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Smooth Quasi-Newton Methods for Nonsmooth Optimization
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Smooth Quasi-Newton Methods for Nonsmooth Optimization. |
| 개인저자 | Guo, Jiayi. |
| 단체저자명 | Cornell University. Operations Research. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 104 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438026391 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Adrian Lewis. |
| 요약 | The success of Newton's method for smooth optimization, when Hessians are available, motivated the idea of quasi-Newton methods, which approximate Hessians in response to changes in gradients and result in superlinear convergence on smooth funct |
| 일반주제명 | Operations research. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
