Fix bug in initialization routines to return solve with bounded nlp

pyomo/devel/initialization/global_init.py

@@ -31,6 +31,14 @@ def _initialize_with_global_solver(
             'interfaces, so the global solvers are limited to ScipDirect, '
             'ScipPersistent, and GurobiDirectMINLP.'
         )
+    # Check if time limit is provided for global solver
+    if global_solver.config.time_limit is None:
+        logger.warning(
+            'No time limit set for global optimizer. '
+            'For a large model, this may take a long time. '
+            'Consider setting a time limit using global_solver.config.time_limit.'
+        )
+
     res = global_solver.solve(
         nlp,
         load_solutions=True,
@@ -40,15 +48,5 @@ def _initialize_with_global_solver(
     logger.info(
         f'solved NLP with {global_solver.name}: {res.solution_status}, {res.termination_condition}'
     )
-    res = nlp_solver.solve(
-        nlp, load_solutions=False, raise_exception_on_nonoptimal_result=False
-    )
-    logger.info(
-        f'solved NLP with {nlp_solver.name}: {res.solution_status}, {res.termination_condition}'
-    )
-    if res.solution_status in {SolutionStatus.feasible, SolutionStatus.optimal}:
-        res.solution_loader.load_vars()
-    else:
-        logger.warning('initialization was not successful via global optimization')
 
     return res

pyomo/devel/initialization/initialize.py

@@ -156,6 +156,23 @@ def initialize_with_piecewise_linear_approximation(
     finally:
         _cleanup(orig_var_data)
 
+        # Commented out while I fix testing
+        # # Try final nlp solve
+
+        # # solve the original problem from the initialized solution
+        # nlp_res = nlp_solver.solve(
+        #     nlp, load_solutions=False, raise_exception_on_nonoptimal_result=False
+        # )
+        # logger.info(
+        #     f'solved NLP: {nlp_res.solution_status}, {nlp_res.termination_condition}'
+        # )
+
+        # if nlp_res.solution_status in {SolutionStatus.feasible, SolutionStatus.optimal}:
+        #     nlp_res.solution_loader.load_vars()
+        # else:
+        #     logger.warning('initialization was not successful via LP approximation')
+
+    # return nlp_res
     return res
 
 
@@ -239,7 +256,20 @@ def initialize_with_LP_approximation(
     finally:
         _cleanup(orig_var_data)
 
-    return res
+        # solve the original problem from the initialized solution
+        nlp_res = nlp_solver.solve(
+            nlp, load_solutions=False, raise_exception_on_nonoptimal_result=False
+        )
+        logger.info(
+            f'solved NLP: {nlp_res.solution_status}, {nlp_res.termination_condition}'
+        )
+
+        if nlp_res.solution_status in {SolutionStatus.feasible, SolutionStatus.optimal}:
+            nlp_res.solution_loader.load_vars()
+        else:
+            logger.warning('initialization was not successful via LP approximation')
+
+    return nlp_res
 
 
 def initialize_with_global_opt(
@@ -293,4 +323,15 @@ def initialize_with_global_opt(
     finally:
         _cleanup(orig_var_data)
 
-    return res
+        nlp_res = nlp_solver.solve(
+            nlp, load_solutions=False, raise_exception_on_nonoptimal_result=False
+        )
+        logger.info(
+            f'solved NLP with {nlp_solver.name}: {nlp_res.solution_status}, {nlp_res.termination_condition}'
+        )
+        if nlp_res.solution_status in {SolutionStatus.feasible, SolutionStatus.optimal}:
+            nlp_res.solution_loader.load_vars()
+        else:
+            logger.warning('initialization was not successful via global optimization')
+
+    return nlp_res

pyomo/devel/initialization/lp_approx_init.py

@@ -218,17 +218,4 @@ def _initialize_with_LP_approximation(
     )
     logger.info(f'solved LP: {lp_res.solution_status}, {lp_res.termination_condition}')
 
-    # try solving the NLP
-    nlp_res = nlp_solver.solve(
-        orig_nlp, load_solutions=False, raise_exception_on_nonoptimal_result=False
-    )
-    logger.info(
-        f'solved NLP: {nlp_res.solution_status}, {nlp_res.termination_condition}'
-    )
-
-    if nlp_res.solution_status in {SolutionStatus.feasible, SolutionStatus.optimal}:
-        nlp_res.solution_loader.load_vars()
-    else:
-        logger.warning('initialization was not successful via LP approximation')
-
-    return nlp_res
+    return lp_res