| LDR | | 01771nam u200385 4500 |
| 001 | | 000000417936 |
| 005 | | 20190215162425 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438168466 |
| 035 | |
▼a (MiAaPQ)AAI10822369 |
| 035 | |
▼a (MiAaPQ)umn:19172 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 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
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |