Consider constraints with empty linexpr correctly (#237)

Closes #213

mip/cbc.py

@@ -1405,20 +1405,23 @@ def var_get_column(self, var: "Var") -> Column:
 
     def add_constr(self, lin_expr: LinExpr, name: str = ""):
         # collecting linear expression data
-        numnz = len(lin_expr.expr)
+
+        # In case of empty linear expression add dummy row
+        # by setting first index of row explicitly with 0
+        numnz = len(lin_expr.expr) or 1
 
         if numnz > self.iidx_space:
             self.iidx_space = max(numnz, self.iidx_space * 2)
             self.iidx = ffi.new("int[%d]" % self.iidx_space)
             self.dvec = ffi.new("double[%d]" % self.iidx_space)
 
         # cind = self.iidx
-        self.iidx = [var.idx for var in lin_expr.expr.keys()]
+        self.iidx = [var.idx for var in lin_expr.expr.keys()] or [0]
 
         # cind = ffi.new("int[]", [var.idx for var in lin_expr.expr.keys()])
         # cval = ffi.new("double[]", [coef for coef in lin_expr.expr.values()])
         # cval = self.dvec
-        self.dvec = [coef for coef in lin_expr.expr.values()]
+        self.dvec = [coef for coef in lin_expr.expr.values()] or [0]
 
         # constraint sense and rhs
         sense = lin_expr.sense.encode("utf-8")

mip/constants.py

@@ -133,6 +133,11 @@ class OptimizationStatus(Enum):
     CUTOFF = 7
     """No feasible solution exists for the current cutoff"""
 
+    INF_OR_UNBD = 8
+    """Special state for gurobi solver. In some cases gurobi could not 
+    determine if the problem is infeasible or unbounded due to application
+    of dual reductions (when active) during presolve."""
+
     OTHER = 10000
 
 

mip/gurobi.py

@@ -775,7 +775,14 @@ def callback(
         if status == 3:  # INFEASIBLE
             return OptimizationStatus.INFEASIBLE
         if status == 4:  # INF_OR_UNBD
-            return OptimizationStatus.UNBOUNDED
+            # Special case by gurobi, where an additional run has to be made
+            # to determine infeasibility or unbounded problem
+            # For this run dual reductions must be disabled
+            # See gurobi support article online - How do I resolve the error "Model is infeasible or unbounded"?
+            # self.set_int_param("DualReductions", 0)
+            # GRBoptimize(self._model)
+            # return OptimizationStatus.INFEASIBLE if self.get_int_attr("Status") == 3 else OptimizationStatus.UNBOUNDED
+            return OptimizationStatus.INF_OR_UNBD
         if status == 5:  # UNBOUNDED
             return OptimizationStatus.UNBOUNDED
         if status == 6:  # CUTOFF

mip/model.py

@@ -331,11 +331,7 @@ def add_constr(
             raise mip.InvalidLinExpr(
                 "A boolean (true/false) cannot be used as a constraint."
             )
-        # TODO: some tests use empty linear constraints, which ideally should not happen
-        # if len(lin_expr) == 0:
-        #     raise mip.InvalidLinExpr(
-        #         "An empty linear expression cannot be used as a constraint."
-        #     )
+
         return self.constrs.add(lin_expr, name, priority)
 
     def add_lazy_constr(self: "Model", expr: "mip.LinExpr"):

test/mip_test.py

@@ -3,7 +3,7 @@
 import pytest
 import networkx as nx
 from mip import Model, xsum, OptimizationStatus, MAXIMIZE, BINARY, INTEGER
-from mip import ConstrsGenerator, CutPool, maximize, CBC, GUROBI, Column
+from mip import ConstrsGenerator, CutPool, maximize, CBC, GUROBI, Column, Constr
 from os import environ
 import math
 
@@ -653,3 +653,37 @@ def test_float(solver: str, val: int):
     assert y.x == float(y)
     # test linear expressions.
     assert float(x + y) == (x + y).x
+
+
+@pytest.mark.parametrize("solver", SOLVERS)
+def test_empty_useless_constraint_is_considered(solver: str):
+    m = Model("empty_constraint", solver_name=solver)
+    x = m.add_var(name="x")
+    y = m.add_var(name="y")
+    m.add_constr(xsum([]) <= 1, name="c_empty")  # useless, empty constraint
+    m.add_constr(x + y <= 5, name="c1")
+    m.add_constr(2 * x + y <= 6, name="c2")
+    m.objective = maximize(x + 2 * y)
+    m.optimize()
+    # check objective
+    assert m.status == OptimizationStatus.OPTIMAL
+    assert abs(m.objective.x - 10) < TOL
+    # check that all names of constraints could be queried
+    assert {c.name for c in m.constrs} == {"c1", "c2", "c_empty"}
+    assert all(isinstance(m.constr_by_name(c_name), Constr) for c_name in ("c1", "c2", "c_empty"))
+
+
+@pytest.mark.parametrize("solver", SOLVERS)
+def test_empty_contradictory_constraint_is_considered(solver: str):
+    m = Model("empty_constraint", solver_name=solver)
+    x = m.add_var(name="x")
+    y = m.add_var(name="y")
+    m.add_constr(xsum([]) <= -1, name="c_contra")  # contradictory empty constraint
+    m.add_constr(x + y <= 5, name="c1")
+    m.objective = maximize(x + 2 * y)
+    m.optimize()
+    # assert infeasibility of problem
+    assert m.status in (OptimizationStatus.INF_OR_UNBD, OptimizationStatus.INFEASIBLE)
+    # check that all names of constraints could be queried
+    assert {c.name for c in m.constrs} == {"c1", "c_contra"}
+    assert all(isinstance(m.constr_by_name(c_name), Constr) for c_name in ("c1", "c_contra"))