(A) new solution algorithm for general NLP problem using convex cut function = 오목 절단 함수를 이용한 일반적 비선형계획법 문제의 새로운 해법

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In this thesis, a new convex cut function method is proposed to handle the constraints that appear in the twice-differentiable general NLP problems. For unconstrained minimization problem, the difference of convex underestimator which is proposed by Chang et al. is used. Both the underestimator and the convex cut function are constructed based on the interval Hessian matrix of objective function and constraints, respectively. For univariate global optimization with non-convex constraints, the proposed convex cut function method shows good performance to several tests. To accelerate the convergence of multi-dimensional global optimization with non-convex constraints, the branch-and-bound algorithm is hybridized so that both the underestimator and the convex cut function get tighter as the iteration proceeds. In the proposed method, the difference of convex underestimator which is proposed by Chang et al. is used to obtain lower bound and the convex cut function serves the basis of discarding rule for an infeasible subregion after branching. The cutting region by the convex cut function is memorized as hyperellipsoid. However, the convergence rate decreases as the number of cutting regions increases. To accelerate the convergence rate, an inclusion relation between two hyperellipsoids should be applied in order to reduce the number of cutting regions. We show that the two-hyperellipsoid inclusion relation is determined by maximizing a quadratic function over a sphere, which is a special case of a trust region subproblem. The proposed method is applied to ten univariate constrained NLP test problems, twelve general NLP test problems and five engineering design problems. The proposed method is proven to have a finite $\varepsilon$ -convergence to locate the global optimum point. The numerical experiments indicate that the proposed method competes with another covering method, the index branch-and-bound algorithm, which uses the Lipschitz constant for univariate con...
Advisors
Lee, Tai-Yongresearcher이태용researcher
Description
한국과학기술원 : 생명화학공학과,
Publisher
한국과학기술원
Issue Date
2007
Identifier
263447/325007  / 020015119
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 생명화학공학과, 2007.2, [ vii, 104 p. ]

Keywords

global optimization; NLP; convex cut function; 오목절단함수; 전역최적화; 비선형계획법

URI
http://hdl.handle.net/10203/29001
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=263447&flag=dissertation
Appears in Collection
CBE-Theses_Ph.D.(박사논문)
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