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Titlebook: Advances in Nonlinear Programming; Proceedings of the 9 Ya-xiang Yuan Conference proceedings 19981st edition Kluwer Academic Publishers 199

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41#
發(fā)表于 2025-3-28 15:49:21 | 只看該作者
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發(fā)表于 2025-3-28 22:45:59 | 只看該作者
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發(fā)表于 2025-3-29 00:32:53 | 只看該作者
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發(fā)表于 2025-3-29 04:53:47 | 只看該作者
45#
發(fā)表于 2025-3-29 07:50:09 | 只看該作者
Genetic Approach to a Visual System,inimization remains a difficult open problem..In this paper, we review and discuss the results obtained with a new technique based on an extension of the Wolfe line-search used in unconstrained optimization. The idea is to follow a piecewise linear path approximating a smooth curve, along which some
46#
發(fā)表于 2025-3-29 11:55:12 | 只看該作者
47#
發(fā)表于 2025-3-29 16:40:11 | 只看該作者
H. Autrum,M. F. Bennett,M. Yoshida,H. Autrumequation-solving that have found fundamental new expression within the context of linear programming. We given an overview of interior-point and infeasible-interior-point LP algorithms from this perspective, concentrating on their underlying algebraic and geometric aspects. We formulate the directio
48#
發(fā)表于 2025-3-29 22:06:44 | 只看該作者
Physics of Vision in Compound Eyes,s, our algorithm does not resolve the subproblem if the trial step results in an increase in the objective function, but instead performs a backtracking line search from the failed point. Backtracking can be done along a straight line or along a curved path. We show that the new algorithm preserves
49#
發(fā)表于 2025-3-30 01:35:59 | 只看該作者
Edwin R. Price,Teresa G. Valencakon with a trust region. We then minimize this local model in order to find the next approximate minimizer. Asymptotically, finding the local minimizer of the quadratic model is equivalent to applying Newton’s method to the stationarity condition..For constrained problems, the local quadratic model c
50#
發(fā)表于 2025-3-30 06:35:28 | 只看該作者
H. Irisawa,A. Irisawa,N. Shigeto. The first can be called “sufficient reduction methods” where the condition for accepting a new point is a sufficient reduction in the merit function. The other can be called “simple reduction” methods where they accept a new point as long as it reduces the merit function. In general, it can be sho
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