Large-Scale and Distributed Optimization

  • Pontus Giselsson
  • Anders Rantzer

Part of the Lecture Notes in Mathematics book series (LNM, volume 2227)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Pontus Giselsson, Anders Rantzer
    Pages 1-10
  3. Anders Hansson, Sina Khoshfetrat Pakazad
    Pages 11-32
  4. Volkan Cevher, Bằng Công Vũ, Alp Yurtsever
    Pages 149-179
  5. Anastasia Bayandina, Pavel Dvurechensky, Alexander Gasnikov, Fedor Stonyakin, Alexander Titov
    Pages 181-213
  6. Lijun Ding, Madeleine Udell
    Pages 215-245
  7. Angelia Nedić, Alexander Olshevsky, Wei Shi
    Pages 247-287
  8. Robert Baier, Philipp Braun, Lars Grüne, Christopher M. Kellett
    Pages 343-373
  9. Back Matter
    Pages 411-412

About this book


This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians.
Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th–16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization.


Large-Scale Optimization Distributed Optimization Operator Splitting Methods Machine Learning Convex Optimization First-Order Methods

Editors and affiliations

  • Pontus Giselsson
    • 1
  • Anders Rantzer
    • 2
  1. 1.Department of Automatic ControlLund UniversityLundSweden
  2. 2.Department of Automatic ControlLund UniversityLundSweden

Bibliographic information