Merge branch 'main' into fix-572

bknueven · web-flow · commit 5409b65673ca · 2025-09-19T13:38:04.000-06:00
diff --git a/examples/farmer/agnostic/farmer_ampl_agnostic.py b/examples/farmer/agnostic/farmer_ampl_agnostic.py
@@ -286,7 +286,7 @@ def solve_one(Ag, s, solve_keyword_args, gripe, tee, need_solution=True):
     #gs.export_data(f"{fname}.dat")
     
     if gs.solve_result != "solved":
-        s._mpisppy_data.scenario_feasible = False
+        s._mpisppy_data.solution_available = False
         if gripe:
             print (f"Solve failed for scenario {s.name} on rank {global_rank}")
             print(f"{gs.solve_result =}")
@@ -295,7 +295,7 @@ def solve_one(Ag, s, solve_keyword_args, gripe, tee, need_solution=True):
         raise solver_exception
 
 
-    s._mpisppy_data.scenario_feasible = True
+    s._mpisppy_data.solution_available = True
     # For AMPL mips, we need to use the gap option to compute bounds
     # https://amplmp.readthedocs.io/rst/features-guide.html
     objobj = gs.get_current_objective()  # different for xhatters
diff --git a/examples/farmer/agnostic/farmer_gurobipy_agnostic.py b/examples/farmer/agnostic/farmer_gurobipy_agnostic.py
@@ -253,18 +253,18 @@ def solve_one(Ag, s, solve_keyword_args, gripe, tee, need_solution=True):
         gs.optimize()
     except gp.GurobiError as e:
         print(f"Error occurred: {str(e)}")
-        s._mpisppy_data.scenario_feasible = False
+        s._mpisppy_data.solution_available = False
         if gripe:
             print(f"Solve failed for scenario {s.name}")
         return
 
     if gs.status != gp.GRB.Status.OPTIMAL:
-        s._mpisppy_data.scenario_feasible = False
+        s._mpisppy_data.solution_available = False
         if gripe:
             print(f"Solve failed for scenario {s.name}")
         return
 
-    s._mpisppy_data.scenario_feasible = True
+    s._mpisppy_data.solution_available = True
     
     # Objective value extraction
     objval = gs.getObjective().getValue()
diff --git a/examples/farmer/agnostic/farmer_pyomo_agnostic.py b/examples/farmer/agnostic/farmer_pyomo_agnostic.py
@@ -206,7 +206,7 @@ def solve_one(Ag, s, solve_keyword_args, gripe, tee=False, need_solution=True):
             (results.solver.termination_condition == TerminationCondition.infeasibleOrUnbounded) or \
             (results.solver.termination_condition == TerminationCondition.unbounded):
 
-        s._mpisppy_data.scenario_feasible = False
+        s._mpisppy_data.solution_available = False
 
         if gripe:
             print (f"Solve failed for scenario {s.name} on rank {global_rank}")
@@ -219,7 +219,7 @@ def solve_one(Ag, s, solve_keyword_args, gripe, tee=False, need_solution=True):
             raise solver_exception
 
     else:
-        s._mpisppy_data.scenario_feasible = True
+        s._mpisppy_data.solution_available = True
         if gd["sense"] == pyo.minimize:
             s._mpisppy_data.outer_bound = results.Problem[0].Lower_bound
         else:
diff --git a/mpisppy/agnostic/ampl_guest.py b/mpisppy/agnostic/ampl_guest.py
@@ -273,15 +273,15 @@ def solve_one(self, Ag, s, solve_keyword_args, gripe, tee=False, need_solution=T
             solver_exception = e
 
         if gs.solve_result != "solved":
-            s._mpisppy_data.scenario_feasible = False
+            s._mpisppy_data.solution_available = False
             if gripe:
                 print (f"Solve failed for scenario {s.name} on rank {global_rank}")
                 print(f"{gs.solve_result =}")
             s._mpisppy_data.inner_bound = None
             return
 
         else:
-            s._mpisppy_data.scenario_feasible = True
+            s._mpisppy_data.solution_available = True
 
         if solver_exception is not None and need_solution:
             raise solver_exception
diff --git a/mpisppy/agnostic/examples/farmer_gurobipy_model.py b/mpisppy/agnostic/examples/farmer_gurobipy_model.py
@@ -163,7 +163,7 @@ def solve_one(Ag, s, solve_keyword_args, gripe, tee, need_solution=True):
     try:
         gs.optimize()
     except gp.GurobiError as e:
-        s._mpisppy_data.scenario_feasible = False
+        s._mpisppy_data.solution_available = False
         solver_exception = e
         if gripe:
             print(f"Solve failed with error {str(e)} for scenario {s.name}")
@@ -174,12 +174,12 @@ def solve_one(Ag, s, solve_keyword_args, gripe, tee, need_solution=True):
         raise solver_exception
 
     if gs.status != gp.GRB.Status.OPTIMAL:
-        s._mpisppy_data.scenario_feasible = False
+        s._mpisppy_data.solution_available = False
         if gripe:
             print(f"Solve failed for scenario {s.name}")
         return
 
-    s._mpisppy_data.scenario_feasible = True
+    s._mpisppy_data.solution_available = True
     
     # Objective value extraction
     objval = gs.getObjective().getValue()
diff --git a/mpisppy/agnostic/gams_guest.py b/mpisppy/agnostic/gams_guest.py
@@ -200,7 +200,7 @@ def solve_one(self, Ag, s, solve_keyword_args, gripe, tee, need_solution=True):
 
         #print(f"{gs.model_status=}, {gs.solver_status=}")
         if gs.model_status not in solve_ok:
-            s._mpisppy_data.scenario_feasible = False
+            s._mpisppy_data.solution_available = False
             if gripe:
                 print (f"Solve failed for scenario {s.name} on rank {global_rank}")
                 print(f"{gs.model_status =}")
@@ -210,7 +210,7 @@ def solve_one(self, Ag, s, solve_keyword_args, gripe, tee, need_solution=True):
         if solver_exception is not None and need_solution:
             raise solver_exception
         
-        s._mpisppy_data.scenario_feasible = True
+        s._mpisppy_data.solution_available = True
 
         # For debugging, thisprints the weights of the PH model
         """W_dict = {}
diff --git a/mpisppy/agnostic/pyomo_guest.py b/mpisppy/agnostic/pyomo_guest.py
@@ -227,7 +227,7 @@ def solve_one(self, Ag, s, solve_keyword_args, gripe, tee=False, need_solution=T
                 (results.solver.termination_condition == TerminationCondition.infeasibleOrUnbounded) or \
                 (results.solver.termination_condition == TerminationCondition.unbounded):
 
-            s._mpisppy_data.scenario_feasible = False
+            s._mpisppy_data.solution_available = False
 
             if gripe:
                 print (f"Solve failed for scenario {s.name} on rank {global_rank}")
@@ -240,7 +240,7 @@ def solve_one(self, Ag, s, solve_keyword_args, gripe, tee=False, need_solution=T
                 raise solver_exception
 
         else:
-            s._mpisppy_data.scenario_feasible = True
+            s._mpisppy_data.solution_available = True
             if gd["sense"] == pyo.minimize:
                 s._mpisppy_data.outer_bound = results.Problem[0].Lower_bound
             else:
diff --git a/mpisppy/cylinders/lshaped_bounder.py b/mpisppy/cylinders/lshaped_bounder.py
@@ -41,7 +41,7 @@ def xhatlshaped_prep(self):
                 print("Total probability of scenarios was ", self.opt.E1)
                 print("E1_tolerance = ", self.opt.E1_tolerance)
             quit()
-        infeasP = self.opt.infeas_prob()
+        infeasP = self.opt.no_incumbent_prob()
         if infeasP != 0.:
             if self.opt.cylinder_rank == 0:
                 print("ERROR")
diff --git a/mpisppy/extensions/xhatbase.py b/mpisppy/extensions/xhatbase.py
@@ -225,7 +225,7 @@ def _try_one(self, snamedict, solver_options=None, verbose=False,
                            need_solution=False,
                            )
 
-        infeasP = self.opt.infeas_prob()
+        infeasP = self.opt.no_incumbent_prob()
         if infeasP != 0.:
             if restore_nonants:
                 self.opt._restore_nonants()
diff --git a/mpisppy/extensions/xhatxbar.py b/mpisppy/extensions/xhatxbar.py
@@ -97,7 +97,7 @@ def _vb(msg):
                             need_solution=False,
                             )
 
-        infeasP = self.opt.infeas_prob()
+        infeasP = self.opt.no_incumbent_prob()
         if infeasP != 0.:
             if restore_nonants:
                 self.opt._restore_nonants()
diff --git a/mpisppy/opt/fwph.py b/mpisppy/opt/fwph.py
@@ -425,7 +425,7 @@ def SDM(self, model_name, mip_solver_options, dtiming, tee, verbose, sdm_iter_li
             self._remove_objective_cutoff(mip)
             # tmipsolve = time.perf_counter() - tbmipsolve
 
-            if mip._mpisppy_data.scenario_feasible:
+            if mip._mpisppy_data.solution_available:
 
                 # Algorithm 2 line 9 (compute \Gamma^t)
                 inner_bound = mip._mpisppy_data.inner_bound
@@ -473,7 +473,7 @@ def SDM(self, model_name, mip_solver_options, dtiming, tee, verbose, sdm_iter_li
             if (itr == 0):
                 dual_bound = mip._mpisppy_data.outer_bound
 
-            if itr + 1 == sdm_iter_limit or not mip._mpisppy_data.scenario_feasible or gamma_t < FW_conv_thresh:
+            if itr + 1 == sdm_iter_limit or not mip._mpisppy_data.solution_available or gamma_t < FW_conv_thresh:
                 return dual_bound
             else:
                 yield dual_bound
diff --git a/mpisppy/phbase.py b/mpisppy/phbase.py
@@ -927,7 +927,7 @@ def _vb(msg):
         self._update_E1()  # Apologies for doing this after the solves...
         if (abs(1 - self.E1) > self.E1_tolerance):
             raise RuntimeError(f"Total probability of scenarios was {self.E1};  E1_tolerance = ", self.E1_tolerance)
-        feasP = self.feas_prob()
+        feasP = self.incumbent_prob()
         if feasP != self.E1:
             raise RuntimeError(f"Infeasibility detected; E_feas={feasP}, E1={self.E1}")
 
diff --git a/mpisppy/spopt.py b/mpisppy/spopt.py
@@ -236,7 +236,7 @@ def _vb(msg):
                 solver_exception = e
 
             if sputils.not_good_enough_results(results):
-                s._mpisppy_data.scenario_feasible = False
+                s._mpisppy_data.solution_available = False
 
                 if gripe:
                     print (f"[{self._get_cylinder_name()}] Solve failed for scenario {s.name}")
@@ -273,17 +273,16 @@ def _vb(msg):
                 else:
                     s._mpisppy_data.outer_bound = results.Problem[0].Upper_bound
                     s._mpisppy_data.inner_bound = results.Problem[0].Lower_bound
-                s._mpisppy_data.scenario_feasible = True
-            # TBD: get this ready for IPopt (e.g., check feas_prob every time)
+            # TBD: get this ready for IPopt (e.g., check incumbent_prob every time)
             # propogate down
             if self.bundling: # must be a bundle
                 for sname in s._ef_scenario_names:
-                     self.local_scenarios[sname]._mpisppy_data.scenario_feasible\
-                         = s._mpisppy_data.scenario_feasible
-                     if s._mpisppy_data.scenario_feasible:
+                     self.local_scenarios[sname]._mpisppy_data.solution_available\
+                         = s._mpisppy_data.solution_available
+                     if s._mpisppy_data.solution_available:
                          self._check_staleness(self.local_scenarios[sname])
             else:  # not a bundle
-                if s._mpisppy_data.scenario_feasible:
+                if s._mpisppy_data.solution_available:
                     self._check_staleness(s)
 
         # end of Agnostic bypass
@@ -499,62 +498,62 @@ def _update_E1(self):
         self.E1 = float(globalP[0])
 
 
-    def feas_prob(self):
-        """ Compute the total probability of all feasible scenarios.
+    def incumbent_prob(self):
+        """ Compute the total probability of all scenarios with a solution available.
 
-        This function can be used to check whether all scenarios are feasible
+        This function can be used to check whether all scenarios have a solution available
         by comparing the return value to one.
 
         Note:
             This function assumes the scenarios have a boolean
-            `_mpisppy_data.scenario_feasible` attribute.
+            `_mpisppy_data.solution_available` attribute.
 
         Returns:
             float:
-                Sum of the scenario probabilities over all feasible scenarios.
-                This value equals E1 if all scenarios are feasible.
+                Sum of the scenario probabilities over all scenarios with an available solution.
+                This value equals E1 if all scenarios have a solution available.
         """
 
         # locals[0] is E_feas and locals[1] is E_1
-        locals = np.zeros(1, dtype='d')
-        globals = np.zeros(1, dtype='d')
+        localP = np.zeros(1, dtype='d')
+        globalP = np.zeros(1, dtype='d')
 
         for k,s in self.local_scenarios.items():
-            if s._mpisppy_data.scenario_feasible:
-                locals[0] += s._mpisppy_probability
+            if s._mpisppy_data.solution_available:
+                localP[0] += s._mpisppy_probability
 
-        self.mpicomm.Allreduce([locals, MPI.DOUBLE],
-                           [globals, MPI.DOUBLE],
+        self.mpicomm.Allreduce([localP, MPI.DOUBLE],
+                           [globalP, MPI.DOUBLE],
                            op=MPI.SUM)
 
-        return float(globals[0])
+        return float(globalP[0])
 
 
-    def infeas_prob(self):
-        """ Sum the total probability for all infeasible scenarios.
+    def no_incumbent_prob(self):
+        """ Sum the total probability for all scenarios with no solution available.
 
         Note:
             This function assumes the scenarios have a boolean
-            `_mpisppy_data.scenario_feasible` attribute.
+            `_mpisppy_data.solution_available` attribute.
 
         Returns:
             float:
-                Sum of the scenario probabilities over all infeasible scenarios.
-                This value equals 0 if all scenarios are feasible.
+                Sum of the scenario probabilities over all scenarios with no solution available.
+                This value equals 0 if all scenarios are feasible and have a solution available.
         """
 
-        locals = np.zeros(1, dtype='d')
-        globals = np.zeros(1, dtype='d')
+        localP = np.zeros(1, dtype='d')
+        globalP = np.zeros(1, dtype='d')
 
         for k,s in self.local_scenarios.items():
-            if not s._mpisppy_data.scenario_feasible:
-                locals[0] += s._mpisppy_probability
+            if not s._mpisppy_data.solution_available:
+                localP[0] += s._mpisppy_probability
 
-        self.mpicomm.Allreduce([locals, MPI.DOUBLE],
-                           [globals, MPI.DOUBLE],
+        self.mpicomm.Allreduce([localP, MPI.DOUBLE],
+                           [globalP, MPI.DOUBLE],
                            op=MPI.SUM)
 
-        return float(globals[0])
+        return float(globalP[0])
 
 
     def avg_min_max(self, compstr):
diff --git a/mpisppy/utils/sputils.py b/mpisppy/utils/sputils.py
@@ -329,7 +329,7 @@ def _create_EF_from_scen_dict(scen_dict, EF_name=None,
     # of single scenarios and bundles when convenient
     EF_instance._mpisppy_data = pyo.Block(name="For non-Pyomo mpi-sppy data")
     EF_instance._mpisppy_model = pyo.Block(name="For mpi-sppy Pyomo additions to the scenario model")
-    EF_instance._mpisppy_data.scenario_feasible = None
+    EF_instance._mpisppy_data.solution_available = None
 
     EF_instance._ef_scenario_names = []
     EF_instance._mpisppy_probability = 0
diff --git a/mpisppy/utils/xhat_eval.py b/mpisppy/utils/xhat_eval.py
@@ -403,7 +403,7 @@ def calculate_incumbent(self, fix_nonants=True, verbose=False):
         self.solve_loop(solver_options=self.current_solver_options, 
                         verbose=verbose)
 
-        infeasP = self.infeas_prob()
+        infeasP = self.no_incumbent_prob()
         if infeasP != 0.:
             return None
         else:

Original file line number	Diff line number	Diff line change
`@@ -225,7 +225,7 @@ def _try_one(self, snamedict, solver_options=None, verbose=False,`
`225`	`225`	`need_solution=False,`
`226`	`226`	`)`
`227`	`227`
`228`		`- infeasP = self.opt.infeas_prob()`
	`228`	`+ infeasP = self.opt.no_incumbent_prob()`
`229`	`229`	`if infeasP != 0.:`
`230`	`230`	`if restore_nonants:`
`231`	`231`	`self.opt._restore_nonants()`
Original file line number	Diff line number	Diff line change
`@@ -97,7 +97,7 @@ def _vb(msg):`
`97`	`97`	`need_solution=False,`
`98`	`98`	`)`
`99`	`99`
`100`		`- infeasP = self.opt.infeas_prob()`
	`100`	`+ infeasP = self.opt.no_incumbent_prob()`
`101`	`101`	`if infeasP != 0.:`
`102`	`102`	`if restore_nonants:`
`103`	`103`	`self.opt._restore_nonants()`