# 14.1. Checking algorithm “AdjointTest”¶

## Description¶

This algorithm allows to check the quality of the adjoint of an operator , by computing a residue whose theoretical properties are known. The test is applicable to any operator, of evolution or observation.

For all formulas, with the current verification point, we take and with a scaling user parameter, defaulting to 1. is the computational operator or code (which is here acquired by the observation operator command “ObservationOperator”).

One can observe the following residue, which is the difference of two scalar products:

in which the optional quantity must be in the image of . If it is not given, we take its default evaluation .

This residue must remain constantly equal to zero at the accuracy of the calculation.

## Optional and required commands¶

The general required commands, available in the editing user graphical or textual interface, are the following:

CheckingPoint
Vector. The variable indicates the vector used as the state around which to perform the required check, noted and similar to the background . It is defined as a “Vector” or “VectorSerie” type object. Its availability in output is conditioned by the boolean “Stored” associated with input.
Observation
List of vectors. The variable indicates the observation vector used for data assimilation or optimization, and usually noted . Its value is defined as an object of type “Vector” if it is a single observation (temporal or not) or “VectorSeries” if it is a succession of observations. Its availability in output is conditioned by the boolean “Stored” associated in input.
ObservationOperator
Operator. The variable indicates the observation operator, usually noted as , which transforms the input parameters to results to be compared to observations . Its value is defined as a “Function” type object or a “Matrix” type one. In the case of “Function” type, different functional forms can be used, as described in the section Requirements for functions describing an operator. If there is some control included in the observation, the operator has to be applied to a pair .

The general optional commands, available in the editing user graphical or textual interface, are indicated in List of commands and keywords for an ADAO checking case. Moreover, the parameters of the command “AlgorithmParameters” allow to choose the specific options, described hereafter, of the algorithm. See Description of options of an algorithm by “AlgorithmParameters” for the good use of this command.

The options are the following:

AmplitudeOfInitialDirection

Real value. This key indicates the scaling of the initial perturbation build as a vector used for the directional derivative around the nominal checking point. The default is 1, that means no scaling.

Example: {"AmplitudeOfInitialDirection":0.5}

EpsilonMinimumExponent

Integer value. This key indicates the minimal exponent value of the power of 10 coefficient to be used to decrease the increment multiplier. The default is -8, and it has to be negative between 0 and -20. For example, its default value leads to calculate the residue of the scalar product formula with a fixed increment multiplied from 1.e0 to 1.e-8.

Example: {"EpsilonMinimumExponent":-12}

InitialDirection

Vector. This key indicates the vector direction used for the directional derivative around the nominal checking point. It has to be a vector of the same vector size than the checking point. If not specified, this direction defaults to a random perturbation around zero of the same vector size than the checking point.

Example: {"InitialDirection":[0.1,0.1,100.,3} for a state space of dimension 4

NumberOfPrintedDigits

Integer value. This key indicates the number of digits of precision for floating point printed output. The default is 5, with a minimum of 0.

Example: {"NumberOfPrintedDigits":5}

SetSeed

Integer value. This key allow to give an integer in order to fix the seed of the random generator used in the algorithm. By default, the seed is left uninitialized, and so use the default initialization from the computer, which then change at each study. To ensure the reproducibility of results involving random samples, it is strongly advised to initialize the seed. A simple convenient value is for example 123456789. It is recommended to put an integer with more than 6 or 7 digits to properly initialize the random generator.

Example: {"SetSeed":123456789}

StoreSupplementaryCalculations

List of names. This list indicates the names of the supplementary variables, that can be available during or at the end of the algorithm, if they are initially required by the user. Their avalability involves, potentially, costly calculations or memory consumptions. The default is then a void list, none of these variables being calculated and stored by default (excepted the unconditionnal variables). The possible names are in the following list (the detailed description of each named variable is given in the following part of this specific algorithmic documentation, in the sub-section “Information and variables available at the end of the algorithm”): [ “CurrentState”, “Residu”, “SimulatedObservationAtCurrentState”, ].

Example : {"StoreSupplementaryCalculations":["BMA", "CurrentState"]}

## Information and variables available at the end of the algorithm¶

At the output, after executing the algorithm, there are information and variables originating from the calculation. The description of Variables and informations available at the output show the way to obtain them by the method named get, of the variable “ADD” of the post-processing in graphical interface, or of the case in textual interface. The input variables, available to the user at the output in order to facilitate the writing of post-processing procedures, are described in the Inventory of potentially available information at the output.

Permanent outputs (non conditional)

The unconditional outputs of the algorithm are the following:

Residu

List of values. Each element is the value of the particular residue checked during the running of the algorithm, in the order of the tests.

Example: r = ADD.get("Residu")[:]

Set of on-demand outputs (conditional or not)

The whole set of algorithm outputs (conditional or not), sorted by alphabetical order, is the following:

CurrentState

List of vectors. Each element is a usual state vector used during the iterative algorithm procedure.

Example: Xs = ADD.get("CurrentState")[:]

Residu

List of values. Each element is the value of the particular residue checked during the running of the algorithm, in the order of the tests.

Example: r = ADD.get("Residu")[:]

SimulatedObservationAtCurrentState

List of vectors. Each element is an observed vector simulated by the observation operator from the current state, that is, in the observation space.

Example: hxs = ADD.get("SimulatedObservationAtCurrentState")[-1]

## Python (TUI) use examples¶

Here is a very simple use of the given algorithm and its parameters, written in [DocR] Textual User Interface for ADAO (TUI/API), and from which input information allow to define an equivalent cas in graphical interface.

This example describes the test of the quality of the adjoint of some operator, whose direct formulation is given and whose adjoint formulation is here approximated by default. The required information is minimal, namely here an operator (described for the test by the observation command “ObservationOperator”), and a state to test it on (described for the test by the command “CheckingPoint”). An observation can be given as here (described for the test by the command “Observation”). The output has been set to determine the printout, for example to make more easy automatic comparison.

The actual check is to observe whether the residue is consistently equal to zero at the accuracy of the calculation.

# -*- coding: utf-8 -*-
#
from numpy import array, eye
from adao import adaoBuilder
case = adaoBuilder.New('')
case.setCheckingPoint( Vector = array([0., 1., 2.]), Stored=True )
case.setObservation( Vector = [10., 11., 12.] )
case.setObservationOperator( Matrix = eye(3), )
case.setAlgorithmParameters(
Algorithm='AdjointTest',
Parameters={
'EpsilonMinimumExponent' :-12,
'NumberOfPrintedDigits' : 3,
'SetSeed' : 1234567,
},
)
case.execute()


The execution result is the following:


ADJOINTTEST
===========

This test allows to analyze the quality of an adjoint operator associated
to some given direct operator. If the adjoint operator is approximated and
not given, the test measures the quality of the automatic approximation.

Using the "ScalarProduct" formula, one observes the residue R which is the
difference of two scalar products:

R(Alpha) = | < TangentF_X(dX) , Y > - < dX , AdjointF_X(Y) > |

which must remain constantly equal to zero to the accuracy of the calculation.
One takes dX0 = Normal(0,X) and dX = Alpha*dX0, where F is the calculation
operator. If it is given, Y must be in the image of F. If it is not given,
one takes Y = F(X).

(Remark: numbers that are (about) under 2e-16 represent 0 to machine precision)

-------------------------------------------------------------
i   Alpha     ||X||       ||Y||       ||dX||     R(Alpha)
-------------------------------------------------------------
0  1e+00   2.236e+00   1.910e+01   3.536e+00   0.000e+00
1  1e-01   2.236e+00   1.910e+01   3.536e-01   0.000e+00
2  1e-02   2.236e+00   1.910e+01   3.536e-02   0.000e+00
3  1e-03   2.236e+00   1.910e+01   3.536e-03   0.000e+00
4  1e-04   2.236e+00   1.910e+01   3.536e-04   0.000e+00
5  1e-05   2.236e+00   1.910e+01   3.536e-05   0.000e+00
6  1e-06   2.236e+00   1.910e+01   3.536e-06   0.000e+00
7  1e-07   2.236e+00   1.910e+01   3.536e-07   0.000e+00
8  1e-08   2.236e+00   1.910e+01   3.536e-08   0.000e+00
9  1e-09   2.236e+00   1.910e+01   3.536e-09   0.000e+00
10  1e-10   2.236e+00   1.910e+01   3.536e-10   0.000e+00
11  1e-11   2.236e+00   1.910e+01   3.536e-11   0.000e+00
12  1e-12   2.236e+00   1.910e+01   3.536e-12   0.000e+00
-------------------------------------------------------------


## See also¶

References to other sections: