• Journal of the Korean Mathematical Society
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• v.48 no.5
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• pp.985-999
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• 2011

A new filled function method is presented in this paper to solve box-constrained nonlinear integer programming problems. It is shown that for a given non-global local minimizer, a better local minimizer can be obtained by local search starting from an improved initial point which is obtained by locally solving a box-constrained integer programming problem. Several illustrative numerical examples are reported to show the efficiency of the present method.

• Bulletin of the Korean Mathematical Society
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• v.42 no.4
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• pp.837-849
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• 2005

In this paper, we obtain a successive quadratic programming algorithm for solving the semidefinite programming (SDP) relaxation of the binary quadratic programming. Combining with a randomized method of Goemans and Williamson, it provides an efficient approximation for the binary quadratic programming. Furthermore, its convergence result is given. At last, We report some numerical examples to compare our method with the interior-point method on Maxcut problem.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.215-218
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• 1996

In work scheduling problems, scheduling constraints are not absolutely rigid; they may be changed depending on the scheduling aspect effected. In order to cope with changes in scheduling constraints and assignment strategies and to optimize scheduling results quickly, this paper will propose a new scheduling method which combines knowledge engineering and mathematical programming techniques.

• East Asian mathematical journal
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• v.35 no.5
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• pp.597-612
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• 2019

In this study, we propose a new logarithmic barrier approach to solve linear programming problem using the projective method of Karmarkar. We are interested in computation of the direction by Newton's method and of the step-size using minorant functions instead of line search methods in order to reduce the computation cost. Our new approach is even more beneficial than classical line search methods. We reinforce our purpose by many interesting numerical simulations proved the effectiveness of the algorithm developed in this work.

• East Asian mathematical journal
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• v.28 no.3
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• pp.293-303
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• 2012

Sequential quadratic programming (SQP) has been one of the most important methods for solving nonlinear constrained optimization problems. Recently, filter method, proposed by Fletcher and Leyffer, has been extensively applied for its promising numerical results. In this paper, we present and study an active set SQP-filter algorithm for inequality constrained optimization. The active set technique reduces the size of quadratic programming (QP) subproblem. While by the filter method, there is no penalty parameter estimate. Moreover, Maratos effect can be overcome by filter technique. Global convergence property of the proposed algorithm are established under suitable conditions. Some numerical results are reported in this paper.

• Proceeding of KASS Symposium
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• 2008.05a
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• pp.101-106
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• 2008

This paper describes a comparative study of genetic algorithm and mathematical programming technique applied in the design optimization of geodesic dome. In particular, the genetic algorithm adopted in this study uses the so-called re-birthing technique together with the standard GA operations such as fitness, selection, crossover and mutation to accelerate the searching process. The finite difference method is used to calculate the design sensitivity required in mathematical programming techniques and three different techniques such as sequential linear programming (SLP), sequential quadratic programming(SQP) and modified feasible direction method(MFDM) are consistently used in the design optimization of geodesic dome. The optimum member sizes of geodesic dome against several external loads is evaluated by the codes $ISADO-GA{\alpha}$ and ISADO-OPT. From a numerical example, we found that both optimization techniques such as GA and mathematical programming technique are very effective to calculate the optimum member sizes of three dimensional discrete structures and it can provide a very useful information on the existing structural system and it also has a great potential to produce new structural system for large spatial structures.

• Journal of Korean Society of Forest Science
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• v.76 no.4
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• pp.361-369
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• 1987

The idea of multiple-use of forest land is tile one field of economics to improve the efficiency of forest land, and is the famous management technique widely used in the developed forestry country. This paper introduces the STEM and the constraint method, which is one kind of mathematical programming techniques used for multiple forest Land use, and discusses the differences between these two methods by using the hypothetical data.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.2
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• pp.277-281
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• 2012

I study an optimal consumption and portfolio selection problem with regime-switching using a dynamic programming method. With constant relative risk aversion (CRRA) utility I obtain optimal solutions in closed-form.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.181-189
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• 2005

This paper suggest a method of inclusion probability proportional to size sampling that provides a non-negative and stable variance estimator. The sampling procedure is quite simple and flexible since a sampling design is easily obtained using mathematical programming. This scheme appears to be preferable to Nigam, Kumar and Gupta's (1984) method which uses a balanced incomplete block designs. A comparison is made with their method through an example in the literature.

• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1496-1506
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• 2003

The SIMP (solid isotropic material with penalization) approach is perhaps the most popular density variable relaxation method in topology optimization. This method has been very successful in many applications, but the optimization solution convergence can be improved when new variables, not the direct density variables, are used as the design variables. In this work, we newly propose S-shape functions mapping the original density variables nonlinearly to new design variables. The main role of S-shape function is to push intermediate densities to either lower or upper bounds. In particular, this method works well with nonlinear mathematical programming methods. A method of feasible directions is chosen as a nonlinear mathematical programming method in order to show the effects of the S-shape scaling function on the solution convergence.