| 書目名稱 | Lectures on Convex Optimization |
| 編輯 | Yurii Nesterov |
| 視頻video | http://file.papertrans.cn/584/583492/583492.mp4 |
| 概述 | Presents a self-contained description of fast gradient methods.Offers the first description in the monographic literature of the modern second-order methods based on cubic regularization.Provides a co |
| 叢書名稱 | Springer Optimization and Its Applications |
| 圖書封面 |  |
| 描述 | This book provides a comprehensive, modern introduction to convex optimization, a field that is becoming increasingly important in applied mathematics, economics and finance, engineering, and computer science, notably in data science and machine learning...Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail..Researchers in theoretical optimization as well as professionals working on optimization problems will findthis book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author’s lectures, it can naturally serve as the basis for introductory and a |
| 出版日期 | Textbook 2018Latest edition |
| 關(guān)鍵詞 | complexity; complexity theory; graphs; mathematical programming; optimization; Fast Gradient Methods; Self |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-3-319-91578-4 |
| isbn_ebook | 978-3-319-91578-4Series ISSN 1931-6828 Series E-ISSN 1931-6836 |
| issn_series | 1931-6828 |
| copyright | Springer Nature Switzerland AG 2018 |
|Archiver|手機(jī)版|小黑屋|
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