MARC보기
LDR00000nmm u2200205 4500
001000000329715
00520241016145805
008181129s2018 ||| | | | eng d
020 ▼a 9780438168466
035 ▼a (MiAaPQ)AAI10822369
035 ▼a (MiAaPQ)umn:19172
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 658
1001 ▼a Gao, Xiang.
24510 ▼a Low-order Optimization Algorithms: Iteration Complexity 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 220 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Shuzhong Zhang.
5021 ▼a Thesis (Ph.D.)--University of Minnesota, 2018.
520 ▼a Efficiency and scalability have become the new norms to evaluate optimization algorithms in the modern era of big data analytics. Despite its superior local convergence property, second or higher-order methods are often disadvantaged when dealin
520 ▼a In particular, for the black-box optimization, we consider three different settings: (1) the stochastic programming with the restriction that only one random sample can be drawn at any given decision point
590 ▼a School code: 0130.
650 4 ▼a Operations research.
690 ▼a 0796
71020 ▼a University of Minnesota. ▼b Industrial 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=T14998468 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자