Extraction rates of algorithmically random continuous functionals: Extraction rates of...: D. Cenzer et al.
Saved in:
| Title: | Extraction rates of algorithmically random continuous functionals: Extraction rates of...: D. Cenzer et al. |
|---|---|
| Authors: | Cenzer, Douglas1 (AUTHOR) cenzer@ufl.edu, Fraize, Cameron1 (AUTHOR) cameron.fraize@ufl.edu, Porter, Christopher2 (AUTHOR) christopher.porter@drake.edu |
| Source: | Natural Computing. Mar2025, Vol. 24 Issue 1, p17-28. 12p. |
| Subjects: | Algorithmic randomness, Distribution (Probability theory), Recursion theory, Functionals, Trees |
| Abstract: | In this article, we study the extraction rate, or output/input rate, of continuous functionals on the Cantor space 2 ω , in particular for algorithmically random functionals. It is shown that random functionals have an average extraction rate over all inputs corresponding to the rate of producing a single bit of output, and that this average rate is attained for any sufficiently random input. We also examine functionals computed by discrete distribution generating trees, where we calculate the expected extraction rate and show that this rate is attained for any sufficiently random input. [ABSTRACT FROM AUTHOR] |
| Copyright of Natural Computing is the property of Springer Nature 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.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 183537606 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Extraction rates of algorithmically random continuous functionals: Extraction rates of...: D. Cenzer et al. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cenzer%2C+Douglas%22">Cenzer, Douglas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cenzer@ufl.edu</i><br /><searchLink fieldCode="AR" term="%22Fraize%2C+Cameron%22">Fraize, Cameron</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cameron.fraize@ufl.edu</i><br /><searchLink fieldCode="AR" term="%22Porter%2C+Christopher%22">Porter, Christopher</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> christopher.porter@drake.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Natural+Computing%22">Natural Computing</searchLink>. Mar2025, Vol. 24 Issue 1, p17-28. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithmic+randomness%22">Algorithmic randomness</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Recursion+theory%22">Recursion theory</searchLink><br /><searchLink fieldCode="DE" term="%22Functionals%22">Functionals</searchLink><br /><searchLink fieldCode="DE" term="%22Trees%22">Trees</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this article, we study the extraction rate, or output/input rate, of continuous functionals on the Cantor space 2 ω , in particular for algorithmically random functionals. It is shown that random functionals have an average extraction rate over all inputs corresponding to the rate of producing a single bit of output, and that this average rate is attained for any sufficiently random input. We also examine functionals computed by discrete distribution generating trees, where we calculate the expected extraction rate and show that this rate is attained for any sufficiently random input. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Natural Computing is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=183537606 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11047-024-10000-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 17 Subjects: – SubjectFull: Algorithmic randomness Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Recursion theory Type: general – SubjectFull: Functionals Type: general – SubjectFull: Trees Type: general Titles: – TitleFull: Extraction rates of algorithmically random continuous functionals: Extraction rates of...: D. Cenzer et al. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cenzer, Douglas – PersonEntity: Name: NameFull: Fraize, Cameron – PersonEntity: Name: NameFull: Porter, Christopher IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 15677818 Numbering: – Type: volume Value: 24 – Type: issue Value: 1 Titles: – TitleFull: Natural Computing Type: main |
| ResultId | 1 |
