SVRG and LSVRG approximate gradient methods

[MargaretDuff]

Describe your changes

SVRG and LSVRG approximate gradient methods

Implements:
SVRG

• https://proceedings.neurips.cc/paper/2013/file/ac1dd209cbcc5e5d1c6e28598e8cbbe8-Paper.pdf
• https://github.com/epapoutsellis/StochasticCIL/blob/svrg/Wrappers/Python/cil/optimisation/functions/SVRGFunction.py

LSVRG

• https://github.com/epapoutsellis/StochasticCIL/blob/svrg/Wrappers/Python/cil/optimisation/functions/SVRGFunction.py

Describe any testing you have performed

Please add any demo scripts to CIL-Demos/misc/

Link relevant issues

Closes #1623

Checklist when you are ready to request a review

• I have performed a self-review of my code
• I have added docstrings in line with the guidance in the developer guide
• I have implemented unit tests that cover any new or modified functionality
• CHANGELOG.md has been updated with any functionality change
• Request review from all relevant developers
• Change pull request label to 'Waiting for review'

Contribution Notes

Please read and adhere to the developer guide and local patterns and conventions.

• The content of this Pull Request (the Contribution) is intentionally submitted for inclusion in CIL (the Work) under the terms and conditions of the Apache-2.0 License.
• I confirm that the contribution does not violate any intellectual property rights of third parties

[paskino]

LGTM, it would be nice to add clearly in the documentation the memory requirements of these algorithms

[MargaretDuff]

Jenkins is happy

All comments outdated and checked with Jakob via discord