| 书目名称 | Lagrange-type Functions in Constrained Non-Convex Optimization |
| 编辑 | Alexander Rubinov,Xiaoqi Yang |
| 视频video | http://file.papertrans.cn/581/580467/580467.mp4 |
| 丛书名称 | Applied Optimization |
| 图书封面 |  |
| 描述 | Lagrange and penalty function methods provide a powerful approach, both as a theoretical tool and a computational vehicle, for the study of constrained optimization problems. However, for a nonconvex constrained optimization problem, the classical Lagrange primal-dual method may fail to find a mini mum as a zero duality gap is not always guaranteed. A large penalty parameter is, in general, required for classical quadratic penalty functions in order that minima of penalty problems are a good approximation to those of the original constrained optimization problems. It is well-known that penaity functions with too large parameters cause an obstacle for numerical implementation. Thus the question arises how to generalize classical Lagrange and penalty functions, in order to obtain an appropriate scheme for reducing constrained optimiza tion problems to unconstrained ones that will be suitable for sufficiently broad classes of optimization problems from both the theoretical and computational viewpoints. Some approaches for such a scheme are studied in this book. One of them is as follows: an unconstrained problem is constructed, where the objective function is a convolution of the ob |
| 出版日期 | Book 2003 |
| 关键词 | Grad; Mathematica; applied mathematics; optimization |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4419-9172-0 |
| isbn_softcover | 978-1-4613-4821-4 |
| isbn_ebook | 978-1-4419-9172-0Series ISSN 1384-6485 |
| issn_series | 1384-6485 |
| copyright | Springer Science+Business Media Dordrecht 2003 |
|Archiver|手机版|小黑屋|
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