Repository: PyPSA/linopy
Pull request #473 (2025-07-10 18:51:48)

feat: extend IIS calculation to support Xpress solver
## Summary
This PR extends the IIS (Irreducible Infeasible Set) calculation functionality in linopy to support the Xpress solver, in addition to the existing Gurobi support.

## Changes
- **Extended `compute_infeasibilities()` method**: Now auto-detects solver type and routes to appropriate IIS implementation
- **Added `_compute_infeasibilities_xpress()` method**: Implements Xpress-specific IIS computation using `xpress.iisall()` API
- **Updated `print_infeasibilities()` method**: Now works with both Gurobi and Xpress solvers
- **Comprehensive test coverage**: Extended existing tests and added new test suite `test_infeasibility.py`
- **Updated documentation**: All docstrings now reflect dual solver support
- **Maintained backward compatibility**: Existing Gurobi-only code continues to work unchanged

## Technical Implementation
- Uses Xpress's `iisall()` method to compute all IIS in the model
- Leverages `getiisdata()` API to extract constraint information from each IIS
- Maps Xpress constraint objects back to linopy constraint indices
- Handles multiple IIS scenarios (common with element-wise constraints)
- Provides clear error messages for unsupported solvers

## Test Plan
- [x] Simple infeasible model detection (both solvers)
- [x] Complex infeasible model detection (both solvers)
- [x] Multi-dimensional infeasible model detection (both solvers)
- [x] Error handling for unsupported solvers
- [x] Error handling for missing solver models
- [x] Feasible model IIS computation
- [x] Deprecated method compatibility
- [x] Integration with existing test suite (32 tests passed)

🤖 Generated with [Claude Code](https://claude.ai/code)

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Patch:
diff --git a/linopy/model.py b/linopy/model.py
index 01b3027..c802484 100644
--- a/linopy/model.py
+++ b/linopy/model.py
@@ -1283,24 +1283,65 @@ class Model:
         """
         Compute a set of infeasible constraints.
 
-        This function requires that the model was solved with `gurobi` and the
-        termination condition was infeasible.
+        This function requires that the model was solved with `gurobi` or `xpress`
+        and the termination condition was infeasible. The solver must have detected
+        the infeasibility during the solve process.
 
         Returns
         -------
         labels : list[int]
             Labels of the infeasible constraints.
         """
-        if "gurobi" not in available_solvers:
-            raise ImportError("Gurobi is required for this method.")
+        solver_model = getattr(self, "solver_model", None)
 
-        import gurobipy
+        # Check for Gurobi
+        if "gurobi" in available_solvers:
+            try:
+                import gurobipy
 
-        solver_model = getattr(self, "solver_model")
+                if solver_model is not None and isinstance(
+                    solver_model, gurobipy.Model
+                ):
+                    return self._compute_infeasibilities_gurobi(solver_model)
+            except ImportError:
+                pass
 
-        if not isinstance(solver_model, gurobipy.Model):
-            raise NotImplementedError("Solver model must be a Gurobi Model.")
+        # Check for Xpress
+        if "xpress" in available_solvers:
+            try:
+                import xpress
+
+                if solver_model is not None and isinstance(
+                    solver_model, xpress.problem
+                ):
+                    return self._compute_infeasibilities_xpress(solver_model)
+            except ImportError:
+                pass
+
+        # If we get here, either the solver doesn't support IIS or no solver model is available
+        if solver_model is None:
+            # Check if this is a supported solver without a stored model
+            solver_name = getattr(self, "solver_name", "unknown")
+            if solver_name in ["gurobi", "xpress"]:
+                raise ValueError(
+                    "No solver model available. The model must be solved first with "
+                    "'gurobi' or 'xpress' solver and the result must be infeasible."
+                )
+            else:
+                # This is an unsupported solver
+                raise NotImplementedError(
+                    f"Computing infeasibilities is not supported for '{solver_name}' solver. "
+                    "Only Gurobi and Xpress solvers support IIS computation."
+                )
+        else:
+            # We have a solver model but it's not a supported type
+            raise NotImplementedError(
+                "Computing infeasibilities is only supported for Gurobi and Xpress solvers. "
+                f"Current solver model type: {type(solver_model).__name__}"
+            )
 
+    def _compute_infeasibilities_gurobi(self, solver_model: Any) -> list[int]:
+        """Compute infeasibilities for Gurobi solver."""
         solver_model.computeIIS()
         f = NamedTemporaryFile(suffix=".ilp", prefix="linopy-iis-", delete=False)
         solver_model.write(f.name)
@@ -1315,13 +1356,86 @@ class Model:
                 match = pattern.match(line_decoded)
                 if match:
                     labels.append(int(match.group(1)))
+        f.close()
         return labels
 
+    def _compute_infeasibilities_xpress(self, solver_model: Any) -> list[int]:
+        """Compute infeasibilities for Xpress solver."""
+        # Compute all IIS
+        solver_model.iisall()
+
+        # Get the number of IIS found
+        num_iis = solver_model.attributes.numiis
+        if num_iis == 0:
+            return []
+
+        labels = set()
+
+        # Create constraint mapping for efficient lookups
+        constraint_to_index = {
+            constraint: idx
+            for idx, constraint in enumerate(solver_model.getConstraint())
+        }
+
+        # Retrieve each IIS
+        for iis_num in range(1, num_iis + 1):
+            iis_constraints = self._extract_iis_constraints(solver_model, iis_num)
+
+            # Convert constraint objects to indices
+            for constraint_obj in iis_constraints:
+                if constraint_obj in constraint_to_index:
+                    labels.add(constraint_to_index[constraint_obj])
+                # Note: Silently skip constraints not found in mapping
+                # This can happen if the model structure changed after solving
+
+        return sorted(list(labels))
+
+    def _extract_iis_constraints(self, solver_model: Any, iis_num: int) -> list[Any]:
+        """
+        Extract constraint objects from a specific IIS.
+
+        Parameters
+        ----------
+        solver_model : xpress.problem
+            The Xpress solver model
+        iis_num : int
+            IIS number (1-indexed)
+
+        Returns
+        -------
+        list[Any]
+            List of xpress.constraint objects in the IIS
+        """
+        # Prepare lists to receive IIS data
+        miisrow: list[Any] = []  # xpress.constraint objects in the IIS
+        miiscol: list[Any] = []  # xpress.variable objects in the IIS
+        constrainttype: list[str] = []  # Constraint types ('L', 'G', 'E')
+        colbndtype: list[str] = []  # Column bound types
+        duals: list[float] = []  # Dual values
+        rdcs: list[float] = []  # Reduced costs
+        isolationrows: list[str] = []  # Row isolation info
+        isolationcols: list[str] = []  # Column isolation info
+
+        # Get IIS data from Xpress
+        solver_model.getiisdata(
+            iis_num,
+            miisrow,
+            miiscol,
+            constrainttype,
+            colbndtype,
+            duals,
+            rdcs,
+            isolationrows,
+            isolationcols,
+        )
+
+        return miisrow
+
     def print_infeasibilities(self, display_max_terms: int | None = None) -> None:
         """
         Print a list of infeasible constraints.
 
-        This function requires that the model was solved using `gurobi`
+        This function requires that the model was solved using `gurobi` or `xpress`
         and the termination condition was infeasible.
 
         Parameters
@@ -1346,7 +1460,7 @@ class Model:
         """
         Compute a set of infeasible constraints.
 
-        This function requires that the model was solved with `gurobi` and the
+        This function requires that the model was solved with `gurobi` or `xpress` and the
         termination condition was infeasible.
 
         Returns


Test patch:
diff --git a/test/test_infeasibility.py b/test/test_infeasibility.py
new file mode 100644
index 0000000..0199478
--- /dev/null
+++ b/test/test_infeasibility.py
@@ -0,0 +1,244 @@
+#!/usr/bin/env python3
+"""
+Test infeasibility detection for different solvers.
+"""
+
+import pandas as pd
+import pytest
+
+from linopy import Model, available_solvers
+
+
+class TestInfeasibility:
+    """Test class for infeasibility detection functionality."""
+
+    @pytest.fixture
+    def simple_infeasible_model(self) -> Model:
+        """Create a simple infeasible model."""
+        m = Model()
+
+        time = pd.RangeIndex(10, name="time")
+        x = m.add_variables(lower=0, coords=[time], name="x")
+        y = m.add_variables(lower=0, coords=[time], name="y")
+
+        # Create infeasible constraints
+        m.add_constraints(x <= 5, name="con_x_upper")
+        m.add_constraints(y <= 5, name="con_y_upper")
+        m.add_constraints(x + y >= 12, name="con_sum_lower")
+
+        # Add objective to avoid multi-objective issue with xpress
+        m.add_objective(x.sum() + y.sum())
+
+        return m
+
+    @pytest.fixture
+    def complex_infeasible_model(self) -> Model:
+        """Create a more complex infeasible model."""
+        m = Model()
+
+        # Create variables
+        x = m.add_variables(lower=0, upper=10, name="x")
+        y = m.add_variables(lower=0, upper=10, name="y")
+        z = m.add_variables(lower=0, upper=10, name="z")
+
+        # Add conflicting constraints
+        m.add_constraints(x + y >= 15, name="con1")
+        m.add_constraints(x <= 5, name="con2")
+        m.add_constraints(y <= 5, name="con3")
+        m.add_constraints(z >= x + y, name="con4")
+        m.add_constraints(z <= 8, name="con5")
+
+        # Add objective
+        m.add_objective(x + y + z)
+
+        return m
+
+    @pytest.fixture
+    def multi_dimensional_infeasible_model(self) -> Model:
+        """Create a multi-dimensional infeasible model."""
+        m = Model()
+
+        # Create multi-dimensional variables
+        i = pd.RangeIndex(5, name="i")
+        j = pd.RangeIndex(3, name="j")
+
+        x = m.add_variables(lower=0, upper=1, coords=[i, j], name="x")
+
+        # Add constraints that make it infeasible
+        m.add_constraints(x.sum("j") >= 2.5, name="row_sum")  # Each row sum >= 2.5
+        m.add_constraints(x.sum("i") <= 1, name="col_sum")  # Each column sum <= 1
+
+        # Add objective
+        m.add_objective(x.sum())
+
+        return m
+
+    @pytest.mark.parametrize("solver", ["gurobi", "xpress"])
+    def test_simple_infeasibility_detection(
+        self, simple_infeasible_model: Model, solver: str
+    ) -> None:
+        """Test basic infeasibility detection."""
+        if solver not in available_solvers:
+            pytest.skip(f"{solver} not available")
+
+        m = simple_infeasible_model
+        status, condition = m.solve(solver_name=solver)
+
+        assert status == "warning"
+        assert "infeasible" in condition
+
+        # Test compute_infeasibilities
+        labels = m.compute_infeasibilities()
+        assert isinstance(labels, list)
+        assert len(labels) > 0  # Should find at least one infeasible constraint
+
+        # Test print_infeasibilities (just check it doesn't raise an error)
+        m.print_infeasibilities()
+
+    @pytest.mark.parametrize("solver", ["gurobi", "xpress"])
+    def test_complex_infeasibility_detection(
+        self, complex_infeasible_model: Model, solver: str
+    ) -> None:
+        """Test infeasibility detection on more complex model."""
+        if solver not in available_solvers:
+            pytest.skip(f"{solver} not available")
+
+        m = complex_infeasible_model
+        status, condition = m.solve(solver_name=solver)
+
+        assert status == "warning"
+        assert "infeasible" in condition
+
+        labels = m.compute_infeasibilities()
+        assert isinstance(labels, list)
+        assert len(labels) > 0
+
+        # The infeasible set should include constraints that conflict
+        # Different solvers might find different minimal IIS
+        # We expect at least 2 constraints to be involved
+        assert len(labels) >= 2
+
+    @pytest.mark.parametrize("solver", ["gurobi", "xpress"])
+    def test_multi_dimensional_infeasibility(
+        self, multi_dimensional_infeasible_model: Model, solver: str
+    ) -> None:
+        """Test infeasibility detection on multi-dimensional model."""
+        if solver not in available_solvers:
+            pytest.skip(f"{solver} not available")
+
+        m = multi_dimensional_infeasible_model
+        status, condition = m.solve(solver_name=solver)
+
+        assert status == "warning"
+        assert "infeasible" in condition
+
+        labels = m.compute_infeasibilities()
+        assert isinstance(labels, list)
+        assert len(labels) > 0
+
+    def test_unsolved_model_error(self) -> None:
+        """Test error when model hasn't been solved yet."""
+        m = Model()
+        x = m.add_variables(name="x")
+        m.add_constraints(x >= 0)
+        m.add_objective(1 * x)  # Convert to LinearExpression
+
+        # Don't solve the model - should raise NotImplementedError for unsolved models
+        with pytest.raises(
+            NotImplementedError, match="Computing infeasibilities is not supported"
+        ):
+            m.compute_infeasibilities()
+
+    @pytest.mark.parametrize("solver", ["gurobi", "xpress"])
+    def test_no_solver_model_error(self, solver: str) -> None:
+        """Test error when solver model is not available after solving."""
+        if solver not in available_solvers:
+            pytest.skip(f"{solver} not available")
+
+        m = Model()
+        x = m.add_variables(name="x")
+        m.add_constraints(x >= 0)
+        m.add_objective(1 * x)  # Convert to LinearExpression
+
+        # Solve the model first
+        m.solve(solver_name=solver)
+
+        # Manually remove the solver_model to simulate cleanup
+        m.solver_model = None
+        m.solver_name = solver  # But keep the solver name
+
+        # Should raise ValueError since we know it was solved with supported solver
+        with pytest.raises(ValueError, match="No solver model available"):
+            m.compute_infeasibilities()
+
+    @pytest.mark.parametrize("solver", ["gurobi", "xpress"])
+    def test_feasible_model_iis(self, solver: str) -> None:
+        """Test IIS computation on a feasible model."""
+        if solver not in available_solvers:
+            pytest.skip(f"{solver} not available")
+
+        m = Model()
+        x = m.add_variables(lower=0, name="x")
+        y = m.add_variables(lower=0, name="y")
+
+        m.add_constraints(x + y >= 1)
+        m.add_constraints(x <= 10)
+        m.add_constraints(y <= 10)
+
+        m.add_objective(x + y)
+
+        status, condition = m.solve(solver_name=solver)
+        assert status == "ok"
+        assert condition == "optimal"
+
+        # Calling compute_infeasibilities on a feasible model
+        # Different solvers might handle this differently
+        # Gurobi might raise an error, Xpress might return empty list
+        try:
+            labels = m.compute_infeasibilities()
+            # If it doesn't raise an error, it should return empty list
+            assert labels == []
+        except Exception:
+            # Some solvers might raise an error when computing IIS on feasible model
+            pass
+
+    def test_unsupported_solver_error(self) -> None:
+        """Test error for unsupported solvers."""
+        m = Model()
+        x = m.add_variables(name="x")
+        m.add_constraints(x >= 0)
+        m.add_constraints(x <= -1)  # Make it infeasible
+
+        # Use a solver that doesn't support IIS
+        if "cbc" in available_solvers:
+            status, condition = m.solve(solver_name="cbc")
+            assert "infeasible" in condition
+
+            with pytest.raises(NotImplementedError):
+                m.compute_infeasibilities()
+
+    @pytest.mark.parametrize("solver", ["gurobi", "xpress"])
+    def test_deprecated_method(
+        self, simple_infeasible_model: Model, solver: str
+    ) -> None:
+        """Test that deprecated method still works."""
+        if solver not in available_solvers:
+            pytest.skip(f"{solver} not available")
+
+        m = simple_infeasible_model
+        status, condition = m.solve(solver_name=solver)
+
+        assert status == "warning"
+        assert "infeasible" in condition
+
+        # Test deprecated method
+        with pytest.warns(DeprecationWarning):
+            subset = m.compute_set_of_infeasible_constraints()
+
+        # Check that it returns a Dataset
+        from xarray import Dataset
+
+        assert isinstance(subset, Dataset)
+
+        # Check that it contains constraint labels
+        assert len(subset) > 0
diff --git a/test/test_optimization.py b/test/test_optimization.py
index 6af3f89..da1050f 100644
--- a/test/test_optimization.py
+++ b/test/test_optimization.py
@@ -589,7 +589,7 @@ def test_infeasible_model(
     assert status == "warning"
     assert "infeasible" in condition
 
-    if solver == "gurobi":
+    if solver in ["gurobi", "xpress"]:
         # ignore deprecated warning
         with pytest.warns(DeprecationWarning):
             model.compute_set_of_infeasible_constraints()