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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.15721 |
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| _version_ | 1866909968306798592 |
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| author | Myhre, Sveinung |
| author_facet | Myhre, Sveinung |
| contents | We propose DiscoverDCP, a data-driven framework that integrates symbolic regression with the rule sets of Disciplined Convex Programming (DCP) to perform system identification. By enforcing that all discovered candidate model expressions adhere to DCP composition rules, we ensure that the output expressions are globally convex by construction, circumventing the computationally intractable process of post-hoc convexity verification. This approach allows for the discovery of convex surrogates that exhibit more relaxed and accurate functional forms than traditional fixed-parameter convex expressions (e.g., quadratic functions). The proposed method produces interpretable, verifiable, and flexible convex models suitable for safety-critical control and optimization tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15721 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiscoverDCP: A Data-Driven Approach for Construction of Disciplined Convex Programs via Symbolic Regression Myhre, Sveinung Machine Learning Artificial Intelligence Systems and Control Optimization and Control We propose DiscoverDCP, a data-driven framework that integrates symbolic regression with the rule sets of Disciplined Convex Programming (DCP) to perform system identification. By enforcing that all discovered candidate model expressions adhere to DCP composition rules, we ensure that the output expressions are globally convex by construction, circumventing the computationally intractable process of post-hoc convexity verification. This approach allows for the discovery of convex surrogates that exhibit more relaxed and accurate functional forms than traditional fixed-parameter convex expressions (e.g., quadratic functions). The proposed method produces interpretable, verifiable, and flexible convex models suitable for safety-critical control and optimization tasks. |
| title | DiscoverDCP: A Data-Driven Approach for Construction of Disciplined Convex Programs via Symbolic Regression |
| topic | Machine Learning Artificial Intelligence Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2512.15721 |