The constraints Argument¶
Basic Structure of Constraints¶
minimize
and maximize
can take a list with any number of constraints.
A constraint in estimagic is a dictionary. The following keys are mandatory for all
types of constraints:
1. "loc"
or "query"
but not both: This will select the subset of parameters to
which the constraint applies. If you use “loc”, the corresponding value can be any
expression that is valid for DataFrame.loc
. If you are not familiar with these
methods, check out our tutorial on selecting parameters.
"type"
: This can take any of the following values:
“fixed”: The selected parameters are fixed to a value.
“probability”: The selected parameters sum to one and are between zero and one.
“increasing”: The selected parameters are increasing.
“decreasing”: The seletced parameters are decreasing.
“equality”: The selected parameters are equal to each other.
“pairwise_equality”: Several sets of parameters are pairwise equal to each other.
“covariance”: The selected parameters are variances and covariances.
“sdcorr”: The selected parameters are standard deviations and correlations.
“linear”: The selected parameters satisfy a linear constraint with equality or inequalities.
Depending on the type of constraint, some additional entries in the constraint dictionary might be required. The details are explained on the next pages: