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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.00325 |
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| _version_ | 1866914734890024960 |
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| author | He, Huan Tang, Ziyuan Zhao, Shifan Saad, Yousef Xi, Yuanzhe |
| author_facet | He, Huan Tang, Ziyuan Zhao, Shifan Saad, Yousef Xi, Yuanzhe |
| contents | This paper develops a new class of nonlinear acceleration algorithms based on extending conjugate residual-type procedures from linear to nonlinear equations. The main algorithm has strong similarities with Anderson acceleration as well as with inexact Newton methods - depending on which variant is implemented. We prove theoretically and verify experimentally, on a variety of problems from simulation experiments to deep learning applications, that our method is a powerful accelerated iterative algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_00325 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NLTGCR: A class of Nonlinear Acceleration Procedures based on Conjugate Residuals He, Huan Tang, Ziyuan Zhao, Shifan Saad, Yousef Xi, Yuanzhe Numerical Analysis This paper develops a new class of nonlinear acceleration algorithms based on extending conjugate residual-type procedures from linear to nonlinear equations. The main algorithm has strong similarities with Anderson acceleration as well as with inexact Newton methods - depending on which variant is implemented. We prove theoretically and verify experimentally, on a variety of problems from simulation experiments to deep learning applications, that our method is a powerful accelerated iterative algorithm. |
| title | NLTGCR: A class of Nonlinear Acceleration Procedures based on Conjugate Residuals |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2306.00325 |