From: Jean-Philippe ARGAUD Date: Tue, 3 Apr 2012 13:32:25 +0000 (+0200) Subject: Updating documentation X-Git-Tag: V6_5_0~17 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=fc9c6c19407d69d1c415a08f49b2df2336f69439;p=modules%2Fadao.git Updating documentation --- diff --git a/doc/resources/ADAO_large.png b/doc/resources/ADAO_large.png new file mode 100644 index 0000000..59abee8 Binary files /dev/null and b/doc/resources/ADAO_large.png differ diff --git a/doc/using.rst b/doc/using.rst index 6b91cef..e9417c1 100644 --- a/doc/using.rst +++ b/doc/using.rst @@ -183,12 +183,19 @@ the main available output variables are the following: #. "Innovation" (automatic): the difference between the observations and the control state transformed by the observation operator, noted as :math:`\mathbf{y}^o - \mathbf{H}\mathbf{x}^b`. +#. "APosterioriCovariance" (optional): the covariance matrix of the *a + posteriori* analysis errors, noted as :math:`\mathbf{A}`. #. "OMB" (optional): the difference between the observations and the background, similar to the innovation. #. "BMA" (optional): the difference between the background and the analysis, noted as :math:`\mathbf{x}^b - \mathbf{x}^a`. #. "OMA" (optional): the difference between the observations and the analysis, noted as :math:`\mathbf{y}^o - \mathbf{H}\mathbf{x}^a`. +#. "CostFunctionJ" (optional): the minimisation function, noted as :math:`J`. +#. "CostFunctionJo" (optional): the observation part of the minimisation + function, noted as :math:`J^o`. +#. "CostFunctionJb" (optional): the background part of the minimisation + function, noted as :math:`J^b`. Input variables are also available as output in order to gather all the information at the end of the procedure. @@ -243,7 +250,7 @@ List of commands The different commands are the following: -:ASSIM_STUDY: +:ASSIMILATION_STUDY: *Required command*. This is the general command describing an ADAO case. It hierarchicaly contains all the other commands. @@ -271,7 +278,7 @@ The different commands are the following: :Debug: *Required command*. This let choose the level of trace and intermediary - debug informations.The choices are limited between 0 (for False) and 1 (for + debug informations. The choices are limited between 0 (for False) and 1 (for True) and available through the GUI. :InputVariables: @@ -296,6 +303,13 @@ The different commands are the following: to results :math:`\mathbf{y}` to be compared to observations :math:`\mathbf{y}^o`. +:Observers: + *Optional command*. This command allows to set internal observers, that are + functions linked with a particular variable, which will be executed each + time this variable is modified. It is a convenient way to monitor interest + variables during the data assimilation process, by printing or plotting it, + etc. + :OutputVariables: *Optional command*. This command allows to indicates the name and size of physical variables that are bundled together in the output observation @@ -338,7 +352,11 @@ specified for an algorithm that doesn't support it, the option is simply left unused. :"Blue": - no option + + :CalculateAPosterioriCovariance: + This boolean key allows to enable the calculation and the storage of the + covariance matrix of a posteriori anlysis errors. Be careful, this is a + numericaly costly step. The default is "False". :"LinearLeastSquares": no option @@ -366,15 +384,29 @@ unused. real problems. For some algorithms, the effective stopping step can be slightly different due to algorihtm internal control requirements. + :CalculateAPosterioriCovariance: + This boolean key allows to enable the calculation and the storage of the + covariance matrix of a posteriori anlysis errors. Be careful, this is a + numericaly costly step. The default is "False". + + :CostDecrementTolerance: + This key indicates a limit value, leading to stop successfully the + iterative optimization process when the cost function decreases less than + this tolerance at the last step. The default is 10e-7, and it is + recommended to adapt it the needs on real problems. + :ProjectedGradientTolerance: This key indicates a limit value, leading to stop successfully the iterative optimization process when all the components of the projected gradient are under this limit. It is only used for constrained algorithms. + The default is -1, that is the internal default of each algorithm, and it + is not recommended to change it. :GradientNormTolerance: This key indicates a limit value, leading to stop successfully the iterative optimization process when the norm of the gradient is under this - limit. It is only used for non-constrained algorithms. + limit. It is only used for non-constrained algorithms. The default is + 10e-5 and it is not recommended to change it. :"NonLinearLeastSquares": @@ -399,21 +431,34 @@ unused. real problems. For some algorithms, the effective stopping step can be slightly different due to algorihtm internal control requirements. + :CostDecrementTolerance: + This key indicates a limit value, leading to stop successfully the + iterative optimization process when the cost function decreases less than + this tolerance at the last step. The default is 10e-7, and it is + recommended to adapt it the needs on real problems. + :ProjectedGradientTolerance: This key indicates a limit value, leading to stop successfully the iterative optimization process when all the components of the projected gradient are under this limit. It is only used for constrained algorithms. + The default is -1, that is the internal default of each algorithm, and it + is not recommended to change it. :GradientNormTolerance: This key indicates a limit value, leading to stop successfully the iterative optimization process when the norm of the gradient is under this - limit. It is only used for non-constrained algorithms. + limit. It is only used for non-constrained algorithms. The default is + 10e-5 and it is not recommended to change it. :"EnsembleBlue": no option :"KalmanFilter": - no option + + :CalculateAPosterioriCovariance: + This boolean key allows to enable the calculation and the storage of the + covariance matrix of a posteriori anlysis errors. Be careful, this is a + numericaly costly step. The default is "False". Examples of using these commands are available in the section :ref:`section_examples` and in example files installed with ADAO module.