• Journal of applied mathematics & informatics
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• v.6 no.3
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• pp.747-756
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• 1999

The Newtom method and the quasi-Newton method for solving equations of smooth compositions of semismooth functions are proposed. The Q-superlinear convergence of the Newton method and the Q-linear convergence of the quasi-Newton method are proved. The present methods can be more easily implemeted than previous ones for this class of nonsmooth equations.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.7 no.1
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• pp.7-14
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• 2003

The main concern of this paper is to develop a new class of quasi-newton methods. These methods are intended for use whenever memory space is a major concern and, hence, they are usually referred to as limited memory methods. The methods developed in this work are sensitive to the choice of the memory parameter ${\eta}$ that defines the amount of past information stored within the Hessian (or its inverse) approximation, at each iteration. The results of the numerical experiments made, compared to different choices of these parameters, indicate that these methods improve the performance of limited memory quasi-Newton methods.

• Journal of the Korean Mathematical Society
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• v.33 no.4
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• pp.865-878
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• 1996

We consider numerical methods for finding a solution to a nonlinear system of algebraic equations $$ (1) f(x) = 0, $$ where the function $f : R^n \to R^n$ is ain $x \in R^n$. In [10], a quasi-Gauss-Newton method is proposed and shown the computational efficiency over SQRT algorithm by numerical experiments. The convergence rate of the method has not been proved theoretically. In this paper, we show theoretically that the iterate $x_k$ obtained from the quasi-Gauss-Newton method for the problem (1) actually converges to a root by Kantorovich-type convergence analysis. We also show the rate of convergence of the method is superlinear.

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.491-502
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• 1994

Many problems arising in science and engineering require the numerical solution of a system of n nonlinear equations in n unknowns: (1) given F : $R^{n}$ $\rightarrow$ $R^{n}$ , find $x_{*}$ $\epsilon$ $R^{n}$ / such that F($x_{*}$) = 0. Nonlinear problems are generally solved by iteration. Davidson [3] and Broyden [1] introduced the methods which had led to a large amount of research and a class of algorithm. This work has been called by the quasi-Newton methods, secant updates, or modification methods. Newton's method is the classical method for the problem (1) and quasi-Newton methods have been proposed to circumvent computational disadvantages of Newton's method.(omitted)

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1053-1058
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• 2017

In this paper, the design of iron loss minimization of 600W was performed by using Quasi-Newton method. Stator shoe, the width of stator teeth and yoke, and the length of d-axis flux path were selected as design parameters, and the output characteristics according to each design variable were considered. The objective function was set to minimize iron loss. Using the Quasi-Newton method, the variables converged to the target value while changing simultaneously and multiple times. As the algorithm advanced optimization, the correlation with the behavior of each variable was compared and analyzed.

• 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.8 no.3
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• pp.743-753
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• 2001

We provide local convergence results in affine form for inexact Newton-like as well as quasi-Newton iterative methods in a Banach space setting. We use hypotheses on the second or on the first and mth Frechet-derivative (m≥2 an integer) of the operator involved. Our results allow a wider choice of starting points since our radius of convergence can be larger than the corresponding one given in earlier results using hypotheses on the first-Frechet-derivative only. A numerical example is provided to illustrate this fact. Our results apply when the method is, for example, a difference Newton-like or update-Newton method. Furthermore, our results have direct applications to the solution of autonomous differential equations.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.15 no.4
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• pp.267-275
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• 2011

This paper proposes a new quickly convergent pattern search quasi-Newton algorithm that employs the multi-step version of the Symmetric Rank One (SRI). The new algorithm works on the factorizations of the inverse Hessian approximations to make available a sequence of convergent positive bases required by the pattern search process. The algorithm, in principle, resembles that developed in [1] with multi-step methods dominating the dervation and with numerical improvements incurred, as shown by the numerical results presented herein.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.666-671
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• 2010

Multi-layer perceptron neural networks have been constructed to classify four types of defects in steam generator tubes. Three features are extracted from the signals of the eddy current testing method. These include maximum impedance, phase angle at the point of maximum impedance, and an angle between the point of maximum impedance and the point of half the maximum impedance. Two hundred sets of these features are used for training and assessing the networks. Two approaches are involved to train the networks and to classify the defect type. One is the conjugate gradient method and the other is the Broydon-Fletcher-Goldfarb-Shanno method which is recognized as the most popular algorithm of quasi-Newton methods. It is found from the computation results that the training time of the Broydon-Fletcher-Goldfarb-Shanno method is much faster than that of the conjugate gradient method in most cases. On the other hand, no significant difference of the classification performance between the two methods is observed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1292-1297
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• 2018

In this paper, efficiency optimization design of 600W class IPMSM was performed by using Quasi-Newton method. The output was limited to 600W to meet the same output as the basic model. The behavior of each variable as the design progressed was judged on the efficiency, which is the target value through correlation analysis. The design variables were set as the width of the stator, the position of the permanent magnet from the end of the rotor, the thickness of the permanent magnet, and the width of the permanent magnet.