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Large-scale and distributed optimization : An introduction

Giselsson, Pontus LU and Rantzer, Anders LU (2018) In Lecture Notes in Mathematics 2227. p.1-10
Abstract

The recent explosion in size and complexity of datasets and the increased availability of computational resources has led us to what is sometimes called the big data era. In many big data fields, mathematical optimization has over the last decade emerged as a vital tool in extracting information from the data sets and creating predictors for unseen data. The large dimension of these data sets and the often parallel, distributed, or decentralized computational structures used for storing and handling the data, set new requirements on the optimization algorithms that solve these problems. This has led to a dramatic shift in focus in the optimization community over this period. Much effort has gone into developing algorithms that scale... (More)

The recent explosion in size and complexity of datasets and the increased availability of computational resources has led us to what is sometimes called the big data era. In many big data fields, mathematical optimization has over the last decade emerged as a vital tool in extracting information from the data sets and creating predictors for unseen data. The large dimension of these data sets and the often parallel, distributed, or decentralized computational structures used for storing and handling the data, set new requirements on the optimization algorithms that solve these problems. This has led to a dramatic shift in focus in the optimization community over this period. Much effort has gone into developing algorithms that scale favorably with problem dimension and that can exploit structure in the problem as well as the computational environment. This is also the main focus of this book, which is comprised of individual chapters that further contribute to this development in different ways. In this introductory chapter, we describe the individual contributions, relate them to each other, and put them into a wider context.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Big data problems, Convex optimization, Monotone inclusions, Nonconvex methods, Operator splitting methods, Scalable methods, Stochastic methods
host publication
Lecture Notes in Mathematics
series title
Lecture Notes in Mathematics
volume
2227
pages
10 pages
publisher
Springer
external identifiers
  • scopus:85056614522
ISSN
0075-8434
DOI
10.1007/978-3-319-97478-1_1
language
English
LU publication?
yes
id
4a4dbd67-b783-4f59-86a1-2a840e2cfd38
date added to LUP
2018-11-28 12:21:57
date last changed
2019-09-27 13:07:52
@inbook{4a4dbd67-b783-4f59-86a1-2a840e2cfd38,
  abstract     = {<p>The recent explosion in size and complexity of datasets and the increased availability of computational resources has led us to what is sometimes called the big data era. In many big data fields, mathematical optimization has over the last decade emerged as a vital tool in extracting information from the data sets and creating predictors for unseen data. The large dimension of these data sets and the often parallel, distributed, or decentralized computational structures used for storing and handling the data, set new requirements on the optimization algorithms that solve these problems. This has led to a dramatic shift in focus in the optimization community over this period. Much effort has gone into developing algorithms that scale favorably with problem dimension and that can exploit structure in the problem as well as the computational environment. This is also the main focus of this book, which is comprised of individual chapters that further contribute to this development in different ways. In this introductory chapter, we describe the individual contributions, relate them to each other, and put them into a wider context.</p>},
  author       = {Giselsson, Pontus and Rantzer, Anders},
  issn         = {0075-8434},
  keyword      = {Big data problems,Convex optimization,Monotone inclusions,Nonconvex methods,Operator splitting methods,Scalable methods,Stochastic methods},
  language     = {eng},
  pages        = {1--10},
  publisher    = {Springer},
  series       = {Lecture Notes in Mathematics},
  title        = {Large-scale and distributed optimization : An introduction},
  url          = {http://dx.doi.org/10.1007/978-3-319-97478-1_1},
  volume       = {2227},
  year         = {2018},
}