Merge branch 'OA_convexification' into OA_convexification-with-cuts4NLP

ZedongPeng · Nov 5, 2023 · b05020c · b05020c
2 parents c35bbbd + cb29149
commit b05020c
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diff --git a/pyomo/contrib/mindtpy/algorithm_base_class.py b/pyomo/contrib/mindtpy/algorithm_base_class.py
@@ -106,6 +106,8 @@ def __init__(self, **kwds):
         self.curr_int_sol = []
         self.should_terminate = False
         self.integer_list = []
+        # dictionary {integer solution (list): cuts index (list)}
+        self.int_sol_2_cuts_ind = dict()
 
         # Set up iteration counters
         self.nlp_iter = 0
@@ -794,6 +796,7 @@ def MindtPy_initialization(self):
             self.integer_list.append(self.curr_int_sol)
             fixed_nlp, fixed_nlp_result = self.solve_subproblem()
             self.handle_nlp_subproblem_tc(fixed_nlp, fixed_nlp_result)
+            self.int_sol_2_cuts_ind[self.curr_int_sol] = list(range(1, len(self.mip.MindtPy_utils.cuts.oa_cuts) + 1))
         elif config.init_strategy == 'FP':
             self.init_rNLP()
             self.fp_loop()

diff --git a/pyomo/contrib/mindtpy/single_tree.py b/pyomo/contrib/mindtpy/single_tree.py
@@ -941,15 +941,26 @@ def LazyOACallback_gurobi(cb_m, cb_opt, cb_where, mindtpy_solver, config):
                     )
                 return
             elif config.strategy == 'OA':
+                # Refer to the official document of GUROBI.
+                # Your callback should be prepared to cut off solutions that violate any of your lazy constraints, including those that have already been added. Node solutions will usually respect previously added lazy constraints, but not always.
+                # https://www.gurobi.com/documentation/current/refman/cs_cb_addlazy.html
+                # If this happens, MindtPy will look for the index of corresponding cuts, instead of solving the fixed-NLP again.
+                for ind in mindtpy_solver.int_sol_2_cuts_ind[mindtpy_solver.curr_int_sol]:
+                    cb_opt.cbLazy(mindtpy_solver.mip.MindtPy_utils.cuts.oa_cuts[ind])
                 return
         else:
             mindtpy_solver.integer_list.append(mindtpy_solver.curr_int_sol)
+            if config.strategy == 'OA':
+                cut_ind = len(mindtpy_solver.mip.MindtPy_utils.cuts.oa_cuts)
 
         # solve subproblem
         # The constraint linearization happens in the handlers
         fixed_nlp, fixed_nlp_result = mindtpy_solver.solve_subproblem()
 
         mindtpy_solver.handle_nlp_subproblem_tc(fixed_nlp, fixed_nlp_result, cb_opt)
+        if config.strategy == 'OA':
+            # store the cut index corresponding to current integer solution.
+            mindtpy_solver.int_sol_2_cuts_ind[mindtpy_solver.curr_int_sol] = list(range(cut_ind + 1, len(mindtpy_solver.mip.MindtPy_utils.cuts.oa_cuts) + 1))
 
 
 def handle_lazy_main_feasible_solution_gurobi(cb_m, cb_opt, mindtpy_solver, config):