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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.14021 |
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| _version_ | 1866912128597753856 |
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| author | Zhang, Wanyu Gou, Ruili Liu, Huikang Wang, Zhiguo Ye, Yinyu |
| author_facet | Zhang, Wanyu Gou, Ruili Liu, Huikang Wang, Zhiguo Ye, Yinyu |
| contents | The determination of molecular orientations is crucial for the three-dimensional reconstruction of Cryo-EM images. Traditionally addressed using the common-line method, this challenge is reformulated as a self-consistency error minimization problem constrained to rotation groups. In this paper, we consider the least-squared deviation (LUD) formulation and employ a Riemannian subgradient method to effectively solve the orientation determination problem. To enhance computational efficiency, a block stochastic version of the method is proposed, and its convergence properties are rigorously established. Extensive numerical evaluations reveal that our method not only achieves accuracy comparable to that of state-of-the-art methods but also delivers an average 20-fold speedup. Additionally, we implement a modified formulation and algorithm specifically designed to address scenarios characterized by very low SNR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14021 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Orientation Determination of Cryo-EM Images Using Block Stochastic Riemannian Subgradient Methods Zhang, Wanyu Gou, Ruili Liu, Huikang Wang, Zhiguo Ye, Yinyu Optimization and Control The determination of molecular orientations is crucial for the three-dimensional reconstruction of Cryo-EM images. Traditionally addressed using the common-line method, this challenge is reformulated as a self-consistency error minimization problem constrained to rotation groups. In this paper, we consider the least-squared deviation (LUD) formulation and employ a Riemannian subgradient method to effectively solve the orientation determination problem. To enhance computational efficiency, a block stochastic version of the method is proposed, and its convergence properties are rigorously established. Extensive numerical evaluations reveal that our method not only achieves accuracy comparable to that of state-of-the-art methods but also delivers an average 20-fold speedup. Additionally, we implement a modified formulation and algorithm specifically designed to address scenarios characterized by very low SNR. |
| title | Orientation Determination of Cryo-EM Images Using Block Stochastic Riemannian Subgradient Methods |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2411.14021 |