• Journal of applied mathematics & informatics
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• v.10 no.1_2
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• pp.157-165
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• 2002

The fuzzification of convex sets in median algebras is considered, and some of their properties are investigated. A characterization of finite valued fuzzy convex set is given.

• Journal of the Chungcheong Mathematical Society
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• v.29 no.1
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• pp.29-35
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• 2016

In this paper, we consider a convex optimization problem with a convex integrable objective function and a geometric constraint set. We characterize the solution set of the problem when we know its one solution.

• Journal of the Chungcheong Mathematical Society
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• v.36 no.4
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• pp.279-288
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• 2023

We obtain a Chandrabhan type fixed point theorem for a multimap having a non-compact domain and a weakly closed graph, and taking convex values only on a quasi-convex subset of Hausdorff locally convex topological vector space. We introduce the definition of Chandrabhan-set and find a sufficient condition for every countably condensing multimap to have a relatively compact Chandrabhan-set. Finally, we establish a new version of Sadovskii fixed point theorem for multimaps.

• Journal of applied mathematics & informatics
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• v.29 no.1_2
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• pp.517-521
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• 2011

In this paper, we introduce the concepts of m-convex set, mcconvex function and $m^*c$-convex function. We study basic properties for m-convex sets and characterization for such functions.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.375-381
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• 2008

Boundedness for the set of all the $\epsilon$-approximate solutions for convex optimization problems are considered. We give necessary and sufficient conditions for the sets of all the $\epsilon$-approximate solutions of a convex optimization problem involving finitely many convex functions and a convex semidefinite problem involving a linear matrix inequality to be bounded. Furthermore, we give examples illustrating our results for the boundedness.

• Journal of IKEEE
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• v.17 no.1
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• pp.29-35
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• 2013

In this paper, we suggest an improved Convex Hull algorithm considering sort in plane point set. This algorithm has low computational complexity since processing data are reduced by characteristic of extreme points. Also it obtains a complete convex set with just one processing using an convex vertex discrimination criterion. Initially it requires sorting of point set. However we can't quickly sort because of its heavy operations. This problem was solved by replacing value and index. We measure the execution time of algorithms by generating a random set of points. The results of the experiment show that it is about 2 times faster than the existing algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.1
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• pp.1-6
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• 2014

Most of the previous algorithms focus on computing the convex hull for a set of points. In this paper, we present a method for approximating the convex hull for a set of spheres with various radii in discrete space. Computing the convex hull for a set of spheres is a base technology for many applications that study structural properties of molecules. We present a voxel map data structures, where the molecule is represented as a set of spheres, and corresponding algorithms. Based on CUDA programming for using the parallel architecture of GPU, our algorithm takes less than 40ms for computing the convex hull of 6,400 spheres in average.

• Communications of the Korean Mathematical Society
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• v.23 no.2
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• pp.251-256
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• 2008

In this paper, we introduce the concept of the semi-preconvex set on preconvexity spaces. We study some properties for the semi-preconvex set. Also we introduce the concepts of the sc-convex function and $s^*c$-convex function. Finally, we characterize sc-convex functions, $s^*$-convex functions and semi-preconvex sets by using the co-convexity hull and the convexity hull.

• Journal of the Chungcheong Mathematical Society
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• v.4 no.1
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• pp.55-59
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• 1991

We consider perturbation problems and Lagrangians for convex set function optimization problems. In particular, we prove that the Lagrangian $L({\Omega},y)$ is a convex set function in ${\Omega}$ for each y if the perturbation function is convex.

• Journal of the Chungcheong Mathematical Society
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• v.23 no.4
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• pp.629-633
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• 2010

In this paper we provide a sharp lower bound of the first Neumann eigenvalue of a compact hypersurface $\Sigma$ inside a convex set C in a Riemannian manifold under the assumption that ${\partial}{\Sigma}$ meets ${\partial}C$ orthogonally.