• Journal of applied mathematics & informatics
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• 제30권5_6호
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• pp.759-772
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• 2012

In this paper, semi-infinite constrained programming, a class of constrained nonsmooth optimization problems, are transformed into unconstrained nonsmooth convex programs under the help of exact penalty function. The unconstrained objective function which owns the primal-dual gradient structure has connection with $\mathcal{VU}$-space decomposition. Then a $\mathcal{VU}$-space decomposition method can be applied for solving this unconstrained programs. Finally, the superlinear convergence algorithm is proved under certain assumption.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.419-424
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• 1999

In this paper, the comparison of the first order approximation schemes such as SLP (sequential linear programming), CONLIN(convex linearization), MMA(method of moving asymptotes) and the second order approximation scheme, SQP(sequential quadratic programming) was accomplished for optimization of and nonlinear structures. It was found that MMA and SQP(sequential quadratic programming) was accomplished for optimization of and nonlinear structures. It was found that MMA and SQP are the most efficient methods for optimization. But the number of function call of SQP is much more than that of MMA. Therefore, when it is considered with the expense of computation, MMA is more efficient than SQP. In order to examine the efficiency of MMA for complex optimization problem, it was applied to the helicopter tail boom considering column buckling and local wall buckling constraints. It is concluded that MMA can be a very efficient approximation scheme from simple problems to complex problems.

• 대한수학회지
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• 제50권4호
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• pp.727-753
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• 2013

In this paper, we introduce three new broad classes of second-order generalized convex functions, namely, ($\mathcal{F}$, $b$, ${\phi}$, ${\rho}$, ${\theta}$)-sounivex functions, ($\mathcal{F}$, $b$, ${\phi}$, ${\rho}$, ${\theta}$)-pseudosounivex functions, and ($\mathcal{F}$, $b$, ${\phi}$, ${\rho}$, ${\theta}$)-quasisounivex functions; formulate eight general second-order duality models; and prove appropriate duality theorems under various generalized ($\mathcal{F}$, $b$, ${\phi}$, ${\rho}$, ${\theta}$)-sounivexity assumptions for a multiobjective programming problem containing arbitrary norms.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (1)
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• pp.697-699
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• 2004

Despite many successful spectral clustering algorithm (based on the spectral decomposition of Laplacian(1) or stochastic matrix(2) ) there are several unsolved problems. Most spectral clustering Problems are based on the normalized of algorithm(3) . are close to the classical graph paritioning problem which is NP-hard problem. To get good solution in polynomial time. it needs to establish its convex form by using relaxation. In this paper, we apply a novel optimization technique. semidefinite programming(SDP). to the unsupervised clustering Problem. and present a new multiple Partitioning method. Experimental results confirm that the Proposed method improves the clustering performance. especially in the Problem of being mixed with non-compact clusters compared to the previous multiple spectral clustering methods.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.111-118
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• 2021

본 논문에서는 디바이스 캐싱 네트워크에서 다양한 퀄리티의 콘텐츠를 캐싱하는 기술을 제안한다. 하나의 파일을 온전히 캐싱하는 기존 기술들과 다르게, 저자는 콘텐츠의 일부 조각을 캐싱하는 것을 허용하였고, 사용자가 스스로 캐시 히트를 달성할 수 있는 경우를 고려하였다. 캐싱하는 콘텐츠의 퀄리티와 캐시 히트율 간의 트레이드오프를 분석하고, 사용자가 소비하는 콘텐츠의 기대 퀄리티를 최대화하는 디바이스 캐싱 기법을 제안한다. 퀄리티와 파일 크기의 관계 파라미터에 따라 볼록 최적화 문제와 DC programming 문제 두 가지 방식으로 나누어서 캐싱 문제를 풀어냈다. 퀄리티 증가 폭에 비해 파일 크기가 더 빠르게 증가하면, 인기도에 따라 캐싱할 콘텐츠의 부분 조각이 점차 증가하는 반면, 파일 크기가 더 느리게 증가하면, 일부 인기도가 높은 콘텐츠는 전체를 캐싱하고 그렇지 않은 콘텐츠는 아예 캐싱하지 않는 결과를 낸다.

• 한국해양공학회지
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• 제29권3호
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• pp.263-269
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• 2015

This paper proposes a new approach for solving the weather routing problem by dividing it into two phases with the goal of fuel saving. The problem is to decide two optimal variables: the heading angle and speed of the ship under several constraints. In the first phase, the optimal route is obtained using the Real-Time Adaptive A* algorithm with a fixed ship speed. In other words, only the heading angle is decided. The second phase is the speed scheduling phase. In this phase, the original problem, which is a nonlinear optimization problem, is converted into a geometric programming problem. By solving this geometric programming problem, which is a convex optimization problem, we can obtain an optimal speed scheduling solution very efficiently. A simple case of numerical simulation is conducted in order to validate the proposed method, and the results show that the proposed method can save fuel compared to a constant engine output voyage and constant speed voyage.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.984-987
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• 1996

We consider a feedback control system including interval plant where uncertain parameters expressed in the hyperrectangular box X. Here we define the maximum value of the integral of the error(ISE) as the worst performance index(WPI) due to the plant parameter uncertainty. Suppose that the closed loop system retains robust stability and it belongs to type I. Then we show that the WPI occurs only on the exposed edges of Q. In particular, it is also shown that if ISE is a convex function relative to X, the WPI is attained at one of vertices of X. Some examples are given.

• 대한산업공학회지
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• 제26권2호
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• pp.117-127
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• 2000

In this paper, we propose a new convex combination weight rule for the cross decomposition method which is known to be one of the most reliable and promising strategies for the large scale optimization problems. It is called generalized cross decomposition, a modification of linear mean value cross decomposition for specially structured linear programming problems. This scheme puts more weights on the recent subproblem solutions other than the average. With this strategy, we are having more room for selecting convex combination weights depending on the problem structure and the convergence behavior, and then, we may choose a rule for either faster convergence for getting quick bounds or more accurate solution. Also, we can improve the slow end-tail behavior by using some combined rules. Also, we provide some computational test results that show the superiority of this strategy to the mean value cross decomposition in computational time and the quality of bounds.

• Management Science and Financial Engineering
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• 제18권2호
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• pp.23-27
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• 2012

The problem of jointly determining a robust optimal bundle of price and order quantity for a retailer in a single-retailer, single supplier, single-product supply chain is considered. Demand is modeled as a decreasing power function of product price, and unit purchasing cost is modeled as a decreasing power function of order quantity and demand. Parameters defining the two power functions are uncertain but their possible values are characterized by ellipsoids. We extend a previous study in two ways; the purchasing cost function is generalized to take into account the economies of scale realized by higher product demand in addition to larger order quantity, and an exact transformation into an equivalent convex optimization program is developed instead of a geometric programming approximation scheme proposed in the previous study.

• 제어로봇시스템학회지
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• 제10권3호
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• pp.27-33
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• 2004

최적화는 시스템공학에서 자주 등장하는 문제이며 흔히 다음과 같은 수학적 계획(mathematical programming) 문제로 표현된다. min f(x) (P) subject to g(x) ≤ 0 h(x) ： 0 여기서 x∈R/sup n/, f：R/sup n/→R, g：R/sup n/→R/sup l/, h：R/sup n/→R/sup m/, 그리고 n m이다. 만약 목적함수(objective function)와 가능 영역(feasible region)이 볼록(convex)하다면, 예를 틀어 f(x)와 g(x)가 아래로 볼록하고 h(x)가 선형이라면. 이는 볼록 문제(convex problem)이며 오직 하나의 지역 최소점(local minimum)을 가진다. 그러나 많은 경우. 예를 들어 h(x)가 비선형이라면, 여러 개의 지역 최소점을 가질 수 있는 비 볼록 문제(nonconvex problem)가 된다. 이때 진정한 최소점을 찾는 것. 즉 전역 최적화 (global optimization)가 요구된다.(중략)