Feat/rkhs

[ceriottm]

Implement an RKHS solver for the sparse GPR problem.

Rationale and detailed description of the changes:

Solving the GPR problem using the "normal" equation is very ill-conditioned.
This implements an alternative solver that is much better behaved.
Might be good to also include different options in terms of using solve or lstsq,
let's see where this goes - however this already works.

Tasks before review:

• Add tests, examples, and complete documentation of any added functionality
  • Make sure the autogenerated documentation appears in Sphinx and is
    formatted correctly (ask @max-veit if you need help with this task).
  • Write additional documentation in the Sphinx (not docstrings!) to
    explain the feature and its usage in plain English
  • Make sure the examples run (!) and produce the expected output
  • For bugfix pull requests, list here which tests have been added or re-enabled
    to verify the fix and catch future regressions as well as similar bugs
    elsewhere in the code.
• Run make lint on the project, ensure it passes
  • Run make pretty-cpp and make pretty-python, check that the
    auto-formatting is sensible
  • Review variable names, make sure they're descriptive (ask a friend)
  • Review all TODOs, convert to issues wherever possible
  • Make sure draft, in-progress, and commented-out code is moved to its
    own branch
• If committing any code in Jupyter/IPython notebooks, install and run nbstripout
• Merge with master and resolve any conflicts
• (anything else that's still in progress)

[max-veit]

This is good; how about moving the solvers into their own function (taking just the matrices K_NM, K_MM, and Y), separate from the setup/manager/saving logic implemented in train_gap_model?

[max-veit]

Document the "QR" mode please - how is it different from "RKHS"?

[max-veit]

Btw, merging with the main branch will now fix two out of the three test failures. The remaining failure is due to unit test coverage, which I suggest we do not ignore.

[Luthaf]

"Normal" feels strange as a name. How about Standard or Direct or Direct Least Square?