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Low-order Optimization Algorithms: Iteration Complexity and Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Low-order Optimization Algorithms: Iteration Complexity and Applications. |
| 개인저자 | Gao, Xiang. |
| 단체저자명 | University of Minnesota. Industrial Engineering. |
| 발행사항 | [S.l.]: University of Minnesota., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 220 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438168466 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Shuzhong Zhang. |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Operations research. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
