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2004 Volume 14 Issue 1 Pages
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Article type: Cover
2004 Volume 14 Issue 1 Pages
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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[in Japanese]
Article type: Article
2004 Volume 14 Issue 1 Pages
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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Tomohiro SOGABE, Cheng-Hai JIN, Kuniyoshi ABE, Shao-Liang ZHANG
Article type: Article
2004 Volume 14 Issue 1 Pages
1-12
Published: March 25, 2004
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The Conjugate Gradient Squared method (CGS) has been proposed for solving large and sparse non-Hermitian linear systems, and it is well known that the CGS method may lead to a rather irregular convergence behaviour. In this paper, a Stabilized Conjugate Gradient Squared method (SCGS) is proposed as a variant of the CGS method. Numerical experiments indicate that the SCGS method is often more efficient than the CGS method.
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Chiaki HIROTA, Kazufumi OZAWA
Article type: Article
2004 Volume 14 Issue 1 Pages
13-38
Published: March 25, 2004
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A numerical method is proposed for estimating the blow-up time and blow-up rate of the solution of the system of ordinary differential equations (ODEs), whose solution has a pole at a finite time, that is, the blow-up time. The main idea is to transform the ODE system into a tractable form by Moriguti's technique, and to generate a linearly convergent sequence to the blow-up time. The sequence is accelerated by the Aitken Δ^2 method. The present method is applied to the problem of finding the blow-up time of the solution of partial differential equations (PDEs), by discretizing the PDEs in space. Numerical experiments on the two PDEs, the semilinear reaction-diffusion equation and the heat equation with a nonlinear boundary condition, show the validity of the present method.
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Yasutoshi Yajima, Tetsuo Abe
Article type: Article
2004 Volume 14 Issue 1 Pages
39-57
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We are going to propose a framework for extracting informative or redundant features associated with the nonlinear discriminators generated by support vector machine. We use the idea of decision boundary analysis introduced by Lee and Landgrebe. The quality of this analysis depends on the calculations of the points, or the associated gradient vectors, which are lying on the decision boundary. Exploiting the special structure of the kernel induced feature space, we show that the gradients on the boundary are easily obtained. Numerical experiments for some artificial datasets demonstrate that both informative and redundant features can be identified clearly. Also, we show that the performance of the discriminator can be improved by discarding the redundant features of a real world dataset.
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Xiao-Ming NIU, Tetsuya SAKURAI, Hiroshi SUGIURA
Article type: Article
2004 Volume 14 Issue 1 Pages
59-73
Published: March 25, 2004
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A problem of finding a validated polynomial factor of an analytic function is considered. A validated factorization method was presented by Sakurai and Sugiura in which the bounds of Taylor coefficients of the function was assumed to be available. In this paper, we use complex circular arithmetic to perform the calculation of validated bounds for the Taylor coefficients of a function. Numerical examples show the efficiency of the presented method.
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Masahiro Ishii, May Arima
Article type: Article
2004 Volume 14 Issue 1 Pages
75-89
Published: March 25, 2004
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Executive stock options are used widely in compasation plans. A typical stock option has a fixed strike price. But it has attracted heavy criticism that it unfairly rewards executives for the market's success instead of their own efforts. Therefore, in this paper, we propose two geometric averaged stock options, which have a strike price moving with a benchmark to filter out marketwide factors beyond the executive's control, and evaluate his managerial ability in both up and down markets. We processe as follows: First, we derive a pricing model for the stock options. Then we investigate the values compared to taditional and another indexed stock options. Last, we consider the incentive effect peculiar to the geometric averaged stock options.
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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Article type: Appendix
2004 Volume 14 Issue 1 Pages
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Article type: Cover
2004 Volume 14 Issue 1 Pages
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Article type: Cover
2004 Volume 14 Issue 1 Pages
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