Various changes

1. Fixed collection of bounds information from Gurobi

2. Fixed xfr of results information from pycoek to pyomo

Author: whart222

Diff for: lib/coek/coek/solvers/gurobi/gurobi.cpp

@@ -142,7 +142,7 @@ void GurobiSolver::collect_results(Model& model, std::shared_ptr<SolverResults>&
             results->termination_condition = TerminationCondition::convergence_criteria_satisfied;
             results->solution_status = SolutionStatus::optimal;
             results->objective_value = gmodel->getObjective().getValue();
-            results->objective_bound = results->objective_value;
+            results->objective_bound = gmodel->get(GRB_DoubleAttr_ObjBound);
 
             // Collect values of Gurobi variables
             for (auto& var : model.repn->variables) {
@@ -155,6 +155,8 @@ void GurobiSolver::collect_results(Model& model, std::shared_ptr<SolverResults>&
         else if (status == GRB_SUBOPTIMAL) {
             results->termination_condition = TerminationCondition::other_termination_limit;
             results->solution_status = SolutionStatus::feasible;
+            results->objective_value = gmodel->getObjective().getValue();
+            results->objective_bound = gmodel->get(GRB_DoubleAttr_ObjBound);
             results->error_message
                 = "Unable to satisfy optimality tolerances; a sub-optimal solution is available";
 

Diff for: lib/coek/test/test_examples.cpp

@@ -303,6 +303,8 @@ TEST_CASE("gurobi_examples", "[smoke]")
             auto res = solver.solve(m);
             REQUIRE(coek::check_optimal_termination(res));
 
+            REQUIRE(res->objective_value == Approx(28750.0));
+            REQUIRE(res->objective_bound == Approx(28750.0));
             check(m.get_variables(), simplelp1_soln);
         }
         // SECTION("simplelp1_solve") { simplelp1_solve(); }

Diff for: lib/pyomo_coek/pyomo_coek/components_only/solvers.py

@@ -19,11 +19,42 @@
 from pyomo_coek.components_only.constraint import Constraint
 from pyomo_coek.components_only.variable import Var
 
+from pyomo.opt.results.solution import SolutionStatus
+from pyomo.opt.results.solver import TerminationCondition 
 
 _nlp_solvers = {
     "ipopt",
 }
 
+poek2pyomo_termination_condition =  \
+{
+"convergence_criteria_satisfied": TerminationCondition.optimal,
+"time_limit": TerminationCondition.maxTimeLimit,
+"iteration_limit": TerminationCondition.maxIterations,
+"objective_limit": TerminationCondition.minFunctionValue,
+"min_step_length": TerminationCondition.minStepLength,
+"other_termination_limit": TerminationCondition.unknown,
+"unbounded": TerminationCondition.unbounded,
+"proven_infeasible": TerminationCondition.infeasible,
+"locally_infeasible": TerminationCondition.infeasible,
+"infeasible_or_unbounded": TerminationCondition.infeasibleOrUnbounded,
+"error": TerminationCondition.error,
+"interrupted": TerminationCondition.resourceInterrupt,
+"licensing_problems": TerminationCondition.licensingProblems,
+"solver_not_available":  TerminationCondition.unknown,
+"empty_model":  TerminationCondition.unknown,
+"invalid_model_for_solver": TerminationCondition.unknown,
+"unknown": TerminationCondition.unknown
+}
+poek2pyomo_solution_status = \
+{
+"unknown": SolutionStatus.unknown,              # 
+"no_solution": SolutionStatus.other,            # SolutionStatus.noSolution
+"infeasible": SolutionStatus.infeasible,        # SolutionStatus.infeasible
+"feasible": SolutionStatus.feasible,            # SolutionStatus.feasible
+"optimal": SolutionStatus.optimal               # SolutionStatus.optimal
+}
+
 
 class MySolutionLoader(SolutionLoader):
     def get_primals(self, vars_to_load=None):
@@ -153,12 +184,14 @@ def solve(self, model: _BlockData, timer: HierarchicalTimer = None) -> Results:
         # WARNING - This information needs to be pulled from the coek solver
         res = Results()
         res.solution_loader = self.solution_loader
-        res.termination_condition = TerminationCondition.optimal
-        if self._poek_model:
+        res.termination_condition = poek2pyomo_termination_condition[_res.termination_condition]
+        res.solution_status = poek2pyomo_solution_status[_res.solution_status]
+        if pk.check_optimal_termination(_res):
             res.best_feasible_objective = self._poek_model.get_objective().value
-            res.best_objective_bound = self._poek_model.get_objective().value
-        elif self._poek_nlp_model:
-            res.best_feasible_objective = self._poek_nlp_model.get_objective().value
+            if self._poek_model:
+                res.best_objective_bound = _res.objective_bound
+            elif self._poek_nlp_model:
+                pass
         return res
 
     # WEH - This is a hack