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

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