Source code for the work A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization accepted by AISTATS2023.

This repo contains implementations of a collection of stochastic first-order methods, including ProxSVRG, SAGA, RDA, PStorm, and S-PStorm. The repo contains two main directories, src and test. src/solvers contain the source code for all algorithm implementations. test directory contains the scripts necessary to reproduce the results reported in the paper.

Navigate to the test/data_prep directory and do the following steps.

1. Run bash download.sh to download 10 datasets.
2. Run bash process.sh to perform data preprocessing.
3. Run python compute_Lip to get the estimate of the Lipschitz constants.

1. Navigate to the directory: cd test/bash.
2. Generate the bash scripts: python create_bash.py
3. Run the command bash submit and experiments will run in the background. The logs on each run can be found at test/bash/log and the results will be saved at test/experiments