• 한국항공우주학회지
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• 제35권3호
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• pp.212-217
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• 2007

Alienor method는 전역적 최적화 문제들을 해결하기위한 효과적인 방법이다. 이 방법은 다변수 문제를 한 개의 변수에 의존하는 문제로 변환시킨다. 어떠한 일차원 전역 최적화 방법도 변환된 문제의 해결에 사용할 수 있다. Alienor method와 연결된 여러 가지의 일차원 전역 최적화 방법들이 수학적으로 제안되었으며, 제안된 방법들은 예제를 통하여 성공적으로 증명되었다. 그러나 실제 엔지니어링 문제에 이 방법들을 적용하기에는 여러 가지 문제가 있다. 본 논문에서는 Lipschitz 상수를 사용하지 않는 Lipschitzian 최적화 방법이 Alienor method와 결합되었고, 이 결합된 최적화 알고리즘을 예제에 적용하였다. 본 예제를 통하여 제안된 방법이 보다 우수하게 전역적 최적화 문제에 적용 가능함을 보였다

• Journal of applied mathematics & informatics
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• 제28권1_2호
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• pp.431-438
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• 2010

In this paper, using nonsmooth analysis, we established equivalence results between a locally Lipschitz vector optimization problem and its associated approximated problem under the proper efficiency.

• 대한수학회논문집
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• 제16권4호
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• pp.667-677
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• 2001

We consider a nonsmooth multiobjective opimization problem(PE) involving locally Lipschitz functions and define gen-eralized invexity for locally Lipschitz functions. Using Fritz John type optimality conditions, we establish Fritz John type sufficient optimality theorems for (PE) under generalized invexity.

• Journal of applied mathematics & informatics
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• 제15권1_2호
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• pp.313-321
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• 2004

In this paper we study the problem of three-dimensional layout optimization on the simplified rotating vessel of satellite. The layout optimization model with behavioral constraints is established and some effective and convenient conditions of performance optimization are presented. Moreover, we prove that the performance objective function is locally Lipschitz continuous and the results on the relations between the local optimal solution and the global optimal solution are derived.

• 충청수학회지
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• 제30권1호
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• pp.31-40
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• 2017

In this paper, we consider a nonsmooth multiobjective robust optimization problem with more than two locally Lipschitz objective functions and locally Lipschitz constraint functions in the face of data uncertainty. We prove a nonsmooth sufficient optimality theorem for a weakly robust efficient solution of the problem. We formulate a Wolfe type dual problem for the problem, and establish duality theorems which hold between the problem and its Wolfe type dual problem.

• 충청수학회지
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• 제37권1호
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• pp.41-55
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• 2024

The nonconvex and nonsmooth optimization problem has been widely applicable in image processing and machine learning. In this paper, we propose an extension of the proximal iteratively reweighted ℓ₁ algorithm for nonconvex and nonsmooth minmization problem. We assume the co-coerciveness of a term of objective function instead of Lipschitz gradient condition, which is generalized property of Lipschitz continuity. We prove the global convergence of the proposed algorithm. Numerical results show that the proposed algorithm converges faster than original proximal iteratively reweighed algorithm and existing algorithms.

• 제어로봇시스템학회논문지
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• 제22권6호
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• pp.411-416
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• 2016

This paper presents the design methodology of an unknown input observer for Lipschitz nonlinear systems with unknown inputs in the framework of convex optimization. We use an unknown input observer (UIO) to consider both nonlinearity and disturbance. By deriving a sufficient condition for exponential stability in the linear matrix inequality (LMI) form, existence of a stabilizing observer gain matrix of UIO will be assured by checking whether the quadratic stability margin of the error dynamics is greater than the Lipschitz constant or not. If quadratic stability margin is less than a Lipschitz constant, the coordinate transformation may be used to reduce the Lipschitz constant in the new coordinates. Furthermore, to reduce the maximum singular value of the observer gain matrix elements, an object function to minimize it will be optimally designed by modifying its magnitude so that amplification of sensor measurement noise is minimized via multi-objective optimization algorithm. The performance of UIO is compared to a nonlinear observer (Luenberger-like) with an application to a flexible joint robot system considering a change of load and disturbance. Finally, it is validated via simulations that the estimated angular position and velocity provide true values even in the presence of unknown inputs.

• Journal of applied mathematics & informatics
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• 제28권5_6호
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• pp.1527-1534
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• 2010

In this paper, we consider nonsmooth optimization problem of which objective and constraint functions are locally Lipschitz. We establish sufficient optimality conditions and duality results for nonsmooth vector optimization problem given under nearly strict invexity and near invexity-infineness assumptions.

• 대한수학회보
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• 제51권1호
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• pp.287-301
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• 2014

In this paper, we prove a necessary optimality theorem for a nonsmooth optimization problem in the face of data uncertainty, which is called a robust optimization problem. Recently, the robust optimization problems have been intensively studied by many authors. Moreover, we give examples showing that the convexity of the uncertain sets and the concavity of the constraint functions are essential in the optimality theorem. We present an example illustrating that our main assumptions in the optimality theorem can be weakened.

• Journal of applied mathematics & informatics
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• 제23권1_2호
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• pp.353-359
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• 2007

A necessary and sufficient condition for Demyanov difference and Minkowski difference of compact convex subsets in $R^2$ being equal is given in this paper. Several examples are computed by Matlab to test our result. The necessary and sufficient condition makes us to compute Clarke subdifferential by quasidifferential for a special of Lipschitz functions.