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Titlebook: Linear and Mixed Integer Programming for Portfolio Optimization; Renata Mansini,W?odzimierz Ogryczak,M. Grazia Sper Textbook 2015 Springer

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發(fā)表于 2025-3-21 17:34:19 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Linear and Mixed Integer Programming for Portfolio Optimization
編輯Renata Mansini,W?odzimierz Ogryczak,M. Grazia Sper
視頻videohttp://file.papertrans.cn/587/586457/586457.mp4
概述Focuses on finance, a field of application of growing interest for the operational research community.Presents one of the first graduate texts where OR methods are applied to portfolio analysis.Provid
叢書名稱EURO Advanced Tutorials on Operational Research
圖書封面Titlebook: Linear and Mixed Integer Programming for Portfolio Optimization;  Renata Mansini,W?odzimierz Ogryczak,M. Grazia Sper Textbook 2015 Springer
描述.This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples..
出版日期Textbook 2015
關(guān)鍵詞Binary variables; Financial modeling; Integer variables; Markowitz; Risk management; Transaction cost; sto
版次1
doihttps://doi.org/10.1007/978-3-319-18482-1
isbn_softcover978-3-319-38621-8
isbn_ebook978-3-319-18482-1Series ISSN 2364-687X Series E-ISSN 2364-6888
issn_series 2364-687X
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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發(fā)表于 2025-3-21 21:12:31 | 只看該作者
Renata Mansini,W?odzimierz Ogryczak,M. Grazia Speranzanded to be a short introduction of deep learning basics through the example of a practical implementation case...The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journe
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發(fā)表于 2025-3-22 00:34:01 | 只看該作者
Renata Mansini,W?odzimierz Ogryczak,M. Grazia Speranzanded to be a short introduction of deep learning basics through the example of a practical implementation case...The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journe
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發(fā)表于 2025-3-22 07:58:21 | 只看該作者
Renata Mansini,W?odzimierz Ogryczak,M. Grazia Speranzanded to be a short introduction of deep learning basics through the example of a practical implementation case...The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journe
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發(fā)表于 2025-3-22 10:32:15 | 只看該作者
Renata Mansini,W?odzimierz Ogryczak,M. Grazia Speranzanded to be a short introduction of deep learning basics through the example of a practical implementation case...The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journe
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發(fā)表于 2025-3-22 13:52:10 | 只看該作者
Renata Mansini,W?odzimierz Ogryczak,M. Grazia Speranzanded to be a short introduction of deep learning basics through the example of a practical implementation case...The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journe
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發(fā)表于 2025-3-22 20:54:48 | 只看該作者
Renata Mansini,W?odzimierz Ogryczak,M. Grazia Speranzanded to be a short introduction of deep learning basics through the example of a practical implementation case...The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journe
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