Add highs support in MindtPy

.github/workflows/test_pr_and_main.yml

@@ -612,7 +612,8 @@ jobs:
       if: ${{ ! matrix.slim }}
       shell: bash
       run: |
-        $PYTHON_EXE -m pip install --cache-dir cache/pip highspy \
+        echo "NOTE: temporarily pinning to highspy pre-release for testing"
+        $PYTHON_EXE -m pip install --cache-dir cache/pip "highspy>=1.7.1.dev1" \
             || echo "WARNING: highspy is not available"
 
     - name: Set up coverage tracking

pyomo/contrib/appsi/solvers/highs.py

@@ -176,11 +176,19 @@ def available(self):
             return self.Availability.NotFound
 
     def version(self):
-        version = (
-            highspy.HIGHS_VERSION_MAJOR,
-            highspy.HIGHS_VERSION_MINOR,
-            highspy.HIGHS_VERSION_PATCH,
-        )
+        try:
+            version = (
+                highspy.HIGHS_VERSION_MAJOR,
+                highspy.HIGHS_VERSION_MINOR,
+                highspy.HIGHS_VERSION_PATCH,
+            )
+        except AttributeError:
+            # Older versions of Highs do not have the above attributes
+            # and the solver version can only be obtained by making
+            # an instance of the solver class.
+            tmp = highspy.Highs()
+            version = (tmp.versionMajor(), tmp.versionMinor(), tmp.versionPatch())
+
         return version
 
     @property

pyomo/contrib/mindtpy/algorithm_base_class.py

@@ -146,7 +146,9 @@
         # Store the OA cuts generated in the mip_start_process.
         self.mip_start_lazy_oa_cuts = []
         # Whether to load solutions in solve() function
-        self.load_solutions = True
+        self.mip_load_solutions = True
+        self.nlp_load_solutions = True
+        self.regularization_mip_load_solutions = True
 
     # Support use as a context manager under current solver API
     def __enter__(self):
@@ -296,7 +298,7 @@
                 results = self.mip_opt.solve(
                     self.original_model,
                     tee=config.mip_solver_tee,
-                    load_solutions=self.load_solutions,
+                    load_solutions=self.mip_load_solutions,
                     **config.mip_solver_args,
                 )
                 if len(results.solution) > 0:
@@ -838,7 +840,7 @@
             results = self.nlp_opt.solve(
                 self.rnlp,
                 tee=config.nlp_solver_tee,
-                load_solutions=self.load_solutions,
+                load_solutions=self.nlp_load_solutions,
                 **nlp_args,
             )
             if len(results.solution) > 0:
@@ -860,7 +862,7 @@
             results = self.nlp_opt.solve(
                 self.rnlp,
                 tee=config.nlp_solver_tee,
-                load_solutions=self.load_solutions,
+                load_solutions=self.nlp_load_solutions,
                 **nlp_args,
             )
             if len(results.solution) > 0:
@@ -991,7 +993,10 @@
         mip_args = dict(config.mip_solver_args)
         update_solver_timelimit(self.mip_opt, config.mip_solver, self.timing, config)
         results = self.mip_opt.solve(
-            m, tee=config.mip_solver_tee, load_solutions=self.load_solutions, **mip_args
+            m,
+            tee=config.mip_solver_tee,
+            load_solutions=self.mip_load_solutions,
+            **mip_args,
         )
         if len(results.solution) > 0:
             m.solutions.load_from(results)
@@ -1111,7 +1116,7 @@
                 results = self.nlp_opt.solve(
                     self.fixed_nlp,
                     tee=config.nlp_solver_tee,
-                    load_solutions=self.load_solutions,
+                    load_solutions=self.nlp_load_solutions,
                     **nlp_args,
                 )
                 if len(results.solution) > 0:
@@ -1379,12 +1384,20 @@
         update_solver_timelimit(
             self.feasibility_nlp_opt, config.nlp_solver, self.timing, config
         )
-        TransformationFactory('contrib.deactivate_trivial_constraints').apply_to(
-            feas_subproblem,
-            tmp=True,
-            ignore_infeasible=False,
-            tolerance=config.constraint_tolerance,
-        )
+        try:
+            TransformationFactory('contrib.deactivate_trivial_constraints').apply_to(
+                self.fixed_nlp,
+                tmp=True,
+                ignore_infeasible=False,
+                tolerance=config.constraint_tolerance,
+            )
+        except InfeasibleConstraintException as e:
+            config.logger.error(
+                str(e) + '\nInfeasibility detected in deactivate_trivial_constraints.'
+            )
+            results = SolverResults()
+            results.solver.termination_condition = tc.infeasible
+            return self.fixed_nlp, results
         with SuppressInfeasibleWarning():
             try:
                 with time_code(self.timing, 'feasibility subproblem'):
@@ -1570,7 +1583,7 @@
             main_mip_results = self.mip_opt.solve(
                 self.mip,
                 tee=config.mip_solver_tee,
-                load_solutions=self.load_solutions,
+                load_solutions=self.mip_load_solutions,
                 **mip_args,
             )
             if len(main_mip_results.solution) > 0:
@@ -1658,7 +1671,7 @@
             main_mip_results = self.mip_opt.solve(
                 self.mip,
                 tee=config.mip_solver_tee,
-                load_solutions=self.load_solutions,
+                load_solutions=self.mip_load_solutions,
                 **mip_args,
             )
             # update_attributes should be before load_from(main_mip_results), since load_from(main_mip_results) may fail.
@@ -1719,7 +1732,7 @@
         main_mip_results = self.mip_opt.solve(
             self.mip,
             tee=config.mip_solver_tee,
-            load_solutions=self.load_solutions,
+            load_solutions=self.mip_load_solutions,
             **mip_args,
         )
         # update_attributes should be before load_from(main_mip_results), since load_from(main_mip_results) may fail.
@@ -1762,7 +1775,7 @@
         main_mip_results = self.regularization_mip_opt.solve(
             self.mip,
             tee=config.mip_solver_tee,
-            load_solutions=self.load_solutions,
+            load_solutions=self.regularization_mip_load_solutions,
             **dict(config.mip_solver_args),
         )
         if len(main_mip_results.solution) > 0:
@@ -1978,7 +1991,7 @@
             main_mip_results = self.mip_opt.solve(
                 main_mip,
                 tee=config.mip_solver_tee,
-                load_solutions=self.load_solutions,
+                load_solutions=self.mip_load_solutions,
                 **config.mip_solver_args,
             )
             if len(main_mip_results.solution) > 0:
@@ -2261,6 +2274,11 @@
             raise ValueError(self.config.mip_solver + ' is not available.')
         if not self.mip_opt.license_is_valid():
             raise ValueError(self.config.mip_solver + ' is not licensed.')
+        if self.config.mip_solver == "appsi_highs":
+            if self.mip_opt.version() < (1, 7, 0):
+                raise ValueError(
+                    "MindtPy requires the use of HIGHS version 1.7.0 or higher for full compatibility."
+                )
         if not self.nlp_opt.available():
             raise ValueError(self.config.nlp_solver + ' is not available.')
         if not self.nlp_opt.license_is_valid():
@@ -2308,15 +2326,15 @@
                 config.mip_solver = 'cplex_persistent'
 
         # related to https://github.com/Pyomo/pyomo/issues/2363
+        if 'appsi' in config.mip_solver:
+            self.mip_load_solutions = False
+        if 'appsi' in config.nlp_solver:
+            self.nlp_load_solutions = False
         if (
-            'appsi' in config.mip_solver
-            or 'appsi' in config.nlp_solver
-            or (
-                config.mip_regularization_solver is not None
-                and 'appsi' in config.mip_regularization_solver
-            )
+            config.mip_regularization_solver is not None
+            and 'appsi' in config.mip_regularization_solver
         ):
-            self.load_solutions = False
+            self.regularization_mip_load_solutions = False
 
     ################################################################################################################################
     # Feasibility Pump
@@ -2384,7 +2402,7 @@
                 results = self.nlp_opt.solve(
                     fp_nlp,
                     tee=config.nlp_solver_tee,
-                    load_solutions=self.load_solutions,
+                    load_solutions=self.nlp_load_solutions,
                     **nlp_args,
                 )
                 if len(results.solution) > 0:

pyomo/contrib/mindtpy/config_options.py

@@ -549,7 +549,7 @@ def _add_subsolver_configs(CONFIG):
                     'cplex_persistent',
                     'appsi_cplex',
                     'appsi_gurobi',
-                    # 'appsi_highs', TODO: feasibility pump now fails with appsi_highs #2951
+                    'appsi_highs',
                 ]
             ),
             description='MIP subsolver name',
@@ -631,7 +631,7 @@ def _add_subsolver_configs(CONFIG):
                     'cplex_persistent',
                     'appsi_cplex',
                     'appsi_gurobi',
-                    # 'appsi_highs',
+                    'appsi_highs',
                 ]
             ),
             description='MIP subsolver for regularization problem',

pyomo/contrib/mindtpy/tests/test_mindtpy.py

@@ -56,7 +56,12 @@
 QCP_model._generate_model()
 extreme_model_list = [LP_model.model, QCP_model.model]
 
-required_solvers = ('ipopt', 'glpk')
+if SolverFactory('appsi_highs').available(exception_flag=False) and SolverFactory(
+    'appsi_highs'
+).version() >= (1, 7, 0):
+    required_solvers = ('ipopt', 'appsi_highs')
+else:
+    required_solvers = ('ipopt', 'glpk')
 if all(SolverFactory(s).available(exception_flag=False) for s in required_solvers):
     subsolvers_available = True
 else:

pyomo/contrib/mindtpy/tests/test_mindtpy_ECP.py

@@ -23,7 +23,13 @@
 from pyomo.environ import SolverFactory, value
 from pyomo.opt import TerminationCondition
 
-required_solvers = ('ipopt', 'glpk')
+if SolverFactory('appsi_highs').available(exception_flag=False) and SolverFactory(
+    'appsi_highs'
+).version() >= (1, 7, 0):
+    required_solvers = ('ipopt', 'appsi_highs')
+else:
+    required_solvers = ('ipopt', 'glpk')
+
 if all(SolverFactory(s).available(exception_flag=False) for s in required_solvers):
     subsolvers_available = True
 else:

pyomo/contrib/mindtpy/tests/test_mindtpy_feas_pump.py

@@ -28,8 +28,13 @@
 from pyomo.contrib.mindtpy.tests.feasibility_pump1 import FeasPump1
 from pyomo.contrib.mindtpy.tests.feasibility_pump2 import FeasPump2
 
-required_solvers = ('ipopt', 'glpk')
-# TODO: 'appsi_highs' will fail here.
+if SolverFactory('appsi_highs').available(exception_flag=False) and SolverFactory(
+    'appsi_highs'
+).version() >= (1, 7, 0):
+    required_solvers = ('ipopt', 'appsi_highs')
+else:
+    required_solvers = ('ipopt', 'glpk')
+
 if all(SolverFactory(s).available(exception_flag=False) for s in required_solvers):
     subsolvers_available = True
 else:

pyomo/contrib/mindtpy/tests/test_mindtpy_grey_box.py

@@ -18,7 +18,14 @@
 from pyomo.contrib.mindtpy.tests.MINLP_simple import SimpleMINLP as SimpleMINLP
 
 model_list = [SimpleMINLP(grey_box=True)]
-required_solvers = ('cyipopt', 'glpk')
+
+if SolverFactory('appsi_highs').available(exception_flag=False) and SolverFactory(
+    'appsi_highs'
+).version() >= (1, 7, 0):
+    required_solvers = ('cyipopt', 'appsi_highs')
+else:
+    required_solvers = ('cyipopt', 'glpk')
+
 if all(SolverFactory(s).available(exception_flag=False) for s in required_solvers):
     subsolvers_available = True
 else: