[jira] [Updated] (MXNET-854) SVRG Optimization in Python Module API

["Sandeep Krishnamurthy (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MXNET-854?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sandeep Krishnamurthy updated MXNET-854:
----------------------------------------
    Resolution: Fixed
        Status: Done  (was: To Do)

> SVRG Optimization in Python Module API
> --------------------------------------
>
>                 Key: MXNET-854
>                 URL: https://issues.apache.org/jira/browse/MXNET-854
>             Project: Apache MXNet
>          Issue Type: New Feature
>            Reporter: Stephanie Yuan
>            Priority: Major
>          Time Spent: 10h 50m
>  Remaining Estimate: 0h
>
> SVRG stands for Stochastic Variance Reduced Gradients, which is an optimization technique that employs explicit variance reduction. It has provable guarantees for strong smoothly convex functions and converges faster compared to SGD. 
> The proposal is to create a SVRGModule that implements the SVRG optimization technique under the hood. 
> SVRG Module should:
>  * seamlessly supports both dense and sparse data, run on CPU and GPU instances on single machine and in distributed setting.
>  * minimize the API differences with Python Module API.
> Detailed descriptions of API changes and suggested usage can be found in the Design Proposal [here|https://cwiki.apache.org/confluence/display/MXNET/SVRG+Optimization+in+MXNet+Python+Module]. 



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