Project details for LibSGDQN

Logo LibSGDQN 1.1

by antojne - July 2, 2009, 15:02:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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LibSGDQN proposes an implementation of SGD-QN, a carefully designed quasi-Newton stochastic gradient descent solver for linear SVMs.

The SGD-QN algorithm is a stochastic gradient descent algorithm that makes careful use of second order information and splits the parameter update into independently scheduled components. Thanks to this design, SGD-QN iterates nearly as fast as a first order stochastic gradient descent but requires less iterations to achieve the same accuracy.

This algorithm is extensively described in the paper: "SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent" by A. Bordes, L. Bottou and P. Gallinari published in Journal of Machine Learning Research: Special Topic on Large Scale Learning (2009).

Along with SGD-QN, this library proposes the implementation of two other online solvers for linear SVMs (also discussed in the JMLR paper). A script to re-run the experiments of this paper is also provided.

Changes to previous version:

small bug fix (thx nicolas ;)

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Cygwin, Macosx, Unix
Data Formats: Svmlight
Tags: Large Scale, Support Vector Machines, Stochastic Gradient Descent
Archive: download here

Other available revisons

Version Changelog Date

small bug fix (thx nicolas ;)

July 2, 2009, 15:02:44

Initial Announcement on

June 24, 2009, 16:51:12


looya.B (on July 8, 2009, 07:41:42)
sorry, there is nothing in my download files. what happened?
Antoine Bordes (on July 8, 2009, 08:27:28)
Did you run the '' script in the 'data/' directory? This downloads the datafiles (and might take quite a while ... ) It's working ok for me.

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