This monograph portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.
%0 Journal Article
%1 Hazan16fntopt
%A Hazan, Elad
%D 2016
%J Foundations and Trends in Optimization
%K 01801 paper numerical ai learn optimize algorithm
%N 3-4
%P 157--325
%R 10.1561/2400000013
%T Introduction to Online Convex Optimization
%V 2
%X This monograph portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.
@article{Hazan16fntopt,
abstract = {This monograph portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.},
added-at = {2018-02-10T16:55:07.000+0100},
author = {Hazan, Elad},
biburl = {https://www.bibsonomy.org/bibtex/2664e60373759ad13bfd0db29e067428c/flint63},
description = {Book ISBN 978-1-68083-170-2},
doi = {10.1561/2400000013},
file = {eBook:2016/Hazan16.pdf:PDF},
groups = {public},
interhash = {850804864dde542fe8dfa85e61b63627},
intrahash = {664e60373759ad13bfd0db29e067428c},
issn = {2167-3888},
journal = {Foundations and Trends in Optimization},
keywords = {01801 paper numerical ai learn optimize algorithm},
number = {3-4},
pages = {157--325},
timestamp = {2018-04-16T12:03:36.000+0200},
title = {Introduction to Online Convex Optimization},
username = {flint63},
volume = 2,
year = 2016
}