replace polynomial_degree condition

.gitignore

@@ -24,3 +24,4 @@ gurobi.log
 
 # Jupyterhub/Jupyterlab checkpoints
 .ipynb_checkpoints
+cplex.log

pyomo/contrib/gdpopt/util.py

@@ -290,14 +290,17 @@ def build_ordered_component_lists(model, solve_data):
             model.component_data_objects(
                 ctype=Constraint, active=True,
                 descend_into=(Block, Disjunct))))
+    # print(util_blk.constraint_list)
     setattr(
         util_blk, 'linear_constraint_list', list(c for c in model.component_data_objects(
             ctype=Constraint, active=True, descend_into=(Block, Disjunct))
-            if c.body.polynomial_degree in (0, 1)))
+            if c.body.polynomial_degree() in (0, 1)))
+    # print(util_blk.linear_constraint_list)
     setattr(
         util_blk, 'nonlinear_constraint_list', list(c for c in model.component_data_objects(
             ctype=Constraint, active=True, descend_into=(Block, Disjunct))
-            if c.body.polynomial_degree not in (0, 1)))
+            if c.body.polynomial_degree() not in (0, 1)))
+    # print(util_blk.nonlinear_constraint_list)
     setattr(
         util_blk, 'disjunct_list', list(
             model.component_data_objects(

pyomo/contrib/mindtpy/MindtPy.py

@@ -209,8 +209,7 @@ def solve(self, model, **kwds):
             lin.mip_iters = Set(dimen=1)
 
             if config.feasibility_norm == 'L1' or config.feasibility_norm == 'L2':
-                feas.nl_constraint_set = Set(initialize=[i for i, constr in enumerate(MindtPy.constraint_list, 1) if
-                                                         constr.body.polynomial_degree() not in (1, 0)],
+                feas.nl_constraint_set = Set(initialize=[i for i, constr in enumerate(MindtPy.nonlinear_constraint_list, 1)],
                                              doc="Integer index set over the nonlinear constraints."
                                              "The set corresponds to the index of nonlinear constraint in constraint_set")
                 # Create slack variables for feasibility problem

pyomo/contrib/mindtpy/cut_generation.py

@@ -106,11 +106,7 @@ def add_ecp_cuts(target_model, solve_data, config,
         linearized constraint has been violated
     """
     with time_code(solve_data.timing, 'ECP cut generation'):
-        for constr in target_model.MindtPy_utils.constraint_list:
-
-            if constr.body.polynomial_degree() in (0, 1):
-                continue
-
+        for constr in target_model.MindtPy_utils.nonlinear_constraint_list:
             constr_vars = list(identify_variables(constr.body))
             jacs = solve_data.jacobians
 
@@ -236,10 +232,7 @@ def add_affine_cuts(solve_data, config):
         config.logger.info("Adding affine cuts")
         counter = 0
 
-        for constr in m.MindtPy_utils.constraint_list:
-            if constr.body.polynomial_degree() in (1, 0):
-                continue
-
+        for constr in m.MindtPy_utils.nonlinear_constraint_list:
             vars_in_constr = list(
                 identify_variables(constr.body))
             if any(var.value is None for var in vars_in_constr):

pyomo/contrib/mindtpy/initialization.py

@@ -200,9 +200,8 @@ def init_max_binaries(solve_data, config):
     config.logger.info(
         "MILP %s: maximize value of binaries" %
         (solve_data.mip_iter))
-    for c in MindtPy.constraint_list:
-        if c.body.polynomial_degree() not in (1, 0):
-            c.deactivate()
+    for c in MindtPy.nonlinear_constraint_list:
+        c.deactivate()
     objective = next(m.component_data_objects(Objective, active=True))
     objective.deactivate()
     binary_vars = (
@@ -238,11 +237,11 @@ def init_max_binaries(solve_data, config):
             'MILP master problem is infeasible. '
             'Problem may have no more feasible '
             'binary configurations.')
-    elif subprob_terminate_cond is tc.maxTimeLimit:
+    elif solve_terminate_cond is tc.maxTimeLimit:
         config.logger.info(
             'NLP subproblem failed to converge within time limit.')
         solve_data.results.solver.termination_condition = tc.maxTimeLimit
-    elif subprob_terminate_cond is tc.maxIterations:
+    elif solve_terminate_cond is tc.maxIterations:
         config.logger.info(
             'NLP subproblem failed to converge within iteration limit.')
     else:

pyomo/contrib/mindtpy/iterate.py

@@ -241,8 +241,7 @@ def algorithm_should_terminate(solve_data, config, check_cycling):
     if config.strategy == 'ECP':
         # check to see if the nonlinear constraints are satisfied
         MindtPy = solve_data.working_model.MindtPy_utils
-        nonlinear_constraints = [c for c in MindtPy.constraint_list if
-                                 c.body.polynomial_degree() not in (1, 0)]
+        nonlinear_constraints = [c for c in MindtPy.nonlinear_constraint_list]
         for nlc in nonlinear_constraints:
             if nlc.has_lb():
                 try:

pyomo/contrib/mindtpy/mip_solve.py

@@ -45,9 +45,8 @@ def solve_master(solve_data, config, feas_pump=False):
     MindtPy = solve_data.mip.MindtPy_utils
 
     # Set up MILP
-    for c in MindtPy.constraint_list:
-        if c.body.polynomial_degree() not in (1, 0):
-            c.deactivate()
+    for c in MindtPy.nonlinear_constraint_list:
+        c.deactivate()
 
     MindtPy.MindtPy_linear_cuts.activate()
     main_objective = next(

pyomo/contrib/mindtpy/single_tree.py

@@ -158,9 +158,7 @@ def add_lazy_affine_cuts(self, solve_data, config, opt):
             config.logger.info("Adding affine cuts")
             counter = 0
 
-            for constr in m.MindtPy_utils.constraint_list:
-                if constr.body.polynomial_degree() in (1, 0):
-                    continue
+            for constr in m.MindtPy_utils.nonlinear_constraint_list:
 
                 vars_in_constr = list(
                     identify_variables(constr.body))

pyomo/contrib/mindtpy/util.py

@@ -98,9 +98,7 @@ def calc_jacobians(solve_data, config):
         mode = differentiate.Modes.reverse_symbolic
     elif config.differentiate_mode == "sympy":
         mode = differentiate.Modes.sympy
-    for c in solve_data.mip.MindtPy_utils.constraint_list:
-        if c.body.polynomial_degree() in (1, 0):
-            continue  # skip linear constraints
+    for c in solve_data.mip.MindtPy_utils.nonlinear_constraint_list:
         vars_in_constr = list(EXPR.identify_variables(c.body))
         jac_list = differentiate(
             c.body, wrt_list=vars_in_constr, mode=mode)
@@ -160,21 +158,20 @@ def var_bound_add(solve_data, config):
     """
     m = solve_data.working_model
     MindtPy = m.MindtPy_utils
-    for c in MindtPy.constraint_list:
-        if c.body.polynomial_degree() not in (1, 0):
-            for var in list(EXPR.identify_variables(c.body)):
-                if var.has_lb() and var.has_ub():
-                    continue
-                elif not var.has_lb():
-                    if var.is_integer():
-                        var.setlb(-config.integer_var_bound - 1)
-                    else:
-                        var.setlb(-config.continuous_var_bound - 1)
-                elif not var.has_ub():
-                    if var.is_integer():
-                        var.setub(config.integer_var_bound)
-                    else:
-                        var.setub(config.continuous_var_bound)
+    for c in MindtPy.nonlinear_constraint_list:
+        for var in list(EXPR.identify_variables(c.body)):
+            if var.has_lb() and var.has_ub():
+                continue
+            elif not var.has_lb():
+                if var.is_integer():
+                    var.setlb(-config.integer_var_bound - 1)
+                else:
+                    var.setlb(-config.continuous_var_bound - 1)
+            elif not var.has_ub():
+                if var.is_integer():
+                    var.setub(config.integer_var_bound)
+                else:
+                    var.setub(config.continuous_var_bound)
 
 
 def generate_norm2sq_objective_function(model, setpoint_model, discrete_only=False):