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 Book
%1 Hazan16
%A Hazan, Elad
%B Foundations and Trends in Optimization
%C Boston
%D 2017
%I Now
%K 01801 103 book numerical ai learn optimize algorithm
%R 10.1561/2400000013
%T Introduction to Online Convex Optimization
%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.
%@ 978-1-68083-170-2
@book{Hazan16,
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 = {2018-02-10T16:55:05.000+0100},
address = {Boston},
author = {Hazan, Elad},
biburl = {https://www.bibsonomy.org/bibtex/23bb030e080e2271e47297f46364eb048/flint63},
doi = {10.1561/2400000013},
file = {eBook:2016/Hazan16.pdf:PDF;Now Produktseite:https\://www.nowpublishers.com/article/Details/OPT-013:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/1521133301/:URL},
groups = {public},
interhash = {0d480d7bc13ad7711b78e72c9f8365c8},
intrahash = {3bb030e080e2271e47297f46364eb048},
isbn = {978-1-68083-170-2},
keywords = {01801 103 book numerical ai learn optimize algorithm},
publisher = {Now},
series = {Foundations and Trends in Optimization},
timestamp = {2018-04-16T12:03:38.000+0200},
title = {Introduction to Online Convex Optimization},
username = {flint63},
year = 2017
}