Warn if rep_type of matrix is wrong when building linprog.

mcmtroffaes · Sep 13, 2024 · aedcc9b · aedcc9b
1 parent a515469
commit aedcc9b
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Showing 2 changed files with 8 additions and 2 deletions.
diff --git a/cython/pycddlib.pxi b/cython/pycddlib.pxi
@@ -359,8 +359,12 @@ cdef linprog_from_ptr(dd_LPPtr dd_lp):
 
 
 def linprog_from_matrix(Matrix matrix) -> LinProg:
+    # cddlib does not check if obj_type is valid
     if matrix.obj_type != dd_LPmax and matrix.obj_type != dd_LPmin:
-        raise ValueError(f"invalid value for obj_type: {LPObjType(matrix.obj_type)}")
+        raise ValueError("obj_type must be MIN or MAX")
+    # cddlib assumes H-representation
+    if matrix.rep_type != dd_Inequality:
+        raise ValueError("rep_type must be INEQUALITY")
     cdef dd_ErrorType error = dd_NoError
     # note: dd_Matrix2LP never reports error... so ignore
     cdef dd_LPPtr dd_lp = dd_Matrix2LP(matrix.dd_mat, &error)
@@ -371,7 +375,7 @@ def linprog_from_matrix(Matrix matrix) -> LinProg:
 
 def linprog_from_array(array, dd_LPObjectiveType obj_type):
     if obj_type != dd_LPmax and obj_type != dd_LPmin:
-        raise ValueError(f"invalid value for obj_type: {LPObjType(obj_type)}")
+        raise ValueError("obj_type must be MIN or MAX")
     cdef _Shape shape = _array_shape(array)
     cdef dd_LPPtr dd_lp = dd_CreateLPData(
         obj_type, NUMBER_TYPE, shape.numrows, shape.numcols

diff --git a/test/test_linprog.py b/test/test_linprog.py
@@ -38,6 +38,7 @@ def test_lp2() -> None:
     mat = cdd.matrix_from_array([[4 / 3, -2, -1], [2 / 3, 0, -1], [0, 1, 0], [0, 0, 1]])
     mat.obj_type = cdd.LPObjType.MAX
     mat.obj_func = (0, 3, 4)
+    mat.rep_type = cdd.RepType.INEQUALITY
     lp = cdd.linprog_from_matrix(mat)
     cdd.linprog_solve(lp)
     assert lp.status == cdd.LPStatusType.OPTIMAL
@@ -55,6 +56,7 @@ def test_another() -> None:
     mat = cdd.matrix_from_array(array=array, lin_set=lin_set)
     mat.obj_type = cdd.LPObjType.MIN
     mat.obj_func = obj_func
+    mat.rep_type = cdd.RepType.INEQUALITY
     lp = cdd.linprog_from_matrix(mat)
     assert_matrix_almost_equal(
         lp.array, array + [[-x for x in array[i]] for i in lin_set] + [mat.obj_func]