Choosing a Solver
=================

Fitting in ``lightning`` is biased toward the Bayesian exploration of parameter space rather than maximum likelihood
estimation. As such the majority of our plotting and post-processing functions assume that you've used ``emcee`` for
fitting.

We do however include an option for fitting the SED model with the L-BFGS-B minimization algorithm, results from which
are compatible with the ``lightning_postprocess`` function. There is additionally an option to solve the problem with
L-BFGS-B and then, if it converges, perform a brief exploration of parameter around the best fitting solution
(converting any preexisting bounds on the parameters to uniform priors). This method is potentially much faster than a
brute force fit with ``emcee``, though it risks getting stuck in a local minimum and is not possible
when the solver fails to converge. The results from this MCMC followup are naturally compatible with the MCMC plotting functions.

In the future, we anticipate adding the option to use a nested sampler for exploration of the parameter space.