gradient method for convex case are also established on Riemannian manifolds.

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Publication Details

Journal
Mathematics
Published
2024-08-25
DOI
https://doi.org/10.3390/math12172638
Citations
1
Primary Topic
Numerical methods in inverse problems
Type
article
Field-Weighted Citation Impact
0.89

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article

The Proximal Gradient Method for Composite Optimization Problems on Riemannian Manifolds

Xiaobo Li
1 citations
Mathematics
Numerical methods in inverse problems
0.89
article

The Proximal Gradient Method for Composite Optimization Problems on Riemannian Manifolds

Xiaobo Li
article en
1 citations

Abstract

In this paper, the composite optimization problem is studied on Riemannian manifolds. To tackle this problem, the proximal gradient method to solve composite optimization problems is proposed on Riemannian manifolds. Under some reasonable conditions, the convergence of the proximal gradient method with the backtracking procedure in the nonconvex case is presented. Furthermore, a sublinear convergence rate and the complexity result of the proximal gradient method for convex case are also established on Riemannian manifolds.

MathematicsVol. 12(17)
Civil Aviation Flight University of China (CN)
National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province, Natural Science Foundation for Young Scientists of Shanxi Province, Young Scientists Fund, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 30%
Numerical methods in inverse problems
0.89
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