• Journal of applied mathematics & informatics
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• v.30 no.5_6
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• pp.759-772
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• 2012

In this paper, semi-infinite constrained programming, a class of constrained nonsmooth optimization problems, are transformed into unconstrained nonsmooth convex programs under the help of exact penalty function. The unconstrained objective function which owns the primal-dual gradient structure has connection with $\mathcal{VU}$-space decomposition. Then a $\mathcal{VU}$-space decomposition method can be applied for solving this unconstrained programs. Finally, the superlinear convergence algorithm is proved under certain assumption.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.425-429
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• 1987

This paper describes a collision avoidance method for planning safe trajectories for multi-robot system in common work space. Usually objects have been approximated to convex polyhedra in most previous researches, but in case using such the approximation method it is difficult to represent objects analytically in terms of functions and also to describe tile relationship between the objects. In this paper, in order to solve such problems a modeling method which approximates objects to cylinder ended by hemispheres and or sphere is used and the maximum distance functions is defined which call be calculated simply. Using an objective function with inequality constraints which are related to minimum distance functions, work range and maximum allowable angular velocities of the robots, tile collision avoidance for two robots is formulated to a constrained function optimization problem. With a view to solve tile problem a penalty function having simple form is defined and used. A simple numerical example involving two PUMA-type robots is described.

• East Asian mathematical journal
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• v.32 no.1
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• pp.47-59
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• 2016

The purpose of the present paper is to introduce and investigate two new subclasses of bi-univalent functions of complex order defined in the open unit disk, which are associated with Hurwitz-Lerch zeta function and satisfying subordinate conditions. Furthermore, we find estimates on the Taylor-Maclaurin coefficients ${\mid}a_2{\mid}$ and ${\mid}a_3{\mid}$ for functions in the new subclasses. Several (known or new) consequences of the results are also pointed out.

• Kyungpook Mathematical Journal
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• v.45 no.3
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• pp.395-404
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• 2005

Let α be a complex number with 𝕽α > 0. Let the functions f and g be analytic in the unit disc E = {z : |z| < 1} and normalized by the conditions f(0) = g(0) = 0, f'(0) = g'(0) = 1. In the present article, we study the differential subordinations of the forms $${\alpha}{\frac{z^2f^{{\prime}{\prime}}(z)}{f(z)}}+{\frac{zf^{\prime}(z)}{f(z)}}{\prec}{\alpha}{\frac{z^2g^{{\prime}{\prime}}(z)}{g(z)}}+{\frac{zg^{\prime}(z)}{g(z)}},\;z{\in}E,$$ and $${\frac{z^2f^{{\prime}{\prime}}(z)}{f(z)}}{\prec}{\frac{z^2g^{{\prime}{\prime}}(z)}{g(z)}},\;z{\in}E.$$ As consequences, we obtain a number of sufficient conditions for star likeness of analytic maps in the unit disc. Here, the symbol ' ${\prec}$ ' stands for subordination

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.3
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• pp.107-116
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• 2021

We introduce optimization algorithms using Bregman Divergence for solving non-negative matrix factorization (NMF) problems. Bregman divergence is known a generalization of some divergences such as Frobenius norm and KL divergence and etc. Some algorithms can be applicable to not only NMF with Frobenius norm but also NMF with more general Bregman divergence. Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used. We develop the Bregman proximal gradient method applicable for all NMF formulated in any Bregman divergences. In the derivation of NMF algorithm for Bregman divergence, we need to use majorization/minimization(MM) for a proper auxiliary function. We present algorithmic aspects of NMF for Bregman divergence by using MM of auxiliary function.

• Bulletin of the Korean Mathematical Society
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• v.35 no.3
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• pp.533-545
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• 1998

In this paper, we prove the existence of solutions of the heat equation for harmonic map on a compact manifold with a boundary when the target manifold is allowed to have positively curved parts.

• Honam Mathematical Journal
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• v.39 no.3
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• pp.331-347
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• 2017

The aim of the present paper is to introduce a new subclass of meromorphic functions with positive coefficients defined by a certain integral operator and a necessary and sufficient condition for a function f to be in this class. We obtain coefficient inequality, meromorphically radii of close-to-convexity, starlikeness and convexity, convex linear combinations, Hadamard product and integral transformation for the functions f in this class.

• Communications of the Korean Mathematical Society
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• v.11 no.4
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• pp.1105-1116
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• 1996

A partial ordering is developed among weakly positive quadrant dependent (WPQD) bivariate random vectors. This permits us to measure the degree of WPQD-ness and to compare pairs of WPQD random vectors. Some properties and closures under certain statistical operations are derived. An application is made to measures of dependence such as Kendall's $\tau$ and Spearman's $\rho$.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.24-30
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• 1988

We study the optimal allocation of machines and pallets in a class of manufacturing systems. The FMS is modeled as a closed queueing network with balanced loading of the stations. An Algorithm is developed, which exploits the properties of the throughput function and solves the allocation problem for increasing concave profit and convex cost. We also study the more general case of allocating machines and pallets among a set of FMSs. A dynamic programming approach is developed, which solves the problem with O(M$^{3}$N$^{2}$) operations.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.8 no.2
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• pp.7-13
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• 2004

A Conjugate Gradient (CG) algorithm for unconstrained minimization is proposed which is invariant to a nonlinear scaling of a strictly convex quadratic function and which generates mutually conjugate directions for extended quadratic functions. It is derived for inexact line searches and is designed for the minimization of general nonlinear functions. It compares favorably in numerical tests with the original Dixon algorithm on which the new algorithm is based.