Toward a Systems Theory of Algorithms

Traditionally, numerical algorithms are seen as isolated pieces of code confined to an in silico existence. However, this perspective is inappropriate for many modern computational approaches in control, learning, or optimization, wherein in vivo algorithms interact with their environment. Examples...

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Vydané v:	IEEE control systems letters Ročník 8; s. 1198 - 1210
Hlavní autori:	Dorfler, Florian, He, Zhiyu, Belgioioso, Giuseppe, Bolognani, Saverio, Lygeros, John, Muehlebach, Michael
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2024
Predmet:	Codes decision-making architectures Dynamical systems Heuristic algorithms Machine learning algorithms online optimization and learning Optimization Pipelines Real-time systems Systems theory of algorithms
ISSN:	2475-1456, 2475-1456
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Abstract	Traditionally, numerical algorithms are seen as isolated pieces of code confined to an in silico existence. However, this perspective is inappropriate for many modern computational approaches in control, learning, or optimization, wherein in vivo algorithms interact with their environment. Examples of such open algorithms include various real-time optimization-based control strategies, reinforcement learning, decision-making architectures, online optimization, and many more. Further, even closed algorithms in learning or optimization are increasingly abstracted in block diagrams with interacting dynamic modules and pipelines. In this opinion letter, we state our vision on a to-be-cultivated 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 rangeofchallenges 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.
AbstractList	Traditionally, numerical algorithms are seen as isolated pieces of code confined to an in silico existence. However, this perspective is inappropriate for many modern computational approaches in control, learning, or optimization, wherein in vivo algorithms interact with their environment. Examples of such open algorithms include various real-time optimization-based control strategies, reinforcement learning, decision-making architectures, online optimization, and many more. Further, even closed algorithms in learning or optimization are increasingly abstracted in block diagrams with interacting dynamic modules and pipelines. In this opinion letter, we state our vision on a to-be-cultivated 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 rangeofchallenges 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.
Author	Dorfler, Florian Lygeros, John Bolognani, Saverio He, Zhiyu Muehlebach, Michael Belgioioso, Giuseppe
Author_xml	– sequence: 1 givenname: Florian orcidid: 0000-0002-9649-5305 surname: Dorfler fullname: Dorfler, Florian email: dorfler@ethz.ch organization: Automatic Control Laboratory, ETH Zürich, Zürich, Switzerland – sequence: 2 givenname: Zhiyu orcidid: 0000-0001-9413-5333 surname: He fullname: He, Zhiyu email: zhiyhe@ethz.ch organization: Automatic Control Laboratory, ETH Zürich, Zürich, Switzerland – sequence: 3 givenname: Giuseppe orcidid: 0000-0002-8892-1466 surname: Belgioioso fullname: Belgioioso, Giuseppe email: gbelgioioso@ethz.ch organization: Automatic Control Laboratory, ETH Zürich, Zürich, Switzerland – sequence: 4 givenname: Saverio orcidid: 0000-0002-7935-1385 surname: Bolognani fullname: Bolognani, Saverio email: bsaverio@ethz.ch organization: Automatic Control Laboratory, ETH Zürich, Zürich, Switzerland – sequence: 5 givenname: John orcidid: 0000-0002-6159-1962 surname: Lygeros fullname: Lygeros, John email: jlygeros@ethz.ch organization: Automatic Control Laboratory, ETH Zürich, Zürich, Switzerland – sequence: 6 givenname: Michael orcidid: 0000-0002-7764-3069 surname: Muehlebach fullname: Muehlebach, Michael email: michael.muehlebach@tuebingen.mpg.de organization: Max Planck Institute for Intelligent Systems, Tübingen, Germany
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Snippet	Traditionally, numerical algorithms are seen as isolated pieces of code confined to an in silico existence. However, this perspective is inappropriate for many...
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SubjectTerms	Codes decision-making architectures Dynamical systems Heuristic algorithms Machine learning algorithms online optimization and learning Optimization Pipelines Real-time systems Systems theory of algorithms
Title	Toward a Systems Theory of Algorithms
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