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020 ▼a 9780438168909
035 ▼a (MiAaPQ)AAI10824345
035 ▼a (MiAaPQ)umn:19218
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 658
1001 ▼a Tao, Shaozhe.
24510 ▼a Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications.
260 ▼a [S.l.] : ▼b University of Minnesota., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 189 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: Shuzhong Zhang
5021 ▼a Thesis (Ph.D.)--University of Minnesota, 2018.
520 ▼a Many problems in machine learning can be formulated using optimization models with constraints that are well structured. Driven in part by such applications, the need to solve very large scale optimization models is pushing the performance limit
520 ▼a First, we study popular scalable methods on sparse structured models, including alternating direction method of multipliers, coordinate descent method, proximal gradient method and accelerated proximal gradient method. In contrast to many global
520 ▼a Next we move on to group sparse structured model. We develop an inverse covariance estimator that can regularize for overlapping group sparsity, and provide better estimates, especially when the dimension size is much larger than the number of s
520 ▼a Finally, we explore a certain low-rank structure in tensor. We construct the connection between the low-rank property in tensor and the group sparsity in its factor matrices. This provides a way to find a low-rank tensor decomposition via a regu
590 ▼a School code: 0130.
650 4 ▼a Industrial engineering.
690 ▼a 0546
71020 ▼a University of Minnesota. ▼b Industrial and Systems Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0130
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998648 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자