13.15. Calculation algorithm “TabuSearch”¶

13.15.1. Description¶

This algorithm realizes an estimation of the state of a system by minimization without gradient of a cost function $J$ , using a Tabu list search method. It is a method that does not use the derivatives of the cost function. It falls in the same category than the Calculation algorithm “DerivativeFreeOptimization”, Calculation algorithm “DifferentialEvolution” or Calculation algorithm “ParticleSwarmOptimization”.

This is a mono-objective optimization method allowing for global minimum search of a general error function $J$ of type $L^1$ , $L^2$ or $L^{\infty}$ , with or without weights. The default error function is the augmented weighted least squares function, classically used in data assimilation.

It works by iterative random exploration of the surroundings of the current point, to choose the state that minimizes the error function. To avoid returning to a point already explored, the algorithm’s memory mechanism allows to exclude (hence the name tabu) the return to the last explored states. Positions already explored are kept in a list of finite length.

13.15.2. Some noteworthy properties of the implemented methods¶

To complete the description, we summarize here a few notable properties of the algorithm methods or of their implementations. These properties may have an influence on how it is used or on its computational performance. For further information, please refer to the more comprehensive references given at the end of this algorithm description.

The optimization methods proposed by this algorithm perform a non-local search for the minimum, without however ensuring a global search. This is the case when optimization methods have the ability to avoid being trapped by the first local minimum found. These capabilities are sometimes heuristic.

The methods proposed by this algorithm do not require derivation of the objective function or of one of the operators, thus avoiding this additional cost when derivatives are calculated numerically by multiple evaluations.

The methods proposed by this algorithm achieve their convergence on one or more number criteria. In practice, there may be simultaneously several convergence criteria.

The number is frequently a significant value for the algorithm, such as a number of iterations or a number of evaluations, but it can also be, for example, a number of generations for an evolutionary algorithm.

Convergence thresholds need to be carefully adjusted, to reduce the gobal calculation cost, or to ensure that convergence is adapted to the physical case encountered.

13.15.3. Optional and required commands¶

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

Background: Vector. The variable indicates the background or initial vector used, previously noted as $\mathbf{x}^b$ . Its value is defined as a “Vector” or “VectorSerie” type object. Its availability in output is conditioned by the boolean “Stored” associated with input.

BackgroundError: Matrix. This indicates the background error covariance matrix, previously noted as $\mathbf{B}$ . Its value is defined as a “Matrix” type object, a “ScalarSparseMatrix” type object, or a “DiagonalSparseMatrix” type object, as described in detail in the section Requirements to describe covariance matrices. 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 $\mathbf{y}^o$ . 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.

ObservationError: Matrix. The variable indicates the observation error covariance matrix, usually noted as $\mathbf{R}$ . It is defined as a “Matrix” type object, a “ScalarSparseMatrix” type object, or a “DiagonalSparseMatrix” type object, as described in detail in the section Requirements to describe covariance matrices. Its availability in output is conditioned by the boolean “Stored” associated with input.

ObservationOperator: Operator. The variable indicates the observation operator, usually noted as $H$ , which transforms the input parameters $\mathbf{x}$ to results $\mathbf{y}$ to be compared to observations $\mathbf{y}^o$ . 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 $U$ included in the observation, the operator has to be applied to a pair $(X,U)$ .

The general optional commands, available in the editing user graphical or textual interface, are indicated in List of commands and keywords for data assimilation or optimization case. Moreover, the parameters of the command “AlgorithmParameters” allows 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:

Bounds

List of pairs of real values. This key allows to define pairs of upper and lower bounds for every state variable being optimized. Bounds have to be given by a list of list of pairs of lower/upper bounds for each variable, with a value of None each time there is no bound. The bounds can always be specified, but they are taken into account only by the constrained optimizers. If the list is empty, there are no bounds.

Example: {"Bounds":[[2.,5.],[1.e-2,10.],[-30.,None],[None,None]]}

LengthOfTabuList

Integer value. This key indicates the length of the tabu list, that is the maximum number of previously executed perturbations and kept for the record. The default is 50, and it is recommended to adapt it to the needs on real problems.

Example : {"LengthOfTabuList":50}

MaximumNumberOfIterations

Integer value. This key indicates the maximum number of internal iterations allowed for iterative optimization. The default is 50, which is an arbitrary limit. It is then recommended to adapt this parameter to the needs on real problems.

Example: {"MaximumNumberOfIterations":50}

NoiseAddingProbability

Real value. This key indicates the probability of perturbing a state component. It is a mandatory value between 0 and 1. The default is 1, and it is not recommended to change it.

Example : {"NoiseAddingProbability":1.}

NoiseDistribution

Predefined name. This key indicates the type of the distribution used to generate the state perturbations. This distribution can be of “Uniform” or “Gaussian” type. The default is a distribution of “Uniform” type, and it is recommended to adapt it to the needs on real problems.

Example : {"NoiseDistribution":"Uniform"}

NoiseHalfRange

List of real values. This key indicates,, only in the case of a “Uniform” distribution type asked through the keyword “NoiseDistribution”, the half-amplitude of the uniform state centred perturbations for each component of the state. The default is an empty list, this key must therefore be filled in in the case of a “Uniform” distribution. A simple way to do this is to give a list of the length of the desired state with identical half-amplitudes, as in the example below with half-amplitudes of 3%. It is recommended to take half-amplitudes of a few percent maximum.

Example : {"NoiseHalfRange":<state length>*[0.03]}

NumberOfElementaryPerturbations

Integer value. This key indicates the number of elementary perturbations that will be performed to select a complete state perturbation. The default is 1, and it is recommended to adapt it carefully to the needs of real problems, without choosing too many elementary perturbations.

Example : {"NumberOfElementaryPerturbations":1}

QualityCriterion

Predefined name. This key indicates the quality criterion, minimized to find the optimal state estimate. The default is the usual data assimilation criterion named “DA”, the augmented weighted least squares. The possible criterion has to be in the following list, where the equivalent names are indicated by the sign “<=>”: [“AugmentedWeightedLeastSquares” <=> “AWLS” <=> “DA”, “WeightedLeastSquares” <=> “WLS”, “LeastSquares” <=> “LS” <=> “L2”, “AbsoluteValue” <=> “L1”, “MaximumError” <=> “ME” <=> “Linf”]. See the section for Going further in the state estimation by optimization methods to have a detailed definition of these quality criteria.

Example: {"QualityCriterion":"DA"}

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}

StandardDeviation

List of real values. This key indicates, only in the case of a “Gaussian” distribution type asked through the keyword “NoiseDistribution”, the standard deviation of the state Gaussian perturbations for each state component. The default is an empty list, this key must therefore be filled in in the case of a “Gaussian” distribution. A simple way to do this is to give a list of the length of the desired state with identical standard deviations, as in the example below with standard deviations of 5%. It is recommended to take standard deviations of a few percent maximum.

Example : {"StandardDeviation":<longueur de l'état>*[0.05]}

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 availability 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 unconditional 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”): [ “Analysis”, “BMA”, “CostFunctionJ”, “CostFunctionJb”, “CostFunctionJo”, “CurrentIterationNumber”, “CurrentState”, “Innovation”, “OMA”, “OMB”, “SimulatedObservationAtBackground”, “SimulatedObservationAtCurrentState”, “SimulatedObservationAtOptimum”, ].

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

13.15.4. 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 information 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:

Analysis

List of vectors. Each element of this variable is an optimal state $\mathbf{x}^*$ in optimization, an interpolate or an analysis $\mathbf{x}^a$ in data assimilation.

Example: xa = ADD.get("Analysis")[-1]

CostFunctionJ

List of values. Each element is a value of the chosen error function $J$ .

Example: J = ADD.get("CostFunctionJ")[:]

CostFunctionJb

List of values. Each element is a value of the error function $J^b$ , that is of the background difference part. If this part does not exist in the error function, its value is zero.

Example: Jb = ADD.get("CostFunctionJb")[:]

CostFunctionJo

List of values. Each element is a value of the error function $J^o$ , that is of the observation difference part.

Example: Jo = ADD.get("CostFunctionJo")[:]

Set of on-demand outputs (conditional or not)

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

Analysis

List of vectors. Each element of this variable is an optimal state $\mathbf{x}^*$ in optimization, an interpolate or an analysis $\mathbf{x}^a$ in data assimilation.

Example: xa = ADD.get("Analysis")[-1]

BMA

List of vectors. Each element is a vector of difference between the background and the optimal state.

Example: bma = ADD.get("BMA")[-1]

CostFunctionJ

List of values. Each element is a value of the chosen error function $J$ .

Example: J = ADD.get("CostFunctionJ")[:]

CostFunctionJb

List of values. Each element is a value of the error function $J^b$ , that is of the background difference part. If this part does not exist in the error function, its value is zero.

Example: Jb = ADD.get("CostFunctionJb")[:]

CostFunctionJo

List of values. Each element is a value of the error function $J^o$ , that is of the observation difference part.

Example: Jo = ADD.get("CostFunctionJo")[:]

CurrentIterationNumber

List of integers. Each element is the iteration index at the current step during the iterative algorithm procedure. There is one iteration index value per assimilation step corresponding to an observed state.

Example: cin = ADD.get("CurrentIterationNumber")[-1]

CurrentState

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

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

Innovation

List of vectors. Each element is an innovation vector, which is in static the difference between the optimal and the background, and in dynamic the evolution increment.

Example: d = ADD.get("Innovation")[-1]

OMA

List of vectors. Each element is a vector of difference between the observation and the optimal state in the observation space.

Example: oma = ADD.get("OMA")[-1]

OMB

List of vectors. Each element is a vector of difference between the observation and the background state in the observation space.

Example: omb = ADD.get("OMB")[-1]

SimulatedObservationAtBackground

List of vectors. Each element is a vector of observation simulated by the observation operator from the background $\mathbf{x}^b$ . It is the forecast from the background, and it is sometimes called “Dry”.

Example: hxb = ADD.get("SimulatedObservationAtBackground")[-1]

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]

SimulatedObservationAtOptimum

List of vectors. Each element is a vector of observation obtained by the observation operator from simulation on the analysis or optimal state $\mathbf{x}^a$ . It is the observed forecast from the analysis or the optimal state, and it is sometimes called “Forecast”.

Example: hxa = ADD.get("SimulatedObservationAtOptimum")[-1]

13.15.5. See also¶

References to other sections:

Bibliographical references: