• Journal of applied mathematics & informatics
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• 제32권3_4호
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• pp.389-403
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• 2014

In this paper, we first provide comparison results of preconditioned AOR methods with direct preconditioners $I+{\beta}L$, $I+{\beta}U$ and $I+{\beta}(L+U)$ for solving a linear system whose coefficient matrix is a large sparse irreducible L-matrix, where ${\beta}$ > 0. Next we propose how to find a near optimal parameter ${\beta}$ for which Krylov subspace method with these direct preconditioners performs nearly best. Lastly numerical experiments are provided to compare the performance of preconditioned iterative methods and to illustrate the theoretical results.

• 대한기계학회논문집A
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• 제33권9호
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• pp.878-885
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• 2009

One of important factors in designing MEMS resonators for RF filters is obtaining a desired frequency response function (FRF) within a specific frequency range of interest. Because various array-type MEMS resonators have been recently introduced to improve the filter characteristics such as bandwidth, pass-band, and shape factor, the degrees of freedom (DOF) of finite elements for their FRF calculation dramatically increases and therefore raises computational difficulties. In this paper the Krylov subspace-based model order reduction using moment-matching with non-zero expansion points is represented as a numerical solution to perform the frequency response analyses of those array-type MEMS resonators in an efficient way. By matching moments at a frequency around the specific operation range of the array-type resonators, the required FRF can be efficiently calculated regardless of their operating frequency from significantly reduced systems. In addition, because of the characteristics of the moment-matching method, a minimal order of reduced system with a prearranged accuracy can be determined through an error indicator using successive reduced models, which is very useful to automate the order reduction process and FRF calculation for structural optimization iterations. We also found out that the presented method could obtain the FRF of a $6\times6$ array-type resonator within a seventieth of the computational time necessary for the direct method and in addition FRF calculation by the mode superposition method could not even be completed because of a data overflow with a half after calculation of 9,722 eigenmodes.

• Structural Engineering and Mechanics
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• 제76권5호
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• pp.643-651
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• 2020

Generally, it is necessary to perform transient structural analysis in order to verify and improve the seismic performance of high-rise buildings and bridges against earthquake loads. In this paper, we propose the model order reduction (MOR) method using the Krylov vectors to perform seismic analysis for linear and elastic systems in an efficient way. We then compared the proposed method with the mode superposition method (MSM) by using the limited numbers of modal vectors (or eigenvectors) calculated from the modal analysis. In the calculation, the data of the El Centro earthquake in 1940 were adopted for the seismic loading in the transient analysis. The numerical accuracy and efficiency of the two methods were compared in detail in the case of a simplified high-rise building.

• 대한기계학회논문집A
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• 제21권11호
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• pp.1852-1861
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• 1997

An enhanced subspace iteration algorithm has been developed to solve eigenvalue problems reliably and efficiently. Basic subspace iteration algorithm has been improved by eliminating recalculation of converged eigenvectors, using Krylov sequence as initial vectors and incorporating with shifting techniques. The number of iterations and computational time have been considerably reduced when compared with the original one, and reliability for catching copies of the multiple roots has been retained successfully. Further research would be required for mathematical justification of the present method.

• Journal of applied mathematics & informatics
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• 제28권1_2호
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• pp.351-361
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• 2010

The solution of large sparse linear systems is one of the most important problems in large scale scientific computing. Among the many methods developed, the preconditioned Krylov subspace methods are considered the preferred methods. Selecting a suitable preconditioner with appropriate parameters for a specific sparse linear system presents a challenging task for many application scientists and engineers who have little knowledge of preconditioned iterative methods. The prediction of ILU type preconditioners was considered in [27] where support vector machine(SVM), as a data mining technique, is used to classify large sparse linear systems and predict best preconditioners. In this paper, we apply the data mining approach to the sparse approximate inverse(SAI) type preconditioners to find some parameters with which the preconditioned Krylov subspace method on the linear systems shows best performance.

• Nuclear Engineering and Technology
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• 제49권6호
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• pp.1114-1124
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• 2017

Acceleration/preconditioning strategies available in the SCEPTRE radiation transport code are described. A flexible transport synthetic acceleration (TSA) algorithm that uses a low-order discrete-ordinates ($S_N$) or spherical-harmonics ($P_N$) solve to accelerate convergence of a high-order $S_N$ source-iteration (SI) solve is described. Convergence of the low-order solves can be further accelerated by applying off-the-shelf incomplete-factorization or algebraic-multigrid methods. Also available is an algorithm that uses a generalized minimum residual (GMRES) iterative method rather than SI for convergence, using a parallel sweep-based solver to build up a Krylov subspace. TSA has been applied as a preconditioner to accelerate the convergence of the GMRES iterations. The methods are applied to several problems involving electron transport and problems with artificial cross sections with large scattering ratios. These methods were compared and evaluated by considering material discontinuities and scattering anisotropy. Observed accelerations obtained are highly problem dependent, but speedup factors around 10 have been observed in typical applications.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제3권2호
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• pp.1-4
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• 1999

The Helmholtz equation is very important in physics and engineering. However, solution of the Helmholtz equation is in general known as a very difficult phenomenon. For if the ${\omega}$ is negative, the FDM discretized linear system becomes indefinite, whose solution by iterative method requires a very clever preconditioner. In this paper we assume that ${\omega}$ is nonnegative, and determine the optimal ${\rho}$ parameter for the three dimensional ADI iteration for the Helmholtz equation. The ADI(Alternating Direction Implicit) method is also getting new attentions due to the fact that it is very suitable to the vector/parallel computers, for example, as a preconditioner to the Krylov subspace methods. However, classical ADI was developed for two dimensions, and for three dimensions it is known that its convergence behaviour is quite different from that in two dimensions. So far, in three dimensions the so-called Douglas-Rachford form of ADI was developed. It is known to converge for a relatively wide range of ${\rho}$ values but its convergence is very slow. In this paper we determine the necessary conditions of the ${\rho}$ parameter for the convergence and optimal ${\rho}$ for the three dimensional ADI iteration of the Peaceman-Rachford form for the real Helmholtz equation with nonnegative ${\omega}$. Also, we conducted some experiments which is in close agreement with our theory. This straightforward extension of Peaceman-rachford ADI into three dimensions will be useful as an iterative solver itself or as a preconditioner to the the Krylov subspace methods, such as CG(Conjugate Gradient) method or GMRES(m).

• 한국전산구조공학회논문집
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• 제23권2호
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• pp.153-163
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• 2010

Krylov 부공간 모델차수축소법은 초기 유한요소모델과 축소모델의 전달함수의 계수인 모멘트를 일치시키는 방법을 이용하는 축소기법으로 이미 대형 유한요소모델의 주파수응답함수의 효율적인 계산에 많이 사용되고 있는 방법 중의 하나이다. 본 논문에서는 Krylov 부공간 축소기법을 이용한 관심 주파수영역에 대한 주파수응답 해석과 이를 통하여 계산된 주파수응답의 여러 가지 설계변수에 대한 설계민감도 해석 방법을 제안하였다. 일반적으로 기계시스템의 주파수응답을 고려한 최적설계를 위해서는 설계변수에 대한 관심 주파수영역에서의 주파수응답 및 그의 민감도 정보가 요구되므로, 고려하는 유한요소모델이 대형일 경우에는 관심 주파수영역에서의 반복적인 해석으로 인한 계산비용의 문제가 심각하게 대두된다. 본 논문에서는 축소모델을 이용하여 주파수응답과 주파수응답의 설계민감도 해석을 수행하여 계산의 효율성을 극대화하였다. 계산상 시스템행렬의 민감도 계산에는 시간측면과 구현의 용이성 측면에서 장점이 있는 준해석적 방법을 이용하였다. 수치 예제를 통하여 축소기법을 이용한 주파수응답의 설계민감도 해석 결과를 초기 유한요소모델의 민감도 결과와 비교하여 우수한 정확성 및 효율성을 확인하였다. 본 논문에서 제안된 방법을 주파수응답을 고려하는 최적설계에 이용하는 경우, 결과의 정확성 및 계산비용 측면에서 매우 효과적인 방법이 될 수 있을 것으로 판단된다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제3권2호
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• pp.5-16
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• 1999

We introduce a new projector for accelerating convergence of a symmetric eigenvalue problem Ax = x, and devise a power/Lanczos hybrid algorithm. Acceleration can be achieved by removing the hard-to-annihilate nonsolution eigencomponents corresponding to the widespread eigenvalues with modulus close to 1, by estimating them accurately using the Lanczos method. However, the additional Lanczos results can be obtained without expensive matrix-vector multiplications but a very small amount of extra work, by utilizing simple power-Lanczos interconversion algorithms suggested. Numerical experiments are given at the end.

• 대한수학회논문집
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• 제12권1호
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• pp.145-155
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• 1997

We present a couple of tools that can be used in the solution of nonsymmetric eigenvalue problems. The first one allows us to convert power iterates into Arnoldi's results so that a few eigenpairs are easily obtainable. The other converts Arnoldi's results into power iterates to simulate the power method and improve the result. Suggestions for application are also given.