| 書目名稱 | Numerical Nonsmooth Optimization | | 副標題 | State of the Art Alg | | 編輯 | Adil M. Bagirov,Manlio Gaudioso,Sona Taheri | | 視頻video | http://file.papertrans.cn/670/669144/669144.mp4 | | 概述 | Provides a comprehensive coverage of both traditional and more advanced nonsmooth optimization methods.Gathers for the first time the founding fathers and mothers of the respective nonsmooth optimizat | | 圖書封面 |  | | 描述 | .Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO.?..The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods. In turn, the second focuses on methods that exploit the problem’s special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO..Given its scope, the book isideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intellige | | 出版日期 | Book 2020 | | 關(guān)鍵詞 | Bundle methods; Nondifferentiable optimization; Nonsmooth analysis; Subgradient methods; Test problems; P | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-34910-3 | | isbn_softcover | 978-3-030-34912-7 | | isbn_ebook | 978-3-030-34910-3 | | copyright | Springer Nature Switzerland AG 2020 |
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