Skip navigation
Skip navigation

Finito: A faster, permutable incremental gradient method for big data problems

Defazio, Aaron; Caetano, Tiberio; Domke, Justin

Description

Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem. In this work we introduce a new method in this class with a theoretical convergence rate four times faster than ex-isting methods, for sums with sufficiently many terms. This method is also amendable to a sampling without replacement scheme that in practice gives further speed-ups. We give empirical results showing state of the...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Conference paper
URI: http://hdl.handle.net/1885/57929
Source: 31st International Conference on Machine Learning, ICML 2014

Download

File Description SizeFormat Image
01_Defazio_Finito:_A_faster,_permutable_2014.pdf2.09 MBAdobe PDF    Request a copy


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  20 July 2017/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator