• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.119-136
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• 2007

Block Jacobi and Gauss-Seidel iterative methods are studied for solving $n{\times}n$ fuzzy linear systems. A new splitting method is considered as well. These methods are accompanied with some convergence theorems. Numerical examples are presented to illustrate the theory.

• Communications of the Korean Mathematical Society
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• v.28 no.3
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• pp.603-613
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• 2013

In 2009, Zheng and Miao [B. Zheng and S.-X. Miao, Two new modified Gauss-Seidel methods for linear system with M-matrices, J. Comput. Appl. Math. 233 (2009), 922-930] considered the modified Gauss-Seidel method for solving M-matrix linear system with the preconditioner $P_{max}$. In this paper, we consider the modified Gauss-Seidel method for solving the linear system with the generalized preconditioner $P_{max}({\alpha})$, and study its convergent properties when the coefficient matrix is an H-matrix. Numerical experiments are performed with different examples, and the numerical results verify our theoretical analysis.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.5
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• pp.601-606
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• 2014

The IPO(Iterative Physical Optics) method repeatedly applies the well-known PO(Physical Optics) approximation to calculate the scattered field by a large object. Thus, the IPO method can consider the multiple scattering in the object, which is ignored for the PO approximation. This kind of iteration can improve the final accuracy of the induced current on the scatterer, which can result in the enhancement of the accuracy of the RCS(Radar Cross Section) of the scatterer. Since the IPO method can not exactly but approximately solve the required integral equation, however, the convergence of the IPO solution can not be guaranteed. Hence, we apply the famous techniques used in the inversion of a matrix to the IPO method, which include Jacobi, Gauss-Seidel, SOR(Successive Over Relaxation) and Richardson methods. The proposed IPO methods can efficiently calculate the RCS of a large scatterer, and are numerically verified.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.8 no.12
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• pp.127-138
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• 1985

There are several methods which have been presented up to now in solving the simultaneous linear equations by computer. They are Gaussian Elimination Method, Gauss-Jordan Method, Inverse matrix Method and Gauss-Seidel iterative Method. This paper is not only discussed in their mechanisms compared with their algorithms, depicted flow charts, but also calculated the numbers of arithmetic operations and comparisons in order to criticize their availability. Inverse Matrix Method among em is founded out the smallest in the number of arithmetic operation, but is not the shortest operation time. This paper also indicates the many problems in using these methods and propose the new method which is able to applicate to even small or middle size computers.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.101-124
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• 2002

The convergence rate of a numerical procedure barred on Schwarz Alternating Method (SAM) for solving elliptic boundary value problems (BVP's) depends on the selection of the interface conditions applied on the interior boundaries of the overlapping subdomains. It hee been observed that the Robin condition(mixed interface condition), controlled by a parameter, can optimize SAM's convergence rate. Since the convergence rate is very sensitive to the parameter, Tang[17] suggested another interface condition called over-determined interface condition. Based on the over-determined interface condition, we formulate the two-layer multi-parameterized SAM. For the SAM and the one-dimensional elliptic model BVP's, we determine analytically the optimal values of the parameters. For the two-dimensional elliptic BVP's , we also formulate the two-layer multi-parameterized SAM and suggest a choice of multi-parameter to produce good convergence rate .

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.137-146
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• 2017

For the competitive business environment under purchase dependence, this paper proposes a new approximate calculation of order fill rate which is a probability of satisfying a customer order immediately using the existing inventory. Purchase dependence is different to demand dependence. Purchase dependence treats the purchase behavior of customers, while demand dependence considers demand correlation between items, between regions, or over time. Purchase dependence can be observed in such areas as marketing, manufacturing systems, and distribution systems. Traditional computational methods have a difficulty of the curse of dimensionality for the large cases, when deriving the stationary joint distribution which is utilized to calculate the order fill rate. In order to escape the curse of dimensionality and protect the solution from diverging for the large cases, we develop a greedy iterative search algorithm based on the Gauss-Seidel method. We show that the greedy iterative search algorithm is a dependable algorithm to derive the stationary joint distribution of on-hand inventories in the retailer system by conducting a comparison analysis of a greedy iterative search algorithm with the simulation. In addition, we present some managerial insights such as : (1) The upper bound of order fill rate can be calculated by the one-item pure system, while the lower bound can be provided by the pure system that consists of all items; (2) As the degree of purchase dependence declines while other conditions remain same, it is observed that the difference between the lower and upper bounds reduces, the order fill rate increases, and the order fill rate gets closer to the upper bound.