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Lectures on Convex Optimization - (Springer Optimization and Its Applications) 2nd Edition by Yurii Nesterov (Hardcover)

Lectures on Convex Optimization - (Springer Optimization and Its Applications) 2nd Edition by  Yurii Nesterov (Hardcover) - 1 of 1
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About this item

Highlights

  • 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.
  • About the Author: ​Yurii Nesterov is a well-known specialist in optimization.
  • 589 Pages
  • Mathematics, Applied
  • Series Name: Springer Optimization and Its Applications

Description



About the Book



The first elementary exposition of core ideas of complexity theory for convex optimization, this book explores optimal methods and lower complexity bounds for smooth and non-smooth convex optimization. Also covers polynomial-time interior-point methods.



Book Synopsis



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 find this 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 advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.



From the Back Cover



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 advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.



Review Quotes




"It is a must-read for both students involved in the operations research programs, as well as the researchers in the area of nonlinear programming, in particular in convex optimization." (Marcin Anholcer, zbMATH 1427.90003, 2020)



About the Author



​Yurii Nesterov is a well-known specialist in optimization. He is an author of pioneering works related to fast gradient methods, polynomial-time interior-point methods, smoothing technique, regularized Newton methods, and others. He is a winner of several prestigious international prizes, including George Danzig prize (2000), von Neumann Theory prize (2009), SIAM Outstanding Paper Award (20014), and Euro Gold Medal (2016).
Dimensions (Overall): 9.21 Inches (H) x 6.14 Inches (W) x 1.31 Inches (D)
Weight: 2.28 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 589
Genre: Mathematics
Sub-Genre: Applied
Series Title: Springer Optimization and Its Applications
Publisher: Springer
Format: Hardcover
Author: Yurii Nesterov
Language: English
Street Date: December 1, 2018
TCIN: 1006095424
UPC: 9783319915777
Item Number (DPCI): 247-37-8381
Origin: Made in the USA or Imported
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Shipping details

Estimated ship dimensions: 1.31 inches length x 6.14 inches width x 9.21 inches height
Estimated ship weight: 2.28 pounds
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