feat(model): allow {0,1} bounds for binaries in add_variables

doc/release_notes.rst

@@ -4,6 +4,14 @@ Release Notes
 Upcoming Version
 ----------------
 
+**Features**
+
+* ``add_variables(binary=True, ...)`` now accepts ``lower``/``upper`` bounds, as long as they are 0 or 1. Previously binary bounds could only be set via the ``.lower``/``.upper`` setters after creation. (https://github.com/PyPSA/linopy/issues/776)
+
+**Bug fixes**
+
+* LP file export now honors bounds tightened below ``[0, 1]`` on a binary variable via the ``.lower``/``.upper`` setters after creation (e.g. ``upper = 0``). Previously such bounds were written only by ``io_api="direct"`` and dropped by ``io_api="lp"``. (https://github.com/PyPSA/linopy/issues/776)
+
 Version 0.8.0
 -------------
 

linopy/io.py

@@ -33,6 +33,7 @@
     from highspy.highs import Highs
 
     from linopy.model import Model
+    from linopy.variables import Variable
 
 
 logger = logging.getLogger(__name__)
@@ -235,6 +236,17 @@ def objective_to_file(
         objective_write_quadratic_terms(f, quads, print_variable)
 
 
+def _binary_has_nondefault_bounds(var: Variable) -> bool:
+    """
+    Whether a binary variable carries bounds other than the implied (0, 1).
+
+    Scans the raw bound values (a single vectorised pass each), so masked
+    slots are tolerated: a false positive only routes the variable through
+    the bounds loop, where masked labels are dropped before writing.
+    """
+    return bool((var.lower.values != 0).any() or (var.upper.values != 1).any())
+
+
 def bounds_to_file(
     m: Model,
     f: BufferedWriter,
@@ -250,8 +262,10 @@ def bounds_to_file(
         + list(m.variables.integers)
         + list(m.variables.semi_continuous)
         + [
-            n for n in m.variables.binaries if m.variables[n].fixed
-        ]  # fixed binaries need bounds
+            n
+            for n in m.variables.binaries
+            if _binary_has_nondefault_bounds(m.variables[n])
+        ]
     )
     if not len(list(names)):
         return

linopy/model.py

@@ -620,11 +620,11 @@ def add_variables(
         Parameters
         ----------
         lower : float/array_like, optional
-            Lower bound of the variable(s). Ignored if `binary` is True.
-            The default is -inf.
+            Lower bound of the variable(s). For binary variables it
+            defaults to 0 and, if given, must be 0 or 1. The default is -inf.
         upper : TYPE, optional
-            Upper bound of the variable(s). Ignored if `binary` is True.
-            The default is inf.
+            Upper bound of the variable(s). For binary variables it
+            defaults to 1 and, if given, must be 0 or 1. The default is inf.
         coords : list/dict/xarray.Coordinates, optional
             The coords of the variable array. When provided with **named
             dimensions** (a ``Mapping``, ``xarray.Coordinates``, a
@@ -773,10 +773,14 @@ def add_variables(
             )
 
         if binary:
-            if (lower != -inf) or (upper != inf):
-                raise ValueError("Binary variables cannot have lower or upper bounds.")
-            else:
-                lower, upper = 0, 1
+            if np.isscalar(lower) and lower == -inf:
+                lower = 0
+            elif not (np.isin(lower, (0, 1)) | pd.isna(lower)).all():
+                raise ValueError("Binary variable lower bounds must be 0 or 1.")
+            if np.isscalar(upper) and upper == inf:
+                upper = 1
+            elif not (np.isin(upper, (0, 1)) | pd.isna(upper)).all():
+                raise ValueError("Binary variable upper bounds must be 0 or 1.")
 
         if semi_continuous:
             if not np.isscalar(lower) or float(lower) <= 0:  # type: ignore[arg-type]

test/test_io.py

@@ -515,6 +515,64 @@ def test_to_file_lp_mixed_sign_constraints(tmp_path: Path) -> None:
     assert "=" in content
 
 
+def test_to_file_lp_binary_default_bounds_omitted(tmp_path: Path) -> None:
+    """A binary with the implied [0, 1] bounds gets no bounds section."""
+    m = Model()
+    b = m.add_variables(binary=True, coords=[pd.RangeIndex(3, name="t")], name="b")
+    m.add_constraints(b.sum() >= 1, name="c")
+    m.add_objective(b.sum())
+
+    fn = tmp_path / "binary_default.lp"
+    m.to_file(fn)
+    assert "bounds" not in fn.read_text()
+
+
+def test_to_file_lp_binary_tightened_bounds(tmp_path: Path) -> None:
+    """
+    Per-element bounds tighter than [0, 1] on a binary reach the LP file.
+
+    Regression test for https://github.com/PyPSA/linopy/issues/776: the LP
+    export used to emit binaries only in the `binary` section (implied
+    [0, 1]), diverging from the direct API which honored the bounds.
+    """
+    m = Model()
+    x = m.add_variables(binary=True, coords=[pd.RangeIndex(4, name="t")], name="x")
+    x.upper = pd.Series([1, 1, 0, 0], index=pd.RangeIndex(4, name="t"))
+    m.add_constraints(x.sum() >= 2, name="atleast2")
+    m.add_objective(-1 * x.sum())
+
+    fn = tmp_path / "binary_tightened.lp"
+    m.to_file(fn)
+    content = fn.read_text()
+
+    bounds_section = content.split("bounds")[1].split("binary")[0]
+    labels = m.variables["x"].labels.values
+    for label in labels[2:]:
+        assert f"x{label} <= +0.0" in bounds_section
+
+
+@pytest.mark.skipif(not available_solvers, reason="No solver installed")
+def test_lp_and_direct_agree_on_binary_bounds(tmp_path: Path) -> None:
+    """The LP and direct paths see the same feasible set for tightened binaries."""
+    solver = available_solvers[0]
+
+    def build() -> Model:
+        m = Model()
+        x = m.add_variables(binary=True, coords=[pd.RangeIndex(4, name="t")], name="x")
+        x.upper = pd.Series([1, 1, 0, 0], index=pd.RangeIndex(4, name="t"))
+        m.add_constraints(x.sum() >= 2, name="atleast2")
+        m.add_objective(-1 * x.sum())
+        return m
+
+    m_direct = build()
+    m_direct.solve(solver_name=solver, io_api="direct")
+
+    m_lp = build()
+    m_lp.solve(solver_name=solver, io_api="lp")
+
+    assert m_direct.objective.value == m_lp.objective.value == -2
+
+
 def test_to_file_lp_frozen_vs_mutable(tmp_path: Path) -> None:
     """Test that frozen and mutable constraints produce identical LP output."""
     m_frozen = Model()

test/test_variable_assignment.py

@@ -248,6 +248,63 @@ def test_variable_assignment_binary_with_error() -> None:
         m.add_variables(lower=-2, coords=coords, binary=True)
 
 
+def test_variable_assignment_binary_force_on() -> None:
+    """A scalar bound defaults the other end: lower=1 forces the binary on."""
+    forced_on = Model().add_variables(
+        binary=True, lower=1, coords=[pd.RangeIndex(4, name="t")]
+    )
+    assert (forced_on.lower.values == 1).all()
+    assert (forced_on.upper.values == 1).all()
+
+
+@pytest.mark.parametrize(
+    "upper",
+    [
+        pytest.param([1, 1, 0, 0], id="list"),
+        pytest.param(np.array([1.0, 1.0, 0.0, 0.0]), id="ndarray"),
+        pytest.param(pd.Series([1, 1, 0, 0]), id="series"),
+        pytest.param(
+            xr.DataArray([1, np.nan, 0, 1], dims="t", coords={"t": range(4)}),
+            id="dataarray-nan",
+        ),
+    ],
+)
+def test_variable_assignment_binary_array_bounds_ok(upper) -> None:
+    """0/1 bounds accepted, NaN tolerated (for masking), across containers."""
+    Model().add_variables(binary=True, upper=upper, coords=[pd.RangeIndex(4, name="t")])
+
+
+@pytest.mark.parametrize(
+    "upper",
+    [
+        pytest.param([1, 1, 2, 0], id="list"),
+        pytest.param(np.array([0.5, 1.0, 0.0, 1.0]), id="fractional"),
+        pytest.param(pd.Series([2, 1, 0, 1]), id="series"),
+        pytest.param(
+            xr.DataArray([1, np.nan, 2, 0], dims="t", coords={"t": range(4)}),
+            id="dataarray-nan",
+        ),
+    ],
+)
+def test_variable_assignment_binary_array_bounds_error(upper) -> None:
+    """A non-0/1 value is rejected, even when NaN is also present."""
+    with pytest.raises(ValueError, match="must be 0 or 1"):
+        Model().add_variables(
+            binary=True, upper=upper, coords=[pd.RangeIndex(4, name="t")]
+        )
+
+
+@pytest.mark.parametrize("bound", [0, 1, 0.0, 1.0])
+def test_variable_assignment_binary_scalar_bound_ok(bound) -> None:
+    Model().add_variables(binary=True, upper=bound, coords=[pd.RangeIndex(2)])
+
+
+@pytest.mark.parametrize("bound", [0.5, 2, -1])
+def test_variable_assignment_binary_scalar_bound_error(bound) -> None:
+    with pytest.raises(ValueError, match="must be 0 or 1"):
+        Model().add_variables(binary=True, upper=bound, coords=[pd.RangeIndex(2)])
+
+
 def test_variable_assignment_integer() -> None:
     m = Model()