Online optimisation for dynamic electrical impedance tomography
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909539302899712 |
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| author | Dizon, Neil Jauhiainen, Jyrki Valkonen, Tuomo |
| author_facet | Dizon, Neil Jauhiainen, Jyrki Valkonen, Tuomo |
| contents | Online optimisation studies the convergence of optimisation methods as the data embedded in the problem changes. Based on this idea, we propose a primal dual online method for nonlinear time-discrete inverse problems. We analyse the method through regret theory and demonstrate its performance in real-time monitoring of moving bodies in a fluid with Electrical Impedance Tomography (EIT). To do so, we also prove the second-order differentiability of the Complete Electrode Model (CEM) solution operator on $L^\infty$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12944 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online optimisation for dynamic electrical impedance tomography Dizon, Neil Jauhiainen, Jyrki Valkonen, Tuomo Optimization and Control Computer Vision and Pattern Recognition Online optimisation studies the convergence of optimisation methods as the data embedded in the problem changes. Based on this idea, we propose a primal dual online method for nonlinear time-discrete inverse problems. We analyse the method through regret theory and demonstrate its performance in real-time monitoring of moving bodies in a fluid with Electrical Impedance Tomography (EIT). To do so, we also prove the second-order differentiability of the Complete Electrode Model (CEM) solution operator on $L^\infty$. |
| title | Online optimisation for dynamic electrical impedance tomography |
| topic | Optimization and Control Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.12944 |