• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.131-142
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• 1991

The concept of criterion function is proposed as a framework for comparing the geometric and computational characteristics of various nonlinear programming approaches to linear programming such as the method of centers, Karmakar's algorithm and the gravitational method. Also, we discuss various computational issues involved in obtaining an efficient parallel implementation of these methods. Clearly, the most time consuming part in solving a linear programming problem is the direction finding procedure, where we obtain an improving direction. In most cases, finding an improving direction is equivalent to solving a simple optimization problem defined at the current feasible solution. Again, this simple optimization problem can be seen as a least squares problem, and the computational effort in solving the least squares problem is, in fact, same as the effort as in solving a system of linear equations. Hence, getting a solution to a system of linear equations fast is very important in solving a linear programming problem efficiently. For solving system of linear equations on parallel computing machines, an iterative method seems more adequate than direct methods. Therefore, we propose one possible strategy for getting an efficient parallel implementation of an iterative method for solving a system of equations and present the summary of computational experiment performed on transputer based parallel computing board installed on IBM PC.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.349-353
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• 2008

Using the recently developed ABS algorithm for solving linear Diophantine equations we introduce an algorithm for solving a system of m linear integer inequalities in n variables, m $\leq$ n, with full rank coefficient matrix. We apply this result to solve linear integer programming problems with m $\leq$ n inequalities.

• School Mathematics
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• v.8 no.3
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• pp.265-290
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• 2006

The purpose of this study is to search an effective teaching & learning program by examining how much does Excel affect on problem solving of linear function. This study was based on qualitative case study. Teaching experiment was performed for seven periods with five students in 8th graders. Pre and posts tests were attempted to analyze the changes of student's ability on problem solving of linear function. The analysis of tests were performed in category with correct process-object perspective, near process-object perspective, incorrect process-object perspective. According to this study, the subjects showed an improvement on problem solving perspective of linear function. This meant that lessons using Excel had influenced on the problem solving of linear function. We noticed that exploring the learning environment with Excel could supplement paper-and-pencil environment. We believed that Excel with an intuitive dynamic and explorative skills can play a role in scaffolding to support problem solving of linear function.

• Communications of Mathematical Education
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• v.24 no.2
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• pp.445-474
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• 2010

The results of performing first survey after learning linear equation and second survey after 5 months to find out whether there is change in solving process while seventh grade students solve linear equations are as follows. First, as a result of performing McNemar Test in order to find out the correct answer ratio between first survey and second survey, it was shown as $p=.035^a$ in problem x+4=9 and $p=.012^a$ in problem $x+\frac{1}{4}=\frac{2}{3}$ of problem type A while being shown as $p=.012^a$ in problem x+3=8 and $p=.035^a$ in problem 5(x+2)=20 of problem type B. Second, while there were students not making errors in the second survey among students who made errors in the solving process of problem type A and B, students making errors in the second survey among the students who expressed the solving process correctly in the first survey were shown. Third, while there were students expressing the solving process of linear equation correctly for all problems (type A, type B and type C), there were students expressing several problems correctly and unable to do so for several problems. In conclusion, even if a student has expressed the solving process correctly on all problems, it would be difficult to foresee that the student is able to express properly in the solving process when another problem is given. According to the result of analyzing the reaction of students toward three problem types (type A, type B and type C), it is possible to determine whether a certain student is 'able' or 'unable' to express the solving process of linear equation by analyzing the problem solving process.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.147-158
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• 1995

In this paper a face optimization algorithm is developed for solving the problem (P) of optimizing a linear function over the set of efficient solution of a bicriterion linear program. We show that problem (P) can arise in a variety of practical situations. Since the efficient set is in general a nonoconvex set, problem (P) can be classified as a global optimization problem. The algorithm for solving problem (P) is guaranteed to find an exact optimal or almost exact optimal solution for the problem in a finite number of iterations. The algorithm can be easily implemented using only linear programming method.

• The Pure and Applied Mathematics
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• v.27 no.1
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• pp.35-42
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• 2020

In this paper, numerical algorithms for solving a fuzzy system of linear equations with crisp coefficients are presented. We illustrate the efficiency and accuracy of the proposed methods by solving some numerical examples. We also provide a graphical representation of the fuzzy solutions in three-dimension as a visual reference of the solution of the fuzzy system.

• Journal of applied mathematics & informatics
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• v.39 no.3_4
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• pp.587-600
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• 2021

Some problems in engineering and science can be equivalently transformed into solving multi-linear systems. In this paper, we propose two preconditioned AOR iteration methods to solve multi-linear systems with -tensor. Based on these methods, the general conditions of preconditioners are given. We give the convergence theorem and comparison theorem of the two methods. The results of numerical examples show that methods we propose are more effective.

• Communications of the Korean Mathematical Society
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• v.34 no.2
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• pp.657-670
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• 2019

In this paper, we consider the preconditioned iterative methods for solving linear complementarity problem associated with an M-matrix. Based on the generalized Gunawardena's preconditioner, two preconditioned SSOR methods for solving the linear complementarity problem are proposed. The convergence of the proposed methods are analyzed, and the comparison results are derived. The comparison results showed that preconditioned SSOR methods accelerate the convergent rate of the original SSOR method. Numerical examples are used to illustrate the theoretical results.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.802-807
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• 2005

For linear projects, it has long been known that resource utilization is important in improving work efficiency. However, most existing scheduling techniques cannot satisfy the need for solving such issues. This paper presents an optimization model for solving linear scheduling problems involving resource assignment tasks. The proposed model adopts constraint programming (CP) as the searching algorithm for model formulation, and the proposed model is designed to optimize project total cost. Additionally, the concept of outsourcing resources is introduced here to improve project performance.

• Korean Management Science Review
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• v.15 no.1
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• pp.77-85
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• 1998

In this paper a face optimization algorithm is developed for solving the problem (P) of optimizing a linear function over the set of efficient solutions of a multiple objective linear program. Since the efficient set is in general a nonconvex set, problem (P) can be classified as a global optimization problem. Perhaps due to its inherent difficulty, relatively few attempts have been made to solve problem (P) in spite of the potential benefits which can be obtained by solving problem (P). The algorithm for solving problem (P) is guaranteed to find an exact optimal or almost exact optimal solution for the problem in a finite number of iterations.