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Titlebook: System Modelling and Optimization; Proceedings of the 1 Masao Iri,Keiji Yajima Conference proceedings 1988 Springer-Verlag London 1988 Sess

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樓主: DUCT
21#
發(fā)表于 2025-3-25 06:02:08 | 只看該作者
Lecture Notes in Control and Information Scienceshttp://image.papertrans.cn/t/image/884492.jpg
22#
發(fā)表于 2025-3-25 10:50:01 | 只看該作者
23#
發(fā)表于 2025-3-25 13:59:04 | 只看該作者
24#
發(fā)表于 2025-3-25 18:32:58 | 只看該作者
System Modelling and Optimization978-3-540-39164-7Series ISSN 0170-8643 Series E-ISSN 1610-7411
25#
發(fā)表于 2025-3-25 20:19:05 | 只看該作者
26#
發(fā)表于 2025-3-26 00:57:31 | 只看該作者
27#
發(fā)表于 2025-3-26 07:48:52 | 只看該作者
Newton-type algorithms with nonmonotone line search for large-scale unconstrained optimization,sed employ a nonmonotone steplength selection rule along the search direction which is determined by means of a Truncated-Newton algorithm. Numerical results obtained for a set of test problems are reported.
28#
發(fā)表于 2025-3-26 10:02:30 | 只看該作者
Conference proceedings 1988and Optimization, Tokyo, Japan, August 31 - September 4, 1987, as well as 4 papers for the plenary sessions of the Conference. The aim of the conference was to discuss recent advances in the mathematical representation of engineering, sociotechnical, socioeconomic systems as well as in the optimization of their performances."
29#
發(fā)表于 2025-3-26 14:30:16 | 只看該作者
Chandrasekahr filtering and smoothing methods given covariance model and properties of X and Y funcon. In addition, another type of Chandrasekhar‘s X- and Y-functions that is different from the original ones in terms of numerical stability and accuracy and the corresponding Riccati differential equations systems are presented. Similar formulas are also given for this type of Chandrasekhar‘s X- and Y-functions.
30#
發(fā)表于 2025-3-26 18:10:48 | 只看該作者
Centered newton method for mathematical programming,lso given by applying the Newton method to a projected system of the complementarity equations. These two directions guide the generated sequence of the approximations towards the solution and the center variety respectively. A class of ‘penalized norms’ and ‘guiding cones’ is also introduced for choosing step lengths in bivariate search.
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