© 2020

First-order and Stochastic Optimization Methods for Machine Learning


Part of the Springer Series in the Data Sciences book series (SSDS)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Guanghui Lan
    Pages 1-20
  3. Guanghui Lan
    Pages 21-51
  4. Guanghui Lan
    Pages 53-111
  5. Guanghui Lan
    Pages 113-220
  6. Guanghui Lan
    Pages 305-420
  7. Guanghui Lan
    Pages 421-482
  8. Back Matter
    Pages 567-582

About this book


This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.


Stochastic optimization methods Machine learning algorithms Randomized algorithms Nonconvex optimization methods Distributed and decentralized methods

Authors and affiliations

  1. 1.Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA

Bibliographic information

  • Book Title First-order and Stochastic Optimization Methods for Machine Learning
  • Authors Guanghui Lan
  • Series Title Springer Series in the Data Sciences
  • Series Abbreviated Title Springer Series in the Data Sciences
  • DOI
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
  • Hardcover ISBN 978-3-030-39567-4
  • Softcover ISBN 978-3-030-39570-4
  • eBook ISBN 978-3-030-39568-1
  • Series ISSN 2365-5674
  • Series E-ISSN 2365-5682
  • Edition Number 1
  • Number of Pages XIII, 582
  • Number of Illustrations 2 b/w illustrations, 16 illustrations in colour
  • Topics Optimization
    Machine Learning
  • Buy this book on publisher's site
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