• 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.

• 대한수학회보
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• 제38권2호
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• pp.285-292
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• 2001

The authors compute the Ricci curvature of the GRW space-time to obtain two conditions for the conjugate points which appear as the Timelike Convergence Condition(TCG) and the Jacobi inequality. Moreover, under such two conditions, we obtain a lower bound of the length of a unit timelike geodesic for focal points emanating form the immersed spacelike hypersurface, the graph over the fiber in the GRW space-time.

• Structural Engineering and Mechanics
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• 제65권4호
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• pp.415-422
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• 2018

The Conjugate Finite-Step Length" (CFSL) algorithm is proposed to improve the efficiency and robustness of first order reliability method (FORM) for reliability analysis of highly nonlinear problems. The conjugate FORM-based CFSL is formulated using the adaptive conjugate search direction based on the finite-step size with simple adjusting condition, gradient vector of performance function and previous iterative results including the conjugate gradient vector and converged point. The efficiency and robustness of the CFSL algorithm are compared through several nonlinear mathematical and structural/mechanical examples with the HL-RF and "Finite-Step-Length" (FSL) algorithms. Numerical results illustrated that the CFSL algorithm performs better than the HL-RF for both robust and efficient results while the CFLS is as robust as the FSL for structural reliability analysis but is more efficient.

• Journal of the Korean Statistical Society
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• 제25권4호
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• pp.577-588
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• 1996

The Bayes factor provides a possible hierarchical Bayesian approach for studying the change point problems. A hypothesis for testing change versus no change is considered using predictive distributions. When the underlying distribution is in one-parameter exponential family with conjugate priors, Bayes factors are investigated to the hypothesis above. Finally one example is provided .

• 한국전산구조공학회논문집
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• 제34권4호
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• pp.191-197
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• 2021

분자동역학에서의 원자들의 유도전하를 계산하기 위해서는 유도전하를 미지수로 하는 선형방정식을 풀어야 하는데 원자들의 위치가 변화할 때마다 필요한 계산이므로 상당한 계산비용이 요구된다. 따라서 효율적인 유도전하 계산 방법은 다양한 시스템을 해석하기 위해서 필수적이다. 본 연구에서는 constraints가 존재하는 Lagrange 방정식의 해에 대한 선형 시스템, 즉 saddle point를 가지는 문제를 해결하기 위해서 Uzawa method를 도입하였다. Uzawa 매개변수가 수렴 속도에 영향을 미치는 단점을 극복하고 행렬 연산의 효율성을 위해서 Schur complement와 preconditioned conjugate gradient (PCG) 방법을 통해 계산의 효율성을 극대화하는 가속 Uzawa algorithm을 적용한다. 두 금속 나노입자가 전기장에 놓여진 분자동역학 수치모델을 통해서 제시된 방법이 유도전하계산의 수렴성, 효율성 측면에서 모두 향상된 결과를 도출함을 확인하였다. 특히 기존의 가우스 소거법에 의한 계산보다 약 1/10으로 계산비용이 절감되었고, 기본 Uzawa method에 비하여 conjugate gradient (CG)의 높은 수렴성이 입증되었다.

• Korean Journal of Mathematics
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• 제24권3호
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• pp.397-407
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• 2016

In this paper, the concepts of k-smoothness, k-very smoothness and k-strongly smoothness of Banach spaces are dealt with together briefly by introducing three types k-denting point regarding different topology of conjugate spaces of Banach spaces. In addition, the characterization of first type ${\omega}^*-k$ denting point is described by using the slice of closed unit ball of conjugate spaces.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.740-749
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• 1998

This paper proposes compression of image data using neural networks based on conjugate gradient method and dynamic tunneling system. The conjugate gradient method is applied for high speed optimization .The dynamic tunneling algorithms, which is the deterministic method with tunneling phenomenon, is applied for global optimization. Converging to the local minima by using the conjugate gradient method, the new initial point for escaping the local minima is estimated by dynamic tunneling system. The proposed method has been applied the image data compression of 12 ${\times}$12 pixels. The simulation results shows the proposed networks has better learning performance , in comparison with that using the conventional BP as learning algorithm.

• 한국측량학회지
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• 제37권2호
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• pp.55-62
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• 2019

The stereo geometry establishment based on the precise sensor modeling is prerequisite for accurate stereo data processing. Ground control points are generally required for the accurate sensor modeling though it is not possible over the area where the accessibility is limited or reference data is not available. For the areas, the relative orientation should be carried out to improve the geometric consistency between the stereo data though it does not improve the absolute positional accuracy. The relative orientation requires conjugate points that are well distributed over the entire image region. Therefore the automatic conjugate point extraction is required because the manual operation is labor-intensive. In this study, we applied the method consisting of the key point extraction, the search space minimization based on the epipolar line, and the rigorous outlier detection based on the RPCs (Rational Polynomial Coefficients) bias compensation modeling. We tested different parameters of window sizes for Kompsat-2 across track stereo data and analyzed the RPCs precision after the bias compensation for the cases whether the epipolar line information is used or not. The experimental results showed that matching outliers were inevitable for the different matching parameterization but they were successfully detected and removed with the rigorous method for sub-pixel level of stereo RPCs precision.

• Journal of applied mathematics & informatics
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• 제29권1_2호
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• pp.173-190
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• 2011

In this paper, we construct a new approach of affine scaling interior algorithm using the affine scaling conjugate gradient and Lanczos methods for bound constrained nonlinear optimization. We get the iterative direction by solving quadratic model via affine scaling conjugate gradient and Lanczos methods. By using the line search backtracking technique, we will find an acceptable trial step length along this direction which makes the iterate point strictly feasible and the objective function nonmonotonically decreasing. Global convergence and local superlinear convergence rate of the proposed algorithm are established under some reasonable conditions. Finally, we present some numerical results to illustrate the effectiveness of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.666-671
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• 2010

Multi-layer perceptron neural networks have been constructed to classify four types of defects in steam generator tubes. Three features are extracted from the signals of the eddy current testing method. These include maximum impedance, phase angle at the point of maximum impedance, and an angle between the point of maximum impedance and the point of half the maximum impedance. Two hundred sets of these features are used for training and assessing the networks. Two approaches are involved to train the networks and to classify the defect type. One is the conjugate gradient method and the other is the Broydon-Fletcher-Goldfarb-Shanno method which is recognized as the most popular algorithm of quasi-Newton methods. It is found from the computation results that the training time of the Broydon-Fletcher-Goldfarb-Shanno method is much faster than that of the conjugate gradient method in most cases. On the other hand, no significant difference of the classification performance between the two methods is observed.