Switching Network System Identification via Convex Optimizations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915581769285632 |
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| author | Iwasaki, Kaito Bloch, Anthony Ghaffari, Maani |
| author_facet | Iwasaki, Kaito Bloch, Anthony Ghaffari, Maani |
| contents | This paper introduces a convex optimization framework for identifying switched network systems, in which both the node dynamics and the underlying graph topology switch between a finite number of configurations. Building on our recent convex identification method for general switching systems, we extend the formulation to structured network systems where each mode corresponds to a distinct adjacency matrix. We show that both the continuous node dynamics and binary network topologies can be identified from sampled state-velocity data by solving a sequence of convex programs. The proposed framework provides a unified and scalable way to recover piecewise network structures from data without a prior knowledge of mode labels at each state. Numerical results on diffusively coupled oscillators demonstrate accurate recovery of both mode dynamics and switching graphs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_23721 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Switching Network System Identification via Convex Optimizations Iwasaki, Kaito Bloch, Anthony Ghaffari, Maani Optimization and Control Systems and Control 93B30 (Primary), 93C30, 90C25 This paper introduces a convex optimization framework for identifying switched network systems, in which both the node dynamics and the underlying graph topology switch between a finite number of configurations. Building on our recent convex identification method for general switching systems, we extend the formulation to structured network systems where each mode corresponds to a distinct adjacency matrix. We show that both the continuous node dynamics and binary network topologies can be identified from sampled state-velocity data by solving a sequence of convex programs. The proposed framework provides a unified and scalable way to recover piecewise network structures from data without a prior knowledge of mode labels at each state. Numerical results on diffusively coupled oscillators demonstrate accurate recovery of both mode dynamics and switching graphs. |
| title | Switching Network System Identification via Convex Optimizations |
| topic | Optimization and Control Systems and Control 93B30 (Primary), 93C30, 90C25 |
| url | https://arxiv.org/abs/2510.23721 |