Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910141698277376 |
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| author | Wu, Hao |
| author_facet | Wu, Hao |
| contents | We establish convergence theorems for Riemannian stochastic gradient descents in which the underlying probability spaces vary from iteration to iteration. As applications, we deduce convergence results for Riemannian stochastic gradient descents with varying batch sizes and unbiased batch forming schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06350 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Wu, Hao Optimization and Control 41A60, 53Z50, 62L20, 68T05 We establish convergence theorems for Riemannian stochastic gradient descents in which the underlying probability spaces vary from iteration to iteration. As applications, we deduce convergence results for Riemannian stochastic gradient descents with varying batch sizes and unbiased batch forming schemes. |
| title | Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming |
| topic | Optimization and Control 41A60, 53Z50, 62L20, 68T05 |
| url | https://arxiv.org/abs/2604.06350 |