A stochastic column-block gradient descent method for solving nonlinear systems of equations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866908455588069376 |
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| author | Jiang, Naiyu Bao, Wendi Xing, Lili Li, Weiguo |
| author_facet | Jiang, Naiyu Bao, Wendi Xing, Lili Li, Weiguo |
| contents | In this paper, we propose a new stochastic column-block gradient descent method for solving nonlinear systems of equations. It has a descent direction and holds an approximately optimal step size obtained through an optimization problem. We provide a thorough convergence analysis, and derive an upper bound for the convergence rate of the new method. Numerical experiments demonstrate that the proposed method outperforms the existing ones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13855 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A stochastic column-block gradient descent method for solving nonlinear systems of equations Jiang, Naiyu Bao, Wendi Xing, Lili Li, Weiguo Numerical Analysis In this paper, we propose a new stochastic column-block gradient descent method for solving nonlinear systems of equations. It has a descent direction and holds an approximately optimal step size obtained through an optimization problem. We provide a thorough convergence analysis, and derive an upper bound for the convergence rate of the new method. Numerical experiments demonstrate that the proposed method outperforms the existing ones. |
| title | A stochastic column-block gradient descent method for solving nonlinear systems of equations |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2507.13855 |