國立虎尾科技大學 |

Algorithms for convex optimization /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Algorithms for convex optimization // Nisheeth K. Vishnoi.
作者:	Vishnoi, Nisheeth K.,
面頁冊數:	1 online resource (xvi, 323 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 27 Sep 2021).
標題:	Convex functions. -
電子資源:	https://doi.org/10.1017/9781108699211
ISBN:	9781108699211 (ebook)

Algorithms for convex optimization /
Vishnoi, Nisheeth K.,1976-

Algorithms for convex optimization /Nisheeth K. Vishnoi. - 1 online resource (xvi, 323 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 27 Sep 2021).

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.

ISBN: 9781108699211 (ebook)Subjects--Topical Terms:

527742
Convex functions.

LC Class. No.: QA402.5 / .V57 2021

Dewey Class. No.: 515/.882

Algorithms for convex optimization /
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520 $a 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.
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