Project details for Semi Stochastic Gradient Descent

Logo Semi Stochastic Gradient Descent 1.0

by konkey - July 9, 2014, 04:28:47 CET [ BibTeX BibTeX for corresponding Paper Download ]

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Description:

Efficient implementation of Semi-Stochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2-regularized).

The S2GD algorithm enjoys linear convergence (faster than SGD), and comes without the need of tuning parameters like stepsize (choice explained in paper).

The code is in C++, called from MATLAB. The code is well commented, and should be easily adjusted for different applications.

The package also contains implementation of SGD and SAG.

First time usage:

>> mexAll % compiles the .cpp files

>> demo

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Agnostic
Tags: Stochastic Gradient Descent, Logistic Regression
Archive: download here

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