| 書目名稱 | Handbook of Variational Methods for Nonlinear Geometric Data | | 編輯 | Philipp Grohs,Martin Holler,Andreas Weinmann | | 視頻video | http://file.papertrans.cn/423/422345/422345.mp4 | | 概述 | As a first feature, the book provides a comprehensive collection of works describing the state of the art in their respective discipline. The benefit is a standard reference work for researchers (such | | 圖書封面 |  | | 描述 | .This book covers different, current research directions in the context of variational methods for non-linear geometric data. Each chapter is authored by leading experts in the respective discipline and provides an introduction, an overview and a description of the current state of the art...Non-linear geometric data arises in various applications in science and engineering. Examples of nonlinear data spaces are diverse and include, for instance, nonlinear spaces of matrices, spaces of curves, shapes as well as manifolds of probability measures. Applications can be found in biology, medicine, product engineering, geography and computer vision for instance...Variational methods on the other hand have evolved to being amongst the most powerful tools for applied mathematics. They involve techniques from various branches of mathematics such as statistics, modeling, optimization, numerical mathematics and analysis. The vast majority of research on variational methods, however, is focused on data in linear spaces. Variational methods for non-linear data is currently an emerging research topic.?..As a result, and since such methods involve various branches of mathematics, there is a pleth | | 出版日期 | Book 2020 | | 關(guān)鍵詞 | Geometric nonlinear data; manifold valued data; variational methods; total variation; denoising; optimiza | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-31351-7 | | isbn_softcover | 978-3-030-31353-1 | | isbn_ebook | 978-3-030-31351-7 | | copyright | Springer Nature Switzerland AG 2020 |
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