• 대한전기학회논문지:전력기술부문A
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• 제55권3호
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• pp.133-138
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• 2006

This paper presents an Optimal Power Flow (OPF) algorithm using Interior Point Method (IPM) to swiftly and precisely perform the five minute dispatch. This newly suggested methodology is based on Affine Scaling Interior Point Method which is favorable for large-scale problems involving many constraints. It is also eligible for OPF problems in order to improve the calculation speed and the preciseness of its resultant solutions. Big-M Method is also used to improve the solution stability. Finally, this paper provides relevant case studies to confirm the efficiency of the proposed methodology.

• 산업경영시스템학회지
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• 제21권45호
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• pp.93-100
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• 1998

The researches of Linear Programming are Khachiyan Method, which uses Ellipsoid Method, and Karmarkar, Affine, Path-Following and Interior Point Method which have Polynomial-Time complexity. In this study, Karmarkar Method is more quickly solved as 50 times then Simplex Method for optimal solution. but some special problem is not solved by Karmarkar Method. As a result, the algorithm by APL Language is proved time efficiency and optimal solution in the Primal-Dual interior point algorithm. Furthermore Karmarkar Method and Primal-Dual interior point Method is compared in some examples.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.483-490
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• 2021

In Indonesia, coffee shops, commonly called warung or kedai shops, have begun to appear amid society from remote villages to urban centers. Therefore, the purpose of this study is to examine the effect of cafe atmosphere (i.e., exterior, interior, interior point-of-purchase displays and store layout) on the purchase decision of Generation Z. This study is conducted because of cafe competition is currently overgrowing. This study model consisted of five variables: exterior, interior, interior point-of-purchase displays, store layout, and purchase decision. Sampling in this study used non-probability, with a purposive sampling technique. According to predetermined criteria, the data collection technique employed a questionnaire distributed online to consumers had visited a cafe at least once in the last three months. This study's sample was 137 cafe visitors in Yogyakarta, representing one of the big cities in Indonesia. Therefore, the data was analyzed by using multiple regression. The results of the study indicated that the exterior and interior had a positive and significant effect on purchasing decision. Likewise, interior point-of-purchase displays and store layout positively and significantly affected purchase decision. In addition, this study's findings generally concluded that the cafe atmosphere had a positive and significant effect on purchase decision.

• 경영과학
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• 제21권1호
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• pp.77-86
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• 2004

In this paper, we present a methodology for solving the multicommodity network flow problems using interior point methods. In our method, the minimum cost network flow problem extracted from the given multicommodity network flow problem is solved by primal-dual barrier method in which normal equations are solved partially using preconditioned conjugate gradient method. Based on the solution of the minimum cost network flow problem, a warm-start point is obtained from which Castro's specialized interior point method for multicommodity network flow problem starts. In the computational experiments, the effectiveness of our methodology is shown.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제13권1호
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• pp.41-53
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• 2009

A primal-dual interior point method(IPM) not only is the most efficient method for a computational point of view but also has polynomial complexity. Most of polynomialtime interior point methods(IPMs) are based on the logarithmic barrier functions. Peng et al.([14, 15]) and Roos et al.([3]-[9]) proposed new variants of IPMs based on kernel functions which are called self-regular and eligible functions, respectively. In this paper we define a new kernel function and propose a new IPM based on this kernel function which has $O(n^{\frac{2}{3}}log\frac{n}{\epsilon})$ and $O(\sqrt{n}log\frac{n}{\epsilon})$ iteration bounds for large-update and small-update methods, respectively.

• 대한수학회보
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• 제46권3호
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• pp.521-534
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• 2009

In this paper we present new large-update primal-dual interior point algorithms for $P_*$ linear complementarity problems(LAPS) based on a class of kernel functions, ${\psi}(t)={\frac{t^{p+1}-1}{p+1}}+{\frac{1}{\sigma}}(e^{{\sigma}(1-t)}-1)$, p $\in$ [0, 1], ${\sigma}{\geq}1$. It is the first to use this class of kernel functions in the complexity analysis of interior point method(IPM) for $P_*$ LAPS. We showed that if a strictly feasible starting point is available, then new large-update primal-dual interior point algorithms for $P_*$ LAPS have $O((1+2+\kappa)n^{{\frac{1}{p+1}}}lognlog{\frac{n}{\varepsilon}})$ complexity bound. When p = 1, we have $O((1+2\kappa)\sqrt{n}lognlog\frac{n}{\varepsilon})$ complexity which is so far the best known complexity for large-update methods.

• 대한수학회논문집
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• 제25권4호
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• pp.655-669
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• 2010

In this paper we propose new primal-dual interior point methods (IPMs) for $P_*(\kappa)$ linear complementarity problems (LCPs) and analyze the iteration complexity of the algorithm. New search directions and proximity measures are defined based on a class of kernel functions, $\psi(t)=\frac{t^2-1}{2}-{\int}^t_1e{^{q(\frac{1}{\xi}-1)}d{\xi}$, $q\;{\geq}\;1$. If a strictly feasible starting point is available and the parameter $q\;=\;\log\;\(1+a{\sqrt{\frac{2{\tau}+2{\sqrt{2n{\tau}}+{\theta}n}}{1-{\theta}}\)$, where $a\;=\;1\;+\;\frac{1}{\sqrt{1+2{\kappa}}}$, then new large-update primal-dual interior point algorithms have $O((1\;+\;2{\kappa})\sqrt{n}log\;n\;log\;{\frac{n}{\varepsilon}})$ iteration complexity which is the best known result for this method. For small-update methods, we have $O((1\;+\;2{\kappa})q{\sqrt{qn}}log\;{\frac{n}{\varepsilon}})$ iteration complexity.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제11권4호
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• pp.69-88
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• 2007

In this paper we propose new large-update primal-dual interior point algorithms for $P_{\neq}({\kappa})$ linear complementarity problems(LCPs). New search directions and proximity measures are proposed based on a specific class of kernel functions, ${\psi}(t)={\frac{t^{p+1}-1}{p+1}}+{\frac{t^{-q}-1}{q}}$, q>0, $p{\in}[0,\;1]$, which are the generalized form of the ones in [3] and [12]. It is the first to use this class of kernel functions in the complexity analysis of interior point method(IPM) for $P_*({\kappa})$LCPs. We showed that if a strictly feasible starting point is available, then new large-update primal-dual interior point algorithms for $P_*({\kappa})$ LCPs have the best known complexity $O((1+2{\kappa}){\sqrt{2n}}(log2n)log{\frac{n}{\varepsilon}})$ when p=1 and $q=\frac{1}{2}(log2n)-1$.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.289-292
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• 1996

In Cholesky factorization of the interior point method, dense columns of A matrix make dense Cholesky factor L regardless of sparsity of A matrix. We introduce a method to transform a primal problem to a dual problem in order to preserve the sparsity.

• 한국국방경영분석학회지
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• 제24권1호
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• pp.146-156
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• 1998

Cholesky factorization is known to be inefficient to problems with dense column and network problems in interior point methods. We use the conjugate gradient method and preconditioners to improve the convergence rate of the conjugate gradient method. Several preconditioners were applied to LPABO 5.1 and the results were compared with those of CPLEX 3.0. The conjugate gradient method shows to be more efficient than Cholesky factorization to problems with dense columns and network problems. The incomplete Cholesky factorization preconditioner shows to be the most efficient among the preconditioners.