Optimization
Before optimization, the SSOF problem (with a SSOF model and the StellarSpectraObservationFitting.LSFData it's being fit with) is organized into a work space (like StellarSpectraObservationFitting.TotalWorkspace) which includes a suitable chi-squared loss function and its gradient
\[\mathcal{L}(\beta_M) = \sum_{n=1}^N (Y_{D,n} - Y_{M,n})^T \Sigma_n^{-1} (Y_{D,n} - Y_{M,n}) + \textrm{constant}\]
This object can be passed to a function like StellarSpectraObservationFitting.improve_model! to optimize the SSOF model on the data.
StellarSpectraObservationFitting.improve_model! — Functionimprove_model!(mws; verbose=true, kwargs...)Train the model in mws with an extra step to ensure we are at a local maximum for the scores and RVs