Control-Based Online Distributed Optimization
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915737340215296 |
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| author | van Weerelt, Wouter J. A. Bastianello, Nicola |
| author_facet | van Weerelt, Wouter J. A. Bastianello, Nicola |
| contents | In this paper we design a novel class of online distributed optimization algorithms leveraging control theoretical techniques. We start by focusing on quadratic costs, and assuming to know an internal model of their variation. In this set-up, we formulate the algorithm design as a robust control problem, showing that it yields a fully distributed algorithm. We also provide a distributed routine to acquire the internal model. We show that the algorithm converges exactly to the sequence of optimal solutions. We empirically evaluate the performance of the algorithm for different choices of parameters. Additionally, we evaluate the performance of the algorithm for quadratic problems with inexact internal model and non-quadratic problems, and show that it outperforms alternative algorithms in both scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15498 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Control-Based Online Distributed Optimization van Weerelt, Wouter J. A. Bastianello, Nicola Optimization and Control Systems and Control In this paper we design a novel class of online distributed optimization algorithms leveraging control theoretical techniques. We start by focusing on quadratic costs, and assuming to know an internal model of their variation. In this set-up, we formulate the algorithm design as a robust control problem, showing that it yields a fully distributed algorithm. We also provide a distributed routine to acquire the internal model. We show that the algorithm converges exactly to the sequence of optimal solutions. We empirically evaluate the performance of the algorithm for different choices of parameters. Additionally, we evaluate the performance of the algorithm for quadratic problems with inexact internal model and non-quadratic problems, and show that it outperforms alternative algorithms in both scenarios. |
| title | Control-Based Online Distributed Optimization |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2508.15498 |