Towards a Systems Theory of Algorithms
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910464621936640 |
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| author | Dörfler, Florian He, Zhiyu Belgioioso, Giuseppe Bolognani, Saverio Lygeros, John Muehlebach, Michael |
| author_facet | Dörfler, Florian He, Zhiyu Belgioioso, Giuseppe Bolognani, Saverio Lygeros, John Muehlebach, Michael |
| contents | Traditionally, numerical algorithms are seen as isolated pieces of code confined to an {\em in silico} existence. However, this perspective is not appropriate for many modern computational approaches in control, learning, or optimization, wherein {\em in vivo} algorithms interact with their environment. Examples of such {\em open algorithms} include various real-time optimization-based control strategies, reinforcement learning, decision-making architectures, online optimization, and many more. Further, even {\em closed} algorithms in learning or optimization are increasingly abstracted in block diagrams with interacting dynamic modules and pipelines. In this opinion paper, we state our vision on a to-be-cultivated {\em systems theory of algorithms} and argue in favor of viewing algorithms as open dynamical systems interacting with other algorithms, physical systems, humans, or databases. Remarkably, the manifold tools developed under the umbrella of systems theory are well suited for addressing a range of challenges in the algorithmic domain. We survey various instances where the principles of algorithmic systems theory are being developed and outline pertinent modeling, analysis, and design challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14029 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards a Systems Theory of Algorithms Dörfler, Florian He, Zhiyu Belgioioso, Giuseppe Bolognani, Saverio Lygeros, John Muehlebach, Michael Optimization and Control Machine Learning Systems and Control Traditionally, numerical algorithms are seen as isolated pieces of code confined to an {\em in silico} existence. However, this perspective is not appropriate for many modern computational approaches in control, learning, or optimization, wherein {\em in vivo} algorithms interact with their environment. Examples of such {\em open algorithms} include various real-time optimization-based control strategies, reinforcement learning, decision-making architectures, online optimization, and many more. Further, even {\em closed} algorithms in learning or optimization are increasingly abstracted in block diagrams with interacting dynamic modules and pipelines. In this opinion paper, we state our vision on a to-be-cultivated {\em systems theory of algorithms} and argue in favor of viewing algorithms as open dynamical systems interacting with other algorithms, physical systems, humans, or databases. Remarkably, the manifold tools developed under the umbrella of systems theory are well suited for addressing a range of challenges in the algorithmic domain. We survey various instances where the principles of algorithmic systems theory are being developed and outline pertinent modeling, analysis, and design challenges. |
| title | Towards a Systems Theory of Algorithms |
| topic | Optimization and Control Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2401.14029 |