• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.165-177
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• 2005

In this paper, an adaptive trust region method based on the conic model for unconstrained optimization problems is proposed and analyzed. We establish the global and super linear convergence results of the method. Numerical tests are reported that confirm the efficiency of the new method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.12
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• pp.938-946
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• 2003

Length of an unconstrained viscoelastic damping layer on beams is determined to maximizeloss factor using a numerical search method. The fractional derivative model can describe damping characteristics of viscoelastic damping materials accurately, and is used to represent nonlinearity of complex modulus with frequencies and temperatures. Equivalent flexural rigidity of the unconstrained beam is obtained using Ross, Ungar, Kelvin[RUK] equation. The loss factors of partially covered unconstrained beam are calculated by a modal strain energy method. Optimal lengths of the unconstrained viscoelastic damping layer of beams are identified with ambient temperatures and thickness ratios of beam and damping layer by using a finite-difference-based steepest descent method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.665-671
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• 2003

Length of an unconstrained viscoelastic damping layer on beams is determined to maximize loss factor using a numerical search method. The fractional derivative model can describe damping characteristics of the viscoelastic damping material, and is used to represent nonlinearity of complex modulus with frequencies and temperatures. Equivalent flexural rigidity of the unconstrained beam is obtained using Ross, Ungar, Kerwin(RUK) equation. The loss factors of partially covered unconstrained beam are calculated by a modal strain energy method. Optimal lengths of the unconstrained viscoelastic damping layer of beams are obtained with respect to ambient temperatures and thickness ratios of beam and damping layer.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.656-661
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• 2005

An optimization formulation of unconstrained damping treatment on beams is proposed to minimize vibration responses using a numerical search method. The fractional derivative model is combined with RUK's equivalent stiffness approach in order to represent nonlinearity of complex modulus of damping materials with frequency and temperature. The loss factors of partially covered unconstrained beam are calculated by the modal strain energy method. Vibration responses are calculated by using the modal superposition method, and of which design sensitivity formula with respect to damping layout is derived analytically. Plugging the sensitivity formula into optimization software, we can determine optimally damping treatment region that gives minimum forced response under a given boundary condition. A numerical example shows that the proposed method is very effective in minimizing vibration responses with unconstrained damping layer treatment.

• Journal of the Korean Mathematical Society
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• v.47 no.4
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• pp.767-788
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• 2010

In this paper, we propose two limited memory BFGS algorithms with a nonmonotone line search technique for unconstrained optimization problems. The global convergence of the given methods will be established under suitable conditions. Numerical results show that the presented algorithms are more competitive than the normal BFGS method.

• Journal of the Korean Mathematical Society
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• v.47 no.6
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• pp.1253-1267
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• 2010

This paper considers the unconstrained global optimization with the revised filled function methods. The minimization sequence could leave from a local minimizer to a better minimizer of the objective function through minimizing an auxiliary function constructed at the local minimizer. Some promising numerical results are also included.

• Bulletin of the Korean Mathematical Society
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• v.50 no.1
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• pp.73-81
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• 2013

In this paper, an efficient hybrid nonlinear conjugate gradient method is proposed to solve general unconstrained optimization problems on the basis of CD method [2] and DY method [5], which possess the following property: the sufficient descent property holds without any line search. Under the Wolfe line search conditions, we proved the global convergence of the hybrid method for general nonconvex functions. The numerical results show that the hybrid method is especially efficient for the given test problems, and it can be widely used in scientific and engineering computation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.71-79
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• 1999

We consider multi-step quasi-Newton methods for unconstrained optimization. These methods were introduced by Ford and Moghrabi [1, 2], who showed how interpolating curves could be used to derive a generalization of the Secant Equation (the relation normally employed in the construction of quasi-Newton methods). One of the most successful of these multi-step methods makes use of the current approximation to the Hessian to determine the parameterization of the interpolating curve in the variable-space and, hence, the generalized updating formula. In this paper, we investigate new parameterization techniques to the approximate Hessian, in an attempt to determine a better Hessian approximation at each iteration and, thus, improve the numerical performance of such algorithms.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.193-208
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• 2006

A modified BFGS algorithm for solving the unconstrained optimization, whose Hessian matrix at the minimum point of the convex function is of rank defects, is presented in this paper. The main idea of the algorithm is first to add a modified term to the convex function for obtain an equivalent model, then simply the model to get the modified BFGS algorithm. The superlinear convergence property of the algorithm is proved in this paper. To compared with the Tensor algorithms presented by R. B. Schnabel (seing [4],[5]), this method is more efficient for solving singular unconstrained optimization in computing amount and complication.

• Nonlinear Functional Analysis and Applications
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• v.26 no.4
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• pp.811-837
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• 2021

In this paper, we propose a new conjugate gradient method for solving unconstrained optimization models. By using exact and strong Wolfe line searches, the proposed method possesses the sufficient descent condition and global convergence properties. Numerical results show that the proposed method is efficient at small, medium, and large dimensions for the given test functions. In addition, the proposed method was applied to solve practical application problems in portfolio selection.