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Semi-proximal Augmented Lagrangian-Based Decomposition Methods for Primal Block-Angular Convex Composite Quadratic Conic Programming Problems.

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Title: Semi-proximal Augmented Lagrangian-Based Decomposition Methods for Primal Block-Angular Convex Composite Quadratic Conic Programming Problems.
Authors: Lam, Xin-Yee1 (AUTHOR) mattohkc@math.nus.edu.sg, Sun, Defeng2 (AUTHOR) defeng.sun@polyu.edu.hk, Toh, Kim-Chuan1,3 (AUTHOR) mattohkc@math.nus.edu.sg
Source: INFORMS Journal on Optimization. Summer2021, Vol. 3 Issue 3, p254-277. 24p.
Database: Business Source Ultimate
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  Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Optimization%22">INFORMS Journal on Optimization</searchLink>. Summer2021, Vol. 3 Issue 3, p254-277. 24p.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1287/ijoo.2019.0048
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 254
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      – TitleFull: Semi-proximal Augmented Lagrangian-Based Decomposition Methods for Primal Block-Angular Convex Composite Quadratic Conic Programming Problems.
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            NameFull: Lam, Xin-Yee
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            NameFull: Sun, Defeng
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              Text: Summer2021
              Type: published
              Y: 2021
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            – TitleFull: INFORMS Journal on Optimization
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