| 书目名称 | Order Analysis, Deep Learning, and Connections to Optimization | | 编辑 | Johannes Jahn | | 视频video | | | 概述 | Introduces order analysis in the context of optimization, pioneering new insights.Highlights deep learning from an optimization perspective.Helps readers to deal with order structures and deep learnin | | 丛书名称 | Vector Optimization | | 图书封面 |  | | 描述 | .This book introduces readers to order analysis and various aspects of deep learning, and describes important connections to optimization, such as nonlinear optimization as well as vector and set optimization. Besides a review of the essentials, this book consists of two main parts..The first main part focuses on the introduction of order analysis as an application-driven theory, which allows to treat order structures with an analytical approach. Applications of order analysis to nonlinear optimization, as well as vector and set optimization with fixed and variable order structures, are discussed in detail. This means there are close ties to finance, operations research, and multicriteria decision making..Deep learning is the subject of the second main part of this book. In addition to the usual basics, the focus is on gradient methods, which are investigated in the context of complex models with a large number of parameters. And a new fast variant of a gradient method is presented in this part. Finally, the deep learning approach is extended to data sets given by set-valued data. Although this set-valued approach is more computationally intensive, it has the advantage of producing | | 出版日期 | Book 2024 | | 关键词 | Order theory; Nonlinear optimization; Vector optimization; Set optimization; Deep learning; Gradient meth | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-67422-8 | | isbn_softcover | 978-3-031-67424-2 | | isbn_ebook | 978-3-031-67422-8Series ISSN 1867-8971 Series E-ISSN 1867-898X | | issn_series | 1867-8971 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 16:52:01
