# Optimizer Baselines

## Algorithms

### collab_pls (Collaborative Penalized Least Squares)

`collab_pls()`

:
explanation for the algorithm.
There is no figure showing a fit for for this method since it requires multiple sets of data.

### adaptive_minmax (Adaptive MinMax)

### individual_axes (1D Baseline Correction Along Individual Axes)

`individual_axes()`

is the single unique 2D baseline correction
algorithm that is not available as a 1D algorithm, and it applies the specified 1D
baseline algorithm along each row and/or column of the measured data. This is useful
if the axes of the data are not correlated such that no information is lost by
fitting each axis separately, or when baselines only exist along one axis.

Note that one limitation of `individual_axes()`

is that it does not
handle array-like method_kwargs, such as when different input weights are desired
for each dataset along the rows and/or columns. However, this is an extremely niche
situation, and could be handled by simply using a for-loop to do one dimensional
baseline correction instead.