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COINDUCTIVE STREAMS IN MONOIDAL CATEGORIES.

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Τίτλος: COINDUCTIVE STREAMS IN MONOIDAL CATEGORIES.
Συγγραφείς: DI LAVORE, ELENA, DE FELICE, GIOVANNI, ROMÁN, MARIO
Πηγή: Logical Methods in Computer Science (LMCS); 2025, Vol. 21 Issue 3, p1-52, 52p
Θεματικοί όροι: Monoidal categories, Stochastic processes, Data flow computing, Flowgraphs, Stream measurements
Περίληψη: We introduce monoidal streams. Monoidal streams are a generalization of causal stream functions, which can be defined in cartesian monoidal categories, to arbitrary symmetric monoidal categories. In the same way that streams provide semantics to dataflow programming with pure functions, monoidal streams provide semantics to dataflow programming with arbitrary processes. Monoidal streams also form a feedback monoidal category. In the same way that the coinductive stream calculus reasons about signal flow graphs, coinductive string diagrams reason about feedback monoidal categories, with semantics in monoidal streams. We highlight the probabilistic case, characterizing probabilistic streams as causal stochastic processes. [ABSTRACT FROM AUTHOR]
Copyright of Logical Methods in Computer Science (LMCS) is the property of Logical Methods in Computer Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: COINDUCTIVE STREAMS IN MONOIDAL CATEGORIES.
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  Data: <searchLink fieldCode="AR" term="%22DI+LAVORE%2C+ELENA%22">DI LAVORE, ELENA</searchLink><br /><searchLink fieldCode="AR" term="%22DE+FELICE%2C+GIOVANNI%22">DE FELICE, GIOVANNI</searchLink><br /><searchLink fieldCode="AR" term="%22ROMÁN%2C+MARIO%22">ROMÁN, MARIO</searchLink>
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  Data: Logical Methods in Computer Science (LMCS); 2025, Vol. 21 Issue 3, p1-52, 52p
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  Data: <searchLink fieldCode="DE" term="%22Monoidal+categories%22">Monoidal categories</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Data+flow+computing%22">Data flow computing</searchLink><br /><searchLink fieldCode="DE" term="%22Flowgraphs%22">Flowgraphs</searchLink><br /><searchLink fieldCode="DE" term="%22Stream+measurements%22">Stream measurements</searchLink>
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  Label: Abstract
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  Data: We introduce monoidal streams. Monoidal streams are a generalization of causal stream functions, which can be defined in cartesian monoidal categories, to arbitrary symmetric monoidal categories. In the same way that streams provide semantics to dataflow programming with pure functions, monoidal streams provide semantics to dataflow programming with arbitrary processes. Monoidal streams also form a feedback monoidal category. In the same way that the coinductive stream calculus reasons about signal flow graphs, coinductive string diagrams reason about feedback monoidal categories, with semantics in monoidal streams. We highlight the probabilistic case, characterizing probabilistic streams as causal stochastic processes. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Logical Methods in Computer Science (LMCS) is the property of Logical Methods in Computer Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.46298/LMCS-21(3:18)2025
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      – Code: eng
        Text: English
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      – SubjectFull: Monoidal categories
        Type: general
      – SubjectFull: Stochastic processes
        Type: general
      – SubjectFull: Data flow computing
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      – SubjectFull: Flowgraphs
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      – SubjectFull: Stream measurements
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      – TitleFull: COINDUCTIVE STREAMS IN MONOIDAL CATEGORIES.
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              Text: 2025
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