Minor source update for OM compatibility

[modules/adao.git] / src / daComposant / daAlgorithms / DerivativeFreeOptimization.py

diff --git a/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py b/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
index c5c2a578bcc3489524b48c330676dbca45433d1f..5f34955be8c3eea1905363b7e7a61eb1cc40b40b 100644 (file)
--- a/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
+++ b/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
@@ -24,6 +24,7 @@ import numpy, logging, scipy.optimize
 from daCore import BasicObjects, PlatformInfo
 from daCore.NumericObjects import ApplyBounds, ForceNumericBounds
 from daCore.PlatformInfo import vfloat
+lpi = PlatformInfo.PlatformInfo()
 
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
@@ -134,13 +135,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 "NonLocalOptimization",
                 "DerivativeFree",
                 "ParallelFree",
+                "ConvergenceOnBoth",
             ),
         )
 
     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, U, HO, EM, CM, R, B, Q)
         #
-        if not PlatformInfo.has_nlopt and not self._parameters["Minimizer"] in ["COBYLA", "POWELL", "SIMPLEX"]:
+        if not lpi.has_nlopt and not self._parameters["Minimizer"] in ["COBYLA", "POWELL", "SIMPLEX"]:
             logging.warning(
                 "%s Minimization by SIMPLEX is forced because %s "%(self._name, self._parameters["Minimizer"]) + \
                 "is unavailable (COBYLA, POWELL are also available)")
@@ -238,7 +240,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 full_output = True,
                 disp        = self._parameters["optdisp"],
             )
-        elif self._parameters["Minimizer"] == "COBYLA" and not PlatformInfo.has_nlopt:
+        elif self._parameters["Minimizer"] == "COBYLA" and not lpi.has_nlopt:
             def make_constraints(bounds):
                 constraints = []
                 for (i, (a, b)) in enumerate(bounds):
@@ -262,7 +264,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 catol       = 2. * self._parameters["StateVariationTolerance"],
                 disp        = self._parameters["optdisp"],
             )
-        elif self._parameters["Minimizer"] == "COBYLA" and PlatformInfo.has_nlopt:
+        elif self._parameters["Minimizer"] == "COBYLA" and lpi.has_nlopt:
             import nlopt
             opt = nlopt.opt(nlopt.LN_COBYLA, Xini.size)
 
@@ -289,7 +291,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 print("%s: optimal state: %s"%(opt.get_algorithm_name(), Minimum))
                 print("%s: minimum of J: %s"%(opt.get_algorithm_name(), opt.last_optimum_value()))
                 print("%s: return code: %i"%(opt.get_algorithm_name(), opt.last_optimize_result()))
-        elif self._parameters["Minimizer"] == "SIMPLEX" and not PlatformInfo.has_nlopt:
+        elif self._parameters["Minimizer"] == "SIMPLEX" and not lpi.has_nlopt:
             Minimum, J_optimal, niter, nfeval, rc = scipy.optimize.fmin(
                 func        = CostFunction,
                 x0          = Xini,
@@ -301,7 +303,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 full_output = True,
                 disp        = self._parameters["optdisp"],
             )
-        elif self._parameters["Minimizer"] == "SIMPLEX" and PlatformInfo.has_nlopt:
+        elif self._parameters["Minimizer"] == "SIMPLEX" and lpi.has_nlopt:
             import nlopt
             opt = nlopt.opt(nlopt.LN_NELDERMEAD, Xini.size)
 
@@ -328,7 +330,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 print("%s: optimal state: %s"%(opt.get_algorithm_name(), Minimum))
                 print("%s: minimum of J: %s"%(opt.get_algorithm_name(), opt.last_optimum_value()))
                 print("%s: return code: %i"%(opt.get_algorithm_name(), opt.last_optimize_result()))
-        elif self._parameters["Minimizer"] == "BOBYQA" and PlatformInfo.has_nlopt:
+        elif self._parameters["Minimizer"] == "BOBYQA" and lpi.has_nlopt:
             import nlopt
             opt = nlopt.opt(nlopt.LN_BOBYQA, Xini.size)
 
@@ -355,7 +357,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 print("%s: optimal state: %s"%(opt.get_algorithm_name(), Minimum))
                 print("%s: minimum of J: %s"%(opt.get_algorithm_name(), opt.last_optimum_value()))
                 print("%s: return code: %i"%(opt.get_algorithm_name(), opt.last_optimize_result()))
-        elif self._parameters["Minimizer"] == "NEWUOA" and PlatformInfo.has_nlopt:
+        elif self._parameters["Minimizer"] == "NEWUOA" and lpi.has_nlopt:
             import nlopt
             opt = nlopt.opt(nlopt.LN_NEWUOA, Xini.size)
 
@@ -382,7 +384,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 print("%s: optimal state: %s"%(opt.get_algorithm_name(), Minimum))
                 print("%s: minimum of J: %s"%(opt.get_algorithm_name(), opt.last_optimum_value()))
                 print("%s: return code: %i"%(opt.get_algorithm_name(), opt.last_optimize_result()))
-        elif self._parameters["Minimizer"] == "SUBPLEX" and PlatformInfo.has_nlopt:
+        elif self._parameters["Minimizer"] == "SUBPLEX" and lpi.has_nlopt:
             import nlopt
             opt = nlopt.opt(nlopt.LN_SBPLX, Xini.size)