congrad
/ std
¶
Description¶
Python version of the congrad minimization algorithm
Mike Fisher (ECMWF), April 2002 Frederic Chevallier (LSCE), April 2004, for the Python adaptation
Yaml arguments¶
The following arguments are used to configure the plugin. pyCIF will return an exception at the initialization if mandatory arguments are not specified, or if any argument does not fit accepted values or type:
Optional arguments¶
save_uncertainties: (optional): False
Save the estimated eigenvectors of the inverse of the hessian. They allow the reconstruction of the uncertainty reduction matrix
accepted type: <class ‘bool’>
force_linearize: (optional): False
Force linearizing the cost function by using the TL instead of the forward
accepted type: <class ‘bool’>
maxiter: (optional): 1
maximum number of iterations
accepted type: <class ‘int’>
Requirements¶
The current plugin requires the present plugins to run properly:
Requirement name |
Requirement type |
Explicit definition |
Any valid |
Default name |
Default version |
---|---|---|---|---|---|
simulator |
True |
True |
gausscost |
std |
Yaml template¶
Please find below a template for a Yaml configuration:
1minimizer:
2 plugin:
3 name: congrad
4 version: std
5 type: minimizer
6
7
8 # Optional arguments
9 save_uncertainties: XXXXX
10 force_linearize: XXXXX
11 maxiter: XXXXX