(WIP) Sparse models: S-LASSO and TV-l1

- Supports TV-l1 and S-LASSO priors
- Supports logistic and squared losses
- Has cross validation
- Can automatically select alpha by CV (+ automatic computation of useful alpha ranges for the CV)
- Warning: User must supply l1_ratio
@dohmatob : I would be interested in testing the ideas in the following paper:
http://statweb.stanford.edu/~candes/papers/adap_restart_paper.pdf
in particular paragraph 3.2

Basically, it boils down to the fact that resetting the FISTA parameters (t and t0) every once in a while speeds the convergence process. We are already partly doing that by setting 'z = w' in the monotonous FISTA scheme below.

What I would like you to test is the following strategies:

1. if at line 235 we put z=w and t=1, do we get a better convergence (standard log-log plots on fMRI problems)?

2. if, at the line above (line 226) we do the following (mfista_factor being initialized above to eg 1e-2):

```python
     if energy_delta < mfista_factor * energy:
            mfista_factor *= 1e-2
            ....
```

in other words, loosen the criteria to switch to mFISTA in the beginning of the convergence of the algorithm.
I'd be interested in empirical tests (as usual, standard log-log plots on fMRI data) to see if this speeds things up.