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ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 7, Issue 5 (May 2020), Pages: 87-97

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 Original Research Paper

 Title: A hyperbolic penalty method to solve structured convex minimization problems

 Author(s): Abdelouahed Hamdi 1, Temadher K. Al-Maadeed 1, *, Akram Taati 2

 Affiliation(s):

 1Department of Mathematics, Statistics, and Physics, College of Arts and Sciences, Qatar University, Doha, Qatar
 2Department of Mathematics, University of Guilan, Rasht, Iran

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-1950-8907

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2020.05.011

 Abstract:

This paper presents a decomposition algorithm based on the smooth hyperbolic penalty, which leads to a scheme suitable for parallelized computations. The proposed algorithm can be seen as a separable version of the earlier hyperbolic penalty method built, and its main idea is related to a penalty-type scheme mixed with a kind of resource allocation approach to decompose large scale separable constrained minimization programs. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Hyperbolic penalty methods, Decomposition, Convex functions, Large scale optimization

 Article History: Received 1 December 2019, Received in revised form 15 February 2020, Accepted 20 February 2020

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The authors declare that they have no conflict of interest.

 Citation:

 Hamdi A, Al-Maadeed TK, and Taati A (2020). A hyperbolic penalty method to solve structured convex minimization problems. International Journal of Advanced and Applied Sciences, 7(5): 87-97

 Permanent Link to this page

 Figures

 Fig. 1 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 

 Table 11 Table 12 Table 13 Table 14 Table 15 Table 16 Table 17 Table 18 

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