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Projection Algorithms for Convex and Combinatorial Optimization
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Projection Algorithms for Convex and Combinatorial Optimization. |
| 개인저자 | Haddock, Jamie. |
| 단체저자명 | University of California, Davis. Applied Mathematics. |
| 발행사항 | [S.l.]: University of California, Davis., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 193 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438290549 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Davis, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Jesus A. De Loera. |
| 요약 | This thesis studies projection algorithms in optimization which have frequent applications in data science (e.g., image processing). Contributions in this thesis include proposing and analyzing novel iterative projection methods for the linear f |
| 요약 | Chapter 2 deals with both new and classical iterative projection methods for linear feasibility problems. We provide an accelerated convergence analysis of Motzkin's method on systems of linear equations which is governed by the dynamic range o |
| 요약 | Chapter 3 studies Wolfe's methods for the minimum norm point problem. The complexity of Philip Wolfe's method for the minimum Euclidean-norm point problem over a convex polytope has remained unknown since he proposed the method in 1974. The meth |
| 요약 | Chapter 4 presents results regarding the complexity of the linear feasibility and minimum norm point problems, and connects the two problems. We discuss the complexity of the minimum norm vertex problem over convex polytopes, a problem which is |
| 일반주제명 | Applied mathematics. Mathematics. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
