Skip other unnecessary HiGHS tests

Author: tuliotoffolo

examples/gen_cuts_mip.py

@@ -1,23 +1,21 @@
 """This example reads a MIP (in .lp or .mps), solves its linear programming
 relaxation and then tests the impact of adding different types of cutting
-planes. In the end, the it informs which cut generator produced the best bound
-improvement."""
+planes. In the end, it informs which cut generator produced the best bound
+improvement.
+"""
 
 from textwrap import shorten
-import sys
 from mip import Model, CutType, OptimizationStatus
 import mip
 
-lp_path = ""
-
 #  using test data
 lp_path = mip.__file__.replace("mip/__init__.py", "test/data/1443_0-9.lp").replace(
     "mip\\__init__.py", "test\\data\\1443_0-9.lp"
 )
 
 m = Model()
-if m.solver_name.upper() in ["GRB", "GUROBI"]:
-    print("This feature is currently not supported in Gurobi.")
+if m.solver_name.upper() != mip.CBC:
+    print("This feature is currently supported only in CBC.")
 else:
     m.read(lp_path)
 
@@ -55,8 +53,8 @@
                 best_cut = ct
 
             print(
-                "Linear programming relaxation bound now: %g, improvement of %.2f"
-                % (m2.objective_value, perc_impr)
+                f"Linear programming relaxation bound now: "
+                f"{m2.objective_value:.2f}, improvement of {perc_impr:.2f}"
             )
         else:
             continue

mip/highs.py

@@ -448,6 +448,12 @@ def optimize(
 
         check(self._lib.Highs_run(self._model))
 
+        # check whether unsupported callbacks were set
+        if self.model.lazy_constrs_generator:
+            raise NotImplementedError("HiGHS doesn't support lazy constraints at the moment")
+        if self.model.cuts_generator:
+            raise NotImplementedError("HiGHS doesn't support cuts generator at the moment")
+
         # store solution values for later access
         opt_status = self.get_status()
         if opt_status in (

test/numpy_test.py

@@ -1,14 +1,16 @@
+import time
+
 import numpy as np
+
 from mip import Model, OptimizationStatus
 from mip.ndarray import LinExprTensor
-import time
-
 from util import skip_on
 
 
+@skip_on(NotImplementedError)
 def test_numpy():
     model = Model()
-    N = 1000
+    N = 100
 
     start = time.time()
     x = model.add_var_tensor(shape=(N, N), name="x")