Implement (Stochastic) Subgradient Mean

[unnamedplay-r]

Summary:
Stochastically explore the DTW Averaging Space. It's a useful algorithm for exploring online settings and larger sample sizes. The paper is a bit of work to get through as they rigerously detail a connection between the DTW sample mean to nonsmooth optimization methods for their proposed algorithm. They
also detail a non-stochastic subgradient method and present a vectorized version of DBA that might provide additional performance benefits (I'll create a seperate issue for refactoring DBA to that).

I think this package would benefit greatly for both the Subgradient (SG) and Stochastic Subgradient (SSG) methods.

Original Paper:
Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces

• arxiv pdf: https://arxiv.org/pdf/1701.06393.pdf
• Final version of paper (paywall): https://www.sciencedirect.com/science/article/pii/S0031320317303163

Relevant sections are 4.1. The Subgradient Mean Algorithm and 4.3. The Stochastic Subgradient Mean Algorithm and their respective algorithms 1 and 3.

[rtavenar]

It is now merged to dev, and will be included in tslearn from version 0.3.
Thanks @unnamedplay-r for the ref!