Improve cuPDLPx solver integration

- Add warning when explicit_coordinate_names is set (unsupported)
- Add warning when log_fn is provided (unsupported)
- Fix typo in comment (cuDPLPx -> cuPDLPx)
- Use pytest.skip() for clearer MIP test reporting
- Guard feasible_mip_solvers.remove() when cupdlpx unavailable

Author: FabianHofmann

linopy/io.py

@@ -8,6 +8,7 @@
 import logging
 import shutil
 import time
+import warnings
 from collections.abc import Callable
 from io import BufferedWriter
 from pathlib import Path
@@ -764,23 +765,33 @@ def to_cupdlpx(m: Model, explicit_coordinate_names: bool = False) -> cupdlpxMode
     This function does not write the model to intermediate files but directly
     passes it to cupdlpx.
 
-    cuPDLPx does not support named variables and constraints, so names
-    are not tracked by this function.
+    cuPDLPx does not support named variables and constraints, so the
+    `explicit_coordinate_names` parameter is ignored.
 
     Parameters
     ----------
     m : linopy.Model
+    explicit_coordinate_names : bool, optional
+        Ignored. cuPDLPx does not support named variables/constraints.
 
     Returns
     -------
     model : cupdlpx.Model
     """
     import cupdlpx
 
+    if explicit_coordinate_names:
+        warnings.warn(
+            "cuPDLPx does not support named variables/constraints. "
+            "The explicit_coordinate_names parameter is ignored.",
+            UserWarning,
+            stacklevel=2,
+        )
+
     # build model using canonical form matrices and vectors
     # see https://github.com/MIT-Lu-Lab/cuPDLPx/tree/main/python#modeling
     M = m.matrices
-    A = M.A.tocsr()  # cuDPLPx only support CSR sparse matrix format
+    A = M.A.tocsr()  # cuPDLPx only supports CSR sparse matrix format
     # linopy stores constraints as Ax ?= b and keeps track of inequality
     # sense in M.sense. Convert to separate lower and upper bound vectors.
     l = np.where(

linopy/solvers.py

@@ -2487,6 +2487,9 @@ def _solve(
         if basis_fn is not None:
             logger.warning("Basis files are not supported by cuPDLPx. Ignoring.")
 
+        if log_fn is not None:
+            logger.warning("Log files are not supported by cuPDLPx. Ignoring.")
+
         # solve
         cu_model.optimize()
 

test/test_optimization.py

@@ -53,7 +53,8 @@
     feasible_quadratic_solvers.remove("scip")
 
 feasible_mip_solvers: list[str] = available_solvers.copy()
-feasible_mip_solvers.remove("cupdlpx")  # cuPDLPx does not support MIP yet
+if "cupdlpx" in feasible_mip_solvers:
+    feasible_mip_solvers.remove("cupdlpx")  # cuPDLPx does not support MIP yet
 
 gpu_solvers: list[str] = ["cupdlpx"]
 
@@ -630,11 +631,13 @@ def test_infeasible_model(
 def test_model_with_inf(
     model_with_inf: Model, solver: str, io_api: str, explicit_coordinate_names: bool
 ) -> None:
-    if solver in feasible_mip_solvers:
-        status, condition = model_with_inf.solve(solver, io_api=io_api)
-        assert condition == "optimal"
-        assert (model_with_inf.solution.x == 0).all()
-        assert (model_with_inf.solution.y == 10).all()
+    if solver not in feasible_mip_solvers:
+        pytest.skip(f"{solver} does not support MIP")
+
+    status, condition = model_with_inf.solve(solver, io_api=io_api)
+    assert condition == "optimal"
+    assert (model_with_inf.solution.x == 0).all()
+    assert (model_with_inf.solution.y == 10).all()
 
 
 @pytest.mark.parametrize(
@@ -644,14 +647,16 @@ def test_model_with_inf(
 def test_milp_binary_model(
     milp_binary_model: Model, solver: str, io_api: str, explicit_coordinate_names: bool
 ) -> None:
-    if solver in feasible_mip_solvers:
-        status, condition = milp_binary_model.solve(
-            solver, io_api=io_api, explicit_coordinate_names=explicit_coordinate_names
-        )
-        assert condition == "optimal"
-        assert (
-            (milp_binary_model.solution.y == 1) | (milp_binary_model.solution.y == 0)
-        ).all()
+    if solver not in feasible_mip_solvers:
+        pytest.skip(f"{solver} does not support MIP")
+
+    status, condition = milp_binary_model.solve(
+        solver, io_api=io_api, explicit_coordinate_names=explicit_coordinate_names
+    )
+    assert condition == "optimal"
+    assert (
+        (milp_binary_model.solution.y == 1) | (milp_binary_model.solution.y == 0)
+    ).all()
 
 
 @pytest.mark.parametrize(
@@ -664,15 +669,16 @@ def test_milp_binary_model_r(
     io_api: str,
     explicit_coordinate_names: bool,
 ) -> None:
-    if solver in feasible_mip_solvers:
-        status, condition = milp_binary_model_r.solve(
-            solver, io_api=io_api, explicit_coordinate_names=explicit_coordinate_names
-        )
-        assert condition == "optimal"
-        assert (
-            (milp_binary_model_r.solution.x == 1)
-            | (milp_binary_model_r.solution.x == 0)
-        ).all()
+    if solver not in feasible_mip_solvers:
+        pytest.skip(f"{solver} does not support MIP")
+
+    status, condition = milp_binary_model_r.solve(
+        solver, io_api=io_api, explicit_coordinate_names=explicit_coordinate_names
+    )
+    assert condition == "optimal"
+    assert (
+        (milp_binary_model_r.solution.x == 1) | (milp_binary_model_r.solution.x == 0)
+    ).all()
 
 
 @pytest.mark.parametrize(
@@ -682,12 +688,14 @@ def test_milp_binary_model_r(
 def test_milp_model(
     milp_model: Model, solver: str, io_api: str, explicit_coordinate_names: bool
 ) -> None:
-    if solver in feasible_mip_solvers:
-        status, condition = milp_model.solve(
-            solver, io_api=io_api, explicit_coordinate_names=explicit_coordinate_names
-        )
-        assert condition == "optimal"
-        assert ((milp_model.solution.y == 9) | (milp_model.solution.x == 0.5)).all()
+    if solver not in feasible_mip_solvers:
+        pytest.skip(f"{solver} does not support MIP")
+
+    status, condition = milp_model.solve(
+        solver, io_api=io_api, explicit_coordinate_names=explicit_coordinate_names
+    )
+    assert condition == "optimal"
+    assert ((milp_model.solution.y == 9) | (milp_model.solution.x == 0.5)).all()
 
 
 @pytest.mark.parametrize(
@@ -697,19 +705,20 @@ def test_milp_model(
 def test_milp_model_r(
     milp_model_r: Model, solver: str, io_api: str, explicit_coordinate_names: bool
 ) -> None:
-    if solver in feasible_mip_solvers:
-        # MPS format by Highs wrong, see https://github.com/ERGO-Code/HiGHS/issues/1325
-        # skip it
-        if io_api != "mps":
-            status, condition = milp_model_r.solve(
-                solver,
-                io_api=io_api,
-                explicit_coordinate_names=explicit_coordinate_names,
-            )
-            assert condition == "optimal"
-            assert (
-                (milp_model_r.solution.x == 11) | (milp_model_r.solution.y == 0)
-            ).all()
+    if solver not in feasible_mip_solvers:
+        pytest.skip(f"{solver} does not support MIP")
+
+    # MPS format by Highs wrong, see https://github.com/ERGO-Code/HiGHS/issues/1325
+    if io_api == "mps":
+        pytest.skip("MPS format bug in HiGHS")
+
+    status, condition = milp_model_r.solve(
+        solver,
+        io_api=io_api,
+        explicit_coordinate_names=explicit_coordinate_names,
+    )
+    assert condition == "optimal"
+    assert ((milp_model_r.solution.x == 11) | (milp_model_r.solution.y == 0)).all()
 
 
 @pytest.mark.parametrize(
@@ -834,13 +843,15 @@ def test_quadratic_model_unbounded(
 def test_modified_model(
     modified_model: Model, solver: str, io_api: str, explicit_coordinate_names: bool
 ) -> None:
-    if solver in feasible_mip_solvers:
-        status, condition = modified_model.solve(
-            solver, io_api=io_api, explicit_coordinate_names=explicit_coordinate_names
-        )
-        assert condition == "optimal"
-        assert (modified_model.solution.x == 0).all()
-        assert (modified_model.solution.y == 10).all()
+    if solver not in feasible_mip_solvers:
+        pytest.skip(f"{solver} does not support MIP")
+
+    status, condition = modified_model.solve(
+        solver, io_api=io_api, explicit_coordinate_names=explicit_coordinate_names
+    )
+    assert condition == "optimal"
+    assert (modified_model.solution.x == 0).all()
+    assert (modified_model.solution.y == 10).all()
 
 
 @pytest.mark.parametrize("solver,io_api,explicit_coordinate_names", params)