• Journal of the Korean Operations Research and Management Science Society
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• v.7 no.1
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• pp.11-16
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• 1982

A method is described for the solution of a linearly constrained program with parametric nonlinear objective function. The algorithm proposed in this paper may be regarded as an extension of the simplex method for parametric linear programming. Namely, it specifies the basis at each stage such that feasibility ana optimality of the original problem are satisfied by the optimal solution of the reduced parametric problem involving only nonbasic variables. It is shown that under appropriate assumptions the algorithm is finite. Parametric procedures are also indicated for solving each reduced parametric problem by maintaining the Kuhn-Tucker conditions as the parameter value varies.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.351-360
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• 2006

In this paper, we develop an inexact-Newton method for solving nonsmooth operator equations in infinite-dimensional spaces. The linear convergence and superlinear convergence of inexact-Newton method under some conditions are shown. Then, we characterize the order of convergence in terms of the rate of convergence of the relative residuals. The present inexact-Newton method could be viewed as the extensions of previous ones with same convergent results in finite-dimensional spaces.

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.449-465
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• 1994

The two common numerical methods to approximate the solution of partial differential equations are the finite element method and the finite difference method. They both lead to solving large sparse linear systems. For many applications, it is not unusal that the order of matrix is greater than 10, 000. For this kind of problem, a direct method such as Gaussian elimination can not be used because of the prohibitive cost. To this end, many iterative methods with modest cost have been studied and proposed by numerical analysts.(omitted)

• Journal of applied mathematics & informatics
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• v.12 no.1_2
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• pp.1-20
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• 2003

A modified ABS algorithm for solving a class of singular non-linear systems, $F(x) = 0, $F\;\in \;R^n$, constructed by combining the discreted ABS algorithm and a method of Hoy and Schwetlick (1990), is presented. The second differential operation of F at a point is not required to be calculated directly in this algorithm. Q-quadratic convergence of this algorithm is given.

• Korean Journal of Mathematics
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• v.29 no.3
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• pp.527-545
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• 2021

The aim of this article is to give a numerical method for solving Abel's general fuzzy linear integral equations with arbitrary kernel. The method is based on approximations of fractional integrals and Caputo derivatives. The convergence analysis for the proposed method is also given and the applicability of the proposed method is illustrated by solving some numerical examples. The results show the utility and the greater potential of the fractional calculus method to solve fuzzy integral equations.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.1
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• pp.65-70
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• 1981

L.G. Khachiyan's algorithm for solving a system of strict (or open) linear inequalities with integral coefficients is described. This algorithm is based on the construction of a sequence of ellipsoids in R$^n$ of decreasing n-dimensional volume and contain-ing feasible region. The running time of the algorithm is polynomial in the number of bits of computer core memory required to store the coefficients. It can be applied to solve linear programming problems in polynomially bounded time by the duality theorem of the linear programming problem. But it is difficult to use in solving practical problems. Because it requires the computation of a square roots, besides other arithmatic operations, it is impossible to do these computations exactly with absolute precision.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.333-341
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• 2004

In the real world there are many linear programming problems where all decision parameters are fuzzy numbers. Several approaches exist which use different ranking functions for solving these problems. Unfortunately when there exist alternative optimal solutions, usually with different fuzzy value of the objective function for these solutions, these methods can not specify a clear approach for choosing a solution. In this paper we propose a method to remove the above shortcoming in solving fuzzy number linear programming problems using the concept of expectation and variance as ranking functions

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.97-113
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• 2004

In this paper we shall study moving boundary problems, and we introduce an approach for solving a wide range of them by using calculus of variations and optimization. First, we transform the problem equivalently into an optimal control problem by defining an objective function and artificial control functions. By using measure theory, the new problem is modified into one consisting of the minimization of a linear functional over a set of Radon measures; then we obtain an optimal measure which is then approximated by a finite combination of atomic measures and the problem converted to an infinite-dimensional linear programming. We approximate the infinite linear programming to a finite-dimensional linear programming. Then by using the solution of the latter problem we obtain an approximate solution for moving boundary function on specific time. Furthermore, we show the path of moving boundary from initial state to final state.

• 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.

• 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.