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Projection Algorithms for Convex and Combinatorial Optimization

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자료유형E-Book
개인저자Haddock, Jamie.
단체저자명University of California, Davis. Applied Mathematics.
서명/저자사항Projection Algorithms for Convex and Combinatorial Optimization.
발행사항[S.l.] : University of California, Davis., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항193 p.
소장본 주기School code: 0029.
ISBN9780438290549
일반주기 Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Jesus A. De Loera.
요약This 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
요약Chapter 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
요약Chapter 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
요약Chapter 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
일반주제명Applied mathematics.
Mathematics.
Computer science.
언어영어
기본자료 저록Dissertation Abstracts International80-01B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14998659

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