congrad/std congrad/std

congrad/std 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 : bool, optional, default False

Save the estimated eigenvectors of the inverse of the hessian. They allow the reconstruction of the uncertainty reduction matrix

force_linearize : bool, optional, default False

Force linearizing the cost function by using the TL instead of the forward

maxiter : int, optional, default 1

maximum number of iterations

Requirements#

The current plugin requires the present plugins to run properly:

Requirement name

Requirement type

Explicit definition

Any valid

Default name

Default version

simulator

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  # Optional arguments
 8  save_uncertainties: XXXXX  # bool
 9  force_linearize: XXXXX  # bool
10  maxiter: XXXXX  # int