Algorithms for convex optimization[electronic resource]
- 作者: Vishnoi, Nisheeth K.
- 出版: Cambridge : Cambridge University Press 2021.
- 稽核項: xvi, 323 p. :ill., digital ;24 cm.
- 標題: Convex functions. , Mathematical optimization. , Convex programming.
- ISBN: 1108741770 , 9781108741774
- 試查全文@TNUA:
- 附註: Title from publisher's bibliographic system (viewed on 27 Sep 2021).
- 摘要: In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.
- 電子資源: https://dbs.tnua.edu.tw/login?url=https://doi.org/10.1017/9781108699211
- 系統號: 005331473
- 資料類型: 電子書
- 讀者標籤: 需登入
- 引用網址: 複製連結
In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.
來源: Google Book
來源: Google Book
評分
