# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2019 EDF R&D
+# Copyright (C) 2008-2021 EDF R&D
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
"CostFunctionJbAtCurrentOptimum",
"CostFunctionJo",
"CostFunctionJoAtCurrentOptimum",
+ "CurrentIterationNumber",
"CurrentOptimum",
"CurrentState",
"IndexOfOptimum",
name = "Bounds",
message = "Liste des valeurs de bornes",
)
+ self.defineRequiredParameter(
+ name = "InitializationPoint",
+ typecast = numpy.ravel,
+ message = "État initial imposé (par défaut, c'est l'ébauche si None)",
+ )
self.requireInputArguments(
mandatory= ("Xb", "Y", "HO", "R"),
)
+ self.setAttributes(tags=(
+ "Optimization",
+ "NonLinear",
+ "Variational",
+ ))
def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
- self._pre_run(Parameters, Xb, Y, R, B, Q)
- #
- # Correction pour pallier a un bug de TNC sur le retour du Minimum
- if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
- self.setParameterValue("StoreInternalVariables",True)
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
# Opérateurs
# ----------
Jo = float( 0.5 * _Innovation.T * RI * _Innovation )
J = Jb + Jo
#
+ self.StoredVariables["CurrentIterationNumber"].store( len(self.StoredVariables["CostFunctionJ"]) )
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
#
# Point de démarrage de l'optimisation : Xini = Xb
# ------------------------------------
- Xini = numpy.ravel(Xb)
+ Xini = self._parameters["InitializationPoint"]
#
# Minimisation de la fonctionnelle
# --------------------------------