LDR | | 00000nmm u2200205 4500 |
001 | | 000000329715 |
005 | | 20241016145805 |
008 | | 181129s2018 ||| | | | eng d |
020 | |
▼a 9780438168466 |
035 | |
▼a (MiAaPQ)AAI10822369 |
035 | |
▼a (MiAaPQ)umn:19172 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
049 | 1 |
▼f DP |
082 | 0 |
▼a 658 |
100 | 1 |
▼a Gao, Xiang. |
245 | 10 |
▼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. |
502 | 1 |
▼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 |
710 | 20 |
▼a University of Minnesota.
▼b Industrial Engineering. |
773 | 0 |
▼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 |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998468
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자
▼b 관리자 |