INTRODUCTORY LECTURES ON CONVEX OPTIMIZATION NESTEROV PDF

Yurii Nesterov. It was in the middle of the s, when the seminal paper by Kar markar opened a new epoch in nonlinear optimization. The importance of this paper, containing a new polynomial-time algorithm for linear op timization problems, was not only in its complexity bound. At that time, the most surprising feature of this algorithm was that the theoretical pre diction of its high efficiency was supported by excellent computational results.

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It seems that you're in Germany. We have a dedicated site for Germany. 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.

He is an author of pioneering works related to fast gradient methods, polynomial-time interior-point methods, smoothing technique, regularized Newton methods, and others. Only valid for books with an ebook version.

Springer Reference Works are not included. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. 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 comprehensive treatment of the smoothing technique Develops a new theory of optimization in relative scale see more benefits.

Buy eBook. Buy Hardcover. FAQ Policy. About this Textbook 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.

Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Show all. Show next xx. Read this book on SpringerLink. Recommended for you. PAGE 1.

ARMUTSBERICHT 2009 PDF

Introductory Lectures on Convex Optimization

It seems that you're in Germany. We have a dedicated site for Germany. Thereafter it became more and more common that the new methods were provided with a complexity analysis, which was considered a better justification of their efficiency than computational experiments. Afteralmost fifteen years of intensive research, the main results of this development started to appear in monographs [12, 14, 16, 17, 18, 19]. Approximately at that time the author was asked to prepare a new course on nonlinear optimization for graduate students. The idea was to create a course which would reflect the new developments in the field.

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