Academic Journal
Parallel and Distributed Methods for Constrained Nonconvex Optimization-Part II: Applications in Communications and Machine Learning.
| Τίτλος: | Parallel and Distributed Methods for Constrained Nonconvex Optimization-Part II: Applications in Communications and Machine Learning. |
|---|---|
| Συγγραφείς: | Scutari, Gesualdo, Facchinei, Francisco, Lampariello, Lorenzo, Sardellitti, Stefania, Song, Peiran |
| Πηγή: | IEEE Transactions on Signal Processing; Apr2017, Vol. 65 Issue 8, p1945-1960, 16p |
| Θεματικοί όροι: | Distributed computing, Distributed computing management, Distributed computing software, Distributed algorithms, MIMO systems |
| Περίληψη: | In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the (nontrivial) application of the framework to the following relevant large-scale problems ranging from communications to machine learning: 1) (generalizations of) the rate profile maximization in MIMO interference broadcast networks; 2) the max–min fair multicast multigroup beamforming problem in a multicell environment; and 3) a general nonconvex constrained bi-criteria formulation for $k$ -sparse variable selection in statistical learning; the two criteria are a nonconvex loss objective function, measuring the fitness of the model to data, and the latter is a nonconvex sparsity-inducing constraint in the general form of difference-of-convex (DC) functions, which allows to accomodate in a unified fashion convex and nonconvex surrogates of the $\ell _0$ function. The proposed algorithms outperform current state-of-the-art schemes for 1)–3) both theoretically and numerically. For instance, they are the first distributed schemes for the class of problems 1) and 2); and they also lead to subproblems enjoying closed form solutions. [ABSTRACT FROM PUBLISHER] |
| Copyright of IEEE Transactions on Signal Processing is the property of IEEE 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 124146018 RelevancyScore: 853 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 853.472534179688 |
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| Items | – Name: Title Label: Title Group: Ti Data: Parallel and Distributed Methods for Constrained Nonconvex Optimization-Part II: Applications in Communications and Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Scutari%2C+Gesualdo%22">Scutari, Gesualdo</searchLink><br /><searchLink fieldCode="AR" term="%22Facchinei%2C+Francisco%22">Facchinei, Francisco</searchLink><br /><searchLink fieldCode="AR" term="%22Lampariello%2C+Lorenzo%22">Lampariello, Lorenzo</searchLink><br /><searchLink fieldCode="AR" term="%22Sardellitti%2C+Stefania%22">Sardellitti, Stefania</searchLink><br /><searchLink fieldCode="AR" term="%22Song%2C+Peiran%22">Song, Peiran</searchLink> – Name: TitleSource Label: Source Group: Src Data: IEEE Transactions on Signal Processing; Apr2017, Vol. 65 Issue 8, p1945-1960, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing+management%22">Distributed computing management</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing+software%22">Distributed computing software</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+algorithms%22">Distributed algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22MIMO+systems%22">MIMO systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the (nontrivial) application of the framework to the following relevant large-scale problems ranging from communications to machine learning: 1) (generalizations of) the rate profile maximization in MIMO interference broadcast networks; 2) the max–min fair multicast multigroup beamforming problem in a multicell environment; and 3) a general nonconvex constrained bi-criteria formulation for $k$ -sparse variable selection in statistical learning; the two criteria are a nonconvex loss objective function, measuring the fitness of the model to data, and the latter is a nonconvex sparsity-inducing constraint in the general form of difference-of-convex (DC) functions, which allows to accomodate in a unified fashion convex and nonconvex surrogates of the $\ell _0$ function. The proposed algorithms outperform current state-of-the-art schemes for 1)–3) both theoretically and numerically. For instance, they are the first distributed schemes for the class of problems 1) and 2); and they also lead to subproblems enjoying closed form solutions. [ABSTRACT FROM PUBLISHER] – Name: Abstract Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Signal Processing is the property of IEEE 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TSP.2016.2637314 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1945 Subjects: – SubjectFull: Distributed computing Type: general – SubjectFull: Distributed computing management Type: general – SubjectFull: Distributed computing software Type: general – SubjectFull: Distributed algorithms Type: general – SubjectFull: MIMO systems Type: general Titles: – TitleFull: Parallel and Distributed Methods for Constrained Nonconvex Optimization-Part II: Applications in Communications and Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Scutari, Gesualdo – PersonEntity: Name: NameFull: Facchinei, Francisco – PersonEntity: Name: NameFull: Lampariello, Lorenzo – PersonEntity: Name: NameFull: Sardellitti, Stefania – PersonEntity: Name: NameFull: Song, Peiran IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: Apr2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 1053587X Numbering: – Type: volume Value: 65 – Type: issue Value: 8 Titles: – TitleFull: IEEE Transactions on Signal Processing Type: main |
| ResultId | 1 |