Convergence Analysis of Gradient Algorithms on Riemannian Manifolds without Curvature Constraints and Application to Riemannian Mass-Reference-Cited by-同舟云学术

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Convergence Analysis of Gradient Algorithms on Riemannian Manifolds without Curvature Constraints and Application to Riemannian Mass

Published:2021-01 Issue:1 Volume:31 Page:172-199
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Wang Jinhua,Wang Xiangmei,Li Chong,Yao Jen-Chih

Funder

Ministry of Science and Technology, Taiwan

National Natural Science Foundation of China

Natural Science Foundation of Guizhou Province

Natural Science Foundation of Zhejiang Province

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software

Link

https://epubs.siam.org/doi/pdf/10.1137/19M1289285

Reference36 articles.

1. Trust-Region Methods on Riemannian Manifolds

2. Newton's method on Riemannian manifolds and a geometric model for the human spine

3. Riemannian $L^{p}$ center of mass: Existence, uniqueness, and convexity

4. On the Convergence of Gradient Descent for Finding the Riemannian Center of Mass

5. Stochastic algorithms for computing means of probability measures

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1. Constraint qualifications and optimality conditions for nonsmooth multiobjective mathematical programming problems with vanishing constraints on Hadamard manifolds via convexificators;Journal of Mathematical Analysis and Applications;2024-09

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4. On the Relationship Between the Kurdyka–Łojasiewicz Property and Error Bounds on Hadamard Manifolds;Journal of Optimization Theory and Applications;2024-02-11

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