Minimizers#
In skretrieval a minimizer is the object responsible for determining the optimal
state vector that minimizes the differences between the real measurements and the simulated measurements.
The default minimizer is the skretrieval.retrieval.rodgers.Rodgers object that is an in-house implementation
of the inversion methods described in “Inverse Methods for Atmospheric Sounding” by Clive Rodgers. Iteration is performed
using a Levenberg-Marquardt technique. This is generally a good choice for most problems, and has quite a few diagnostics built
into the calculation. Using an in-house technique also allows for inspection of the retrieval in-between iterations which
can be useful for debugging purposes.
The other minimizer that is available is the skretrieval.retrieval.scipy.SciPyMinimizer which is a wrapper
around the scipy.optimize.least_squares() function. You should consider using this minimizer instead
if your problem is highly non-linear, or has many state vector elements that should remain bounded.
Available Minimizers#
Implements the standard inverse problem method described in "Inverse Methods for Atmospheric Sounding" by Rodgers (2000). |
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A minimization wrapper around Scipy's least_squares function |