Implementing multistart version of theta_est using multiple sampling methods

pyomo/contrib/parmest/examples/reactor_design/multistart_example.py

@@ -0,0 +1,48 @@
+#  ___________________________________________________________________________
+#
+#  Pyomo: Python Optimization Modeling Objects
+#  Copyright (c) 2008-2025
+#  National Technology and Engineering Solutions of Sandia, LLC
+#  Under the terms of Contract DE-NA0003525 with National Technology and
+#  Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+#  rights in this software.
+#  This software is distributed under the 3-clause BSD License.
+#  ___________________________________________________________________________
+
+from pyomo.common.dependencies import pandas as pd
+from os.path import join, abspath, dirname
+import pyomo.contrib.parmest.parmest as parmest
+from pyomo.contrib.parmest.examples.reactor_design.reactor_design import (
+    ReactorDesignExperiment,
+)
+
+
+def main():
+
+    # Read in data
+    file_dirname = dirname(abspath(str(__file__)))
+    file_name = abspath(join(file_dirname, "reactor_data.csv"))
+    data = pd.read_csv(file_name)
+
+    # Create an experiment list
+    exp_list = []
+    for i in range(data.shape[0]):
+        exp_list.append(ReactorDesignExperiment(data, i))
+
+    # View one model
+    # exp0_model = exp_list[0].get_labeled_model()
+    # exp0_model.pprint()
+
+    pest = parmest.Estimator(exp_list, obj_function='SSE')
+
+    # Parameter estimation
+    obj, theta = pest.theta_est()
+
+    # Parameter estimation with multistart to avoid local minima
+    obj, theta = pest.theta_est_multistart(
+        num_starts=10, start_method='random', random_seed=42, max_iter=1000, tol=1e-6
+    )
+
+
+if __name__ == "__main__":
+    main()

pyomo/contrib/parmest/experiment.py

@@ -29,3 +29,10 @@ def __init__(self, model=None):
 
     def get_labeled_model(self):
         return self.model
+
+    def reinitialize_unknown_parameters(self):
+        raise NotImplementedError(
+            "The reinitialize_unknown_parameters method should implemented in the subclass."
+            "Thi method will take new values for the unknown parameters from the Suffix "
+            "and allow users to reinitialize the model."
+        )