• Journal of the Korean Mathematical Society
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• v.40 no.2
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• pp.287-305
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• 2003

Second order necessary optimality condition for properly efficient solutions of a twice differentiable vector optimization problem is given. We obtain a nonsmooth version of the second order necessary optimality condition for properly efficient solutions of a nondifferentiable vector optimization problem. Furthermore, we prove a second order necessary optimality condition for weakly efficient solutions of a nondifferentiable vector optimization problem.

• Journal of English Language & Literature
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• v.55 no.3
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• pp.459-474
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• 2009

The Journal of English Language and Literature. The purpose of this study is to provide an optimality theoretic approach to NPIs (Negative Polarity Items) in English and Korean by proposing three universal constraints. The constraints are C-command Condition (CCC): NPI must be c-commanded by a constituent with negative meaning; Locality Condition (LOC): NPI must be bound in the local domain; Subjacency: NPI licensing must satisfy Subjacency Condition (SBJ); Previous analyses have shown that these three constraints control NPIs in one way or another. This study attempts to demonstrate that NPIs in both English and Korean languages can be nicely accounted for by setting a different constraint hierarchy for the two independent languages. That is, by slightly changing the constraint hierarchy, distributional differences of NPIs in both languages can be accounted straightforwardly within the framework of Optimality Theory.

• Journal of applied mathematics & informatics
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• v.28 no.5_6
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• pp.1535-1544
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• 2010

Sufficient optimality conditions for a class of generalized non-differentiable fractional optimization programming problems are established. Moreover, we prove the weak and strong duality theorems under (V, $\rho$)-invexity assumption.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.107-112
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• 1992

A slight extension of the classical saddle point and the global optimality condition has been discussed relative to some algorithmic implications. It also involves an economic interpretation which shows satisfying, rather than optimizing, decision making behavior under bounded rationality.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1157-1165
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• 2011

The Karush-Kuhn-Tucker (KKT) necessary optimality conditions for nonlinear differentiable programming problems are also sufficient under suitable convexity assumptions. The KKT conditions in multiobjective programming problems with interval-valued objective and constraint functions are derived in this paper. The main contribution of this paper is to obtain the Pareto optimal solutions by resorting to the sufficient optimality condition.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.386-390
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• 1992

Algorithmic or kinematic singularities are inevitably a introduced if optimality criteria or augmented kinematic equations are used to resolve the redundancy of almost any manipulator with rotary joints. In this paper, a sufficient condition for a singularity-free optimal solution of the kinematic control of a redundant manipulator is derived and, specifically, algorithmic singularities are analyzed for optimality-based methods. A singularity-free space (SFS) to characterize the performance of a secondary task for a redundant manipulator using the sufficient condition for a redundant manipulator is defined. The SFS is a set of regions classified by the loci of configurations satisfying the inflection condition for manipulability measure in the Configuration space. Using SFS, the topological property of the Configuration space and the invertible workspace without singularities are analyzed.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.425-434
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• 2006

The process of producing 1,3-preprandiol by microorganism continuous cultivation would attain its equilibrium state. How to get the highest concentration of 1,3-propanediol at that time is the aim for producers. Based on this fact, an optimization model is introduced in this paper, existence of optimal solution is proved. By infinite-dimensional optimal theory, the optimal condition of model is given and the equivalence between optimal condition and the zero of optimality function is proved.

• Journal of the Korean Mathematical Society
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• v.44 no.4
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• pp.971-985
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• 2007

A sequential optimality condition characterizing the efficient solution without any constraint qualification for an abstract convex vector optimization problem is given in sequential forms using subdifferentials and ${\epsilon}$-subdifferentials. Another sequential condition involving only the subdifferentials, but at nearby points to the efficient solution for constraints, is also derived. Moreover, we present a proposition with a sufficient condition for an efficient solution to be properly efficient, which are a generalization of the well-known Isermann result for a linear vector optimization problem. An example is given to illustrate the significance of our main results. Also, we give an example showing that the proper efficiency may not imply certain closeness assumption.

• Journal of the Korean Mathematical Society
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• v.43 no.2
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• pp.241-258
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• 2006

This paper gives conditions under which necessary optimality conditions in a locally Lipschitz program can be expressed as the invexity of the active constraint functions or the type I invexity of the objective function and the constraint functions on the feasible set of the program. The results are nonsmooth extensions of those of Hanson and Mond obtained earlier in differentiable case.

• Bulletin of the Korean Mathematical Society
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• v.23 no.2
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• pp.141-148
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• 1986

In [1], M. Guignard considered a constraint set in a Banach space, which is similar to that in [2] and gave a first order necessary optimality condition which generalized the Kuhn-Tucker conditions [3]. Sufficiency is proved for objective functions which is either pseudoconcave [5] or quasi-concave [6] where the constraint sets are taken pseudoconvex. In this note, we consider a psedoconvex programming problem in a Hilbert space. Constraint set in a Hillbert space being pseudoconvex and the objective function is restrained by an operator equation. Then we use the methods similar to that in [1] and [6] to obtain a necessary and sufficient optimality condition.