- We will send in 10–14 business days.
/images/books/5648738/9781108741774.jpg)
- Author: Nisheeth K Vishnoi
- Publisher: Cambridge University Press
- ISBN-10: 1108741770
- ISBN-13: 9781108741774
- Format: 15.2 x 22.9 x 1.9 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Reviews
Description
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 21d.02:43:33
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Nisheeth K Vishnoi
- Publisher: Cambridge University Press
- ISBN-10: 1108741770
- ISBN-13: 9781108741774
- Format: 15.2 x 22.9 x 1.9 cm, minkšti viršeliai
- Language: English English
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.
Reviews