doc/en/ref_algorithm_TabuSearch.rst

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   2    Copyright (C) 2008-2024 EDF R&D
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  20    See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
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  22    Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
  23
  24 .. index:: single: TabuSearch
  25 .. _section_ref_algorithm_TabuSearch:
  26
  27 Calculation algorithm "*TabuSearch*"
  28 ------------------------------------
  29
  30 .. ------------------------------------ ..
  31 .. include:: snippets/Header2Algo01.rst
  32
  33 This algorithm realizes an estimation of the state of a system by minimization
  34 without gradient of a cost function :math:`J`, using a Tabu list search method.
  35 It is a method that does not use the derivatives of the cost function. It falls
  36 in the same category than the
  37 :ref:`section_ref_algorithm_DerivativeFreeOptimization`,
  38 :ref:`section_ref_algorithm_DifferentialEvolution` or
  39 :ref:`section_ref_algorithm_ParticleSwarmOptimization`.
  40
  41 This is a mono-objective optimization method allowing for global minimum search
  42 of a general error function :math:`J` of type :math:`L^1`, :math:`L^2` or
  43 :math:`L^{\infty}`, with or without weights. The default error function is the
  44 augmented weighted least squares function, classically used in data
  45 assimilation.
  46
  47 It works by iterative random exploration of the surroundings of the current
  48 point, to choose the state that minimizes the error function. To avoid
  49 returning to a point already explored, the algorithm's memory mechanism allows
  50 to exclude (hence the name *tabu*) the return to the last explored states.
  51 Positions already explored are kept in a list of finite length.
  52
  53 .. ------------------------------------ ..
  54 .. include:: snippets/Header2Algo12.rst
  55
  56 .. include:: snippets/FeaturePropNonLocalOptimization.rst
  57
  58 .. include:: snippets/FeaturePropDerivativeFree.rst
  59
  60 .. ------------------------------------ ..
  61 .. include:: snippets/Header2Algo02.rst
  62
  63 .. include:: snippets/Background.rst
  64
  65 .. include:: snippets/BackgroundError.rst
  66
  67 .. include:: snippets/Observation.rst
  68
  69 .. include:: snippets/ObservationError.rst
  70
  71 .. include:: snippets/ObservationOperator.rst
  72
  73 .. ------------------------------------ ..
  74 .. include:: snippets/Header2Algo03AdOp.rst
  75
  76 .. include:: snippets/BoundsWithNone.rst
  77
  78 .. include:: snippets/LengthOfTabuList.rst
  79
  80 .. include:: snippets/MaximumNumberOfIterations_50.rst
  81
  82 .. include:: snippets/NoiseAddingProbability.rst
  83
  84 .. include:: snippets/NoiseDistribution.rst
  85
  86 .. include:: snippets/NoiseHalfRange.rst
  87
  88 .. include:: snippets/NumberOfElementaryPerturbations.rst
  89
  90 .. include:: snippets/QualityCriterion.rst
  91
  92 .. include:: snippets/SetSeed.rst
  93
  94 .. include:: snippets/StandardDeviation.rst
  95
  96 StoreSupplementaryCalculations
  97   .. index:: single: StoreSupplementaryCalculations
  98
  99   *List of names*. This list indicates the names of the supplementary
 100   variables, that can be available during or at the end of the algorithm, if
 101   they are initially required by the user. Their availability involves,
 102   potentially, costly calculations or memory consumptions. The default is then
 103   a void list, none of these variables being calculated and stored by default
 104   (excepted the unconditional variables). The possible names are in the
 105   following list (the detailed description of each named variable is given in
 106   the following part of this specific algorithmic documentation, in the
 107   sub-section "*Information and variables available at the end of the
 108   algorithm*"): [
 109   "Analysis",
 110   "BMA",
 111   "CostFunctionJ",
 112   "CostFunctionJb",
 113   "CostFunctionJo",
 114   "CurrentIterationNumber",
 115   "CurrentState",
 116   "Innovation",
 117   "OMA",
 118   "OMB",
 119   "SimulatedObservationAtBackground",
 120   "SimulatedObservationAtCurrentState",
 121   "SimulatedObservationAtOptimum",
 122   ].
 123
 124   Example :
 125   ``{"StoreSupplementaryCalculations":["CurrentState", "Residu"]}``
 126
 127 .. ------------------------------------ ..
 128 .. include:: snippets/Header2Algo04.rst
 129
 130 .. include:: snippets/Analysis.rst
 131
 132 .. include:: snippets/CostFunctionJ.rst
 133
 134 .. include:: snippets/CostFunctionJb.rst
 135
 136 .. include:: snippets/CostFunctionJo.rst
 137
 138 .. ------------------------------------ ..
 139 .. include:: snippets/Header2Algo05.rst
 140
 141 .. include:: snippets/Analysis.rst
 142
 143 .. include:: snippets/BMA.rst
 144
 145 .. include:: snippets/CostFunctionJ.rst
 146
 147 .. include:: snippets/CostFunctionJb.rst
 148
 149 .. include:: snippets/CostFunctionJo.rst
 150
 151 .. include:: snippets/CurrentIterationNumber.rst
 152
 153 .. include:: snippets/CurrentState.rst
 154
 155 .. include:: snippets/Innovation.rst
 156
 157 .. include:: snippets/OMA.rst
 158
 159 .. include:: snippets/OMB.rst
 160
 161 .. include:: snippets/SimulatedObservationAtBackground.rst
 162
 163 .. include:: snippets/SimulatedObservationAtCurrentState.rst
 164
 165 .. include:: snippets/SimulatedObservationAtOptimum.rst
 166
 167 .. ------------------------------------ ..
 168 .. _section_ref_algorithm_TabuSearch_examples:
 169
 170 .. include:: snippets/Header2Algo06.rst
 171
 172 - :ref:`section_ref_algorithm_DerivativeFreeOptimization`
 173 - :ref:`section_ref_algorithm_DifferentialEvolution`
 174 - :ref:`section_ref_algorithm_ParticleSwarmOptimization`
 175
 176 .. ------------------------------------ ..
 177 .. include:: snippets/Header2Algo07.rst
 178
 179 - [Glover89]_
 180 - [Glover90]_
 181 - [WikipediaTS]_