# SAG, SAGA, SVRG methods for Saddle Point Problems

# Code webpage: Stochastic Variance Reduction Methods for Saddle Point Problems

Code webpage: Stochastic Variance Reduction Methods for Saddle Point Problems

This page contains information about the software implementation of Stochastic Variance Reduction Methods (e.g. SAG, SAGA, SVRG) for Saddle Point Problems, used in the paper:

*Stochastic Variance Reduction Methods for Saddle Point Problems.*

P.Balamurugan, Francis Bach.

In *Advances in Neural Information Processing Systems*, 2016. *(To appear)*

Preprint available* at hal arxiv.*

## Download

Download

You can download the current version of the code from the link NIPS_sagsaddle_code.zip

Please note: This code is available free only for non-commercial purposes.

## How To Use

How To Use

- The code works in Matlab.
- Please follow the given steps to use the code.

- Unzip the archive NIPS_sagsaddle_code.zip under some path /USER_PATH (this depends on your machine).
- Under the path /USER_PATH, a directory named NIPS_sagsaddle_code is created. The full path of this directory is /USER_PATH/NIPS_sagsaddle_code
- Open a Matlab shell window.
- Change directory using the following command on the Matlab shell prompt
- cd /USER_PATH/NIPS_sagsaddle_code/

- Type the following command on the Matlab prompt
- simulations_sagsaddle_nips(2,1)

- The function simulations_sagsaddle_nips(ftype, gtype) accepts the following arguments:
- ftype: Regularizer options for function f(x)
- 2 for L1-Norm
- 5 for cluster norm

- gtype: Loss options for function g(y)
- 1 for Squared Hinge-loss
- 6 for AUC Loss

- ftype: Regularizer options for function f(x)
- Please note that a toy data set is used in the code. For other data, please make appropriate changes to the code.