doc/en/ref_algorithm_TabuSearch.rst

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   2    Copyright (C) 2008-2023 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 of a cost function :math:`J` without gradient. It is a method that does not use
  35 the derivatives of the cost function. It falls in the same category than the
  36 :ref:`section_ref_algorithm_DerivativeFreeOptimization`, the
  37 :ref:`section_ref_algorithm_ParticleSwarmOptimization` or the
  38 :ref:`section_ref_algorithm_DifferentialEvolution`.
  39
  40 This is an optimization method allowing for global minimum search of a general
  41 error function :math:`J` of type :math:`L^1`, :math:`L^2` or :math:`L^{\infty}`,
  42 with or without weights. The default error function is the augmented weighted
  43 least squares function, classically used in data assimilation.
  44
  45 It works by iterative random exploration of the surroundings of the current
  46 point, to choose the state that minimizes the error function. To avoid
  47 returning to a point already explored, the algorithm's memory mechanism allows
  48 to exclude (hence the name *tabu*) the return to the last explored states.
  49 Positions already explored are kept in a list of finite length.
  50
  51 .. ------------------------------------ ..
  52 .. include:: snippets/Header2Algo02.rst
  53
  54 .. include:: snippets/Background.rst
  55
  56 .. include:: snippets/BackgroundError.rst
  57
  58 .. include:: snippets/Observation.rst
  59
  60 .. include:: snippets/ObservationError.rst
  61
  62 .. include:: snippets/ObservationOperator.rst
  63
  64 .. ------------------------------------ ..
  65 .. include:: snippets/Header2Algo03AdOp.rst
  66
  67 .. include:: snippets/BoundsWithNone.rst
  68
  69 .. include:: snippets/LengthOfTabuList.rst
  70
  71 .. include:: snippets/MaximumNumberOfIterations_50.rst
  72
  73 .. include:: snippets/NoiseAddingProbability.rst
  74
  75 .. include:: snippets/NoiseDistribution.rst
  76
  77 .. include:: snippets/NoiseHalfRange.rst
  78
  79 .. include:: snippets/NumberOfElementaryPerturbations.rst
  80
  81 .. include:: snippets/QualityCriterion.rst
  82
  83 .. include:: snippets/SetSeed.rst
  84
  85 .. include:: snippets/StandardDeviation.rst
  86
  87 StoreSupplementaryCalculations
  88   .. index:: single: StoreSupplementaryCalculations
  89
  90   *List of names*. This list indicates the names of the supplementary
  91   variables, that can be available during or at the end of the algorithm, if
  92   they are initially required by the user. Their avalability involves,
  93   potentially, costly calculations or memory consumptions. The default is then
  94   a void list, none of these variables being calculated and stored by default
  95   (excepted the unconditionnal variables). The possible names are in the
  96   following list (the detailed description of each named variable is given in
  97   the following part of this specific algorithmic documentation, in the
  98   sub-section "*Information and variables available at the end of the
  99   algorithm*"): [
 100   "Analysis",
 101   "BMA",
 102   "CostFunctionJ",
 103   "CostFunctionJb",
 104   "CostFunctionJo",
 105   "CurrentIterationNumber",
 106   "CurrentState",
 107   "Innovation",
 108   "OMA",
 109   "OMB",
 110   "SimulatedObservationAtBackground",
 111   "SimulatedObservationAtCurrentState",
 112   "SimulatedObservationAtOptimum",
 113   ].
 114
 115   Example :
 116   ``{"StoreSupplementaryCalculations":["CurrentState", "Residu"]}``
 117
 118 .. ------------------------------------ ..
 119 .. include:: snippets/Header2Algo04.rst
 120
 121 .. include:: snippets/Analysis.rst
 122
 123 .. include:: snippets/CostFunctionJ.rst
 124
 125 .. include:: snippets/CostFunctionJb.rst
 126
 127 .. include:: snippets/CostFunctionJo.rst
 128
 129 .. ------------------------------------ ..
 130 .. include:: snippets/Header2Algo05.rst
 131
 132 .. include:: snippets/Analysis.rst
 133
 134 .. include:: snippets/BMA.rst
 135
 136 .. include:: snippets/CostFunctionJ.rst
 137
 138 .. include:: snippets/CostFunctionJb.rst
 139
 140 .. include:: snippets/CostFunctionJo.rst
 141
 142 .. include:: snippets/CurrentIterationNumber.rst
 143
 144 .. include:: snippets/CurrentState.rst
 145
 146 .. include:: snippets/Innovation.rst
 147
 148 .. include:: snippets/OMA.rst
 149
 150 .. include:: snippets/OMB.rst
 151
 152 .. include:: snippets/SimulatedObservationAtBackground.rst
 153
 154 .. include:: snippets/SimulatedObservationAtCurrentState.rst
 155
 156 .. include:: snippets/SimulatedObservationAtOptimum.rst
 157
 158 .. ------------------------------------ ..
 159 .. _section_ref_algorithm_TabuSearch_examples:
 160
 161 .. include:: snippets/Header2Algo06.rst
 162
 163 - :ref:`section_ref_algorithm_DerivativeFreeOptimization`
 164 - :ref:`section_ref_algorithm_DifferentialEvolution`
 165 - :ref:`section_ref_algorithm_ParticleSwarmOptimization`
 166
 167 .. ------------------------------------ ..
 168 .. include:: snippets/Header2Algo07.rst
 169
 170 - [Glover89]_
 171 - [Glover90]_
 172 - [WikipediaTS]_