Add Xpress 9.8+ API support with backward compatibility (#542)

Author: brunoficoxpress

doc/index.rst

@@ -44,7 +44,7 @@ flexible data-handling features:
    - `HiGHS <https://www.maths.ed.ac.uk/hall/HiGHS/>`__
    - `MindOpt <https://solver.damo.alibaba.com/doc/en/html/index.html>`__
    - `Gurobi <https://www.gurobi.com/>`__
-   - `Xpress <https://www.fico.com/en/products/fico-xpress-solver>`__
+   - `Xpress <https://www.fico.com/en/fico-xpress-trial-and-licensing-options>`__
    - `Cplex <https://www.ibm.com/de-de/analytics/cplex-optimizer>`__
    - `MOSEK <https://www.mosek.com/>`__
    - `COPT <https://www.shanshu.ai/copt>`__

doc/prerequisites.rst

@@ -34,7 +34,7 @@ Linopy won't work without a solver. Currently, the following solvers are support
 -  `GLPK <https://www.gnu.org/software/glpk/>`__ - open source, free, not very fast
 -  `HiGHS <https://www.maths.ed.ac.uk/hall/HiGHS/>`__ - open source, free, fast
 -  `Gurobi <https://www.gurobi.com/>`__  - closed source, commercial, very fast
--  `Xpress <https://www.fico.com/en/products/fico-xpress-solver>`__ - closed source, commercial, very fast
+-  `Xpress <https://www.fico.com/en/fico-xpress-trial-and-licensing-options>`__ - closed source, commercial, very fast
 -  `Cplex <https://www.ibm.com/de-de/analytics/cplex-optimizer>`__ - closed source, commercial, very fast
 -  `MOSEK <https://www.mosek.com/>`__
 -  `MindOpt <https://solver.damo.alibaba.com/doc/en/html/index.html>`__ -

linopy/constraints.py

@@ -1015,7 +1015,14 @@ def print_labels(
             if display_max_terms is not None:
                 opts.set_value(display_max_terms=display_max_terms)
             res = [print_single_constraint(self.model, v) for v in values]
-        print("\n".join(res))
+
+        output = "\n".join(res)
+        try:
+            print(output)
+        except UnicodeEncodeError:
+            # Replace Unicode math symbols with ASCII equivalents for Windows console
+            output = output.replace("≤", "<=").replace("≥", ">=").replace("≠", "!=")
+            print(output)
 
     def set_blocks(self, block_map: np.ndarray) -> None:
         """

linopy/model.py

@@ -1458,7 +1458,10 @@ def _compute_infeasibilities_gurobi(self, solver_model: Any) -> list[int]:
     def _compute_infeasibilities_xpress(self, solver_model: Any) -> list[int]:
         """Compute infeasibilities for Xpress solver."""
         # Compute all IIS
-        solver_model.iisall()
+        try:  # Try new API first
+            solver_model.IISAll()
+        except AttributeError:  # Fallback to old API
+            solver_model.iisall()
 
         # Get the number of IIS found
         num_iis = solver_model.attributes.numiis
@@ -1502,28 +1505,55 @@ def _extract_iis_constraints(self, solver_model: Any, iis_num: int) -> list[Any]
         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,
-        )
+        # Declare variables before try/except to avoid mypy redefinition errors
+        miisrow: list[Any]
+        miiscol: list[Any]
+        constrainttype: list[str]
+        colbndtype: list[str]
+        duals: list[float]
+        rdcs: list[float]
+        isolationrows: list[str]
+        isolationcols: list[str]
+
+        try:  # Try new API first
+            (
+                miisrow,
+                miiscol,
+                constrainttype,
+                colbndtype,
+                duals,
+                rdcs,
+                isolationrows,
+                isolationcols,
+            ) = solver_model.getIISData(iis_num)
+
+            # Transform list of indices to list of constraint objects
+            for i in range(len(miisrow)):
+                miisrow[i] = solver_model.getConstraint(miisrow[i])
+
+        except AttributeError:  # Fallback to old API
+            # Prepare lists to receive IIS data
+            miisrow = []  # xpress.constraint objects in the IIS
+            miiscol = []  # xpress.variable objects in the IIS
+            constrainttype = []  # Constraint types ('L', 'G', 'E')
+            colbndtype = []  # Column bound types
+            duals = []  # Dual values
+            rdcs = []  # Reduced costs
+            isolationrows = []  # Row isolation info
+            isolationcols = []  # Column isolation info
+
+            # Get IIS data from Xpress
+            solver_model.getiisdata(
+                iis_num,
+                miisrow,
+                miiscol,
+                constrainttype,
+                colbndtype,
+                duals,
+                rdcs,
+                isolationrows,
+                isolationcols,
+            )
 
         return miisrow
 

linopy/solvers.py

@@ -1559,63 +1559,90 @@ def solve_problem_from_file(
         Result
         """
         CONDITION_MAP = {
-            "lp_optimal": "optimal",
-            "mip_optimal": "optimal",
-            "lp_infeasible": "infeasible",
-            "lp_infeas": "infeasible",
-            "mip_infeasible": "infeasible",
-            "lp_unbounded": "unbounded",
-            "mip_unbounded": "unbounded",
+            xpress.SolStatus.NOTFOUND: "unknown",
+            xpress.SolStatus.OPTIMAL: "optimal",
+            xpress.SolStatus.FEASIBLE: "terminated_by_limit",
+            xpress.SolStatus.INFEASIBLE: "infeasible",
+            xpress.SolStatus.UNBOUNDED: "unbounded",
         }
 
         io_api = read_io_api_from_problem_file(problem_fn)
         sense = read_sense_from_problem_file(problem_fn)
 
         m = xpress.problem()
 
-        m.read(path_to_string(problem_fn))
-        m.setControl(self.solver_options)
+        try:  # Try new API first
+            m.readProb(path_to_string(problem_fn))
+        except AttributeError:  # Fallback to old API
+            m.read(path_to_string(problem_fn))
+
+        # Set solver options - new API uses setControl per option, old API accepts dict
+        if self.solver_options is not None:
+            m.setControl(self.solver_options)
 
         if log_fn is not None:
-            m.setlogfile(path_to_string(log_fn))
+            try:  # Try new API first
+                m.setLogFile(path_to_string(log_fn))
+            except AttributeError:  # Fallback to old API
+                m.setlogfile(path_to_string(log_fn))
 
         if warmstart_fn is not None:
-            m.readbasis(path_to_string(warmstart_fn))
+            try:  # Try new API first
+                m.readBasis(path_to_string(warmstart_fn))
+            except AttributeError:  # Fallback to old API
+                m.readbasis(path_to_string(warmstart_fn))
 
-        m.solve()
+        m.optimize()
 
         # if the solver is stopped (timelimit for example), postsolve the problem
-        if m.getAttrib("solvestatus") == xpress.solvestatus_stopped:
-            m.postsolve()
+        if m.attributes.solvestatus == xpress.enums.SolveStatus.STOPPED:
+            try:  # Try new API first
+                m.postSolve()
+            except AttributeError:  # Fallback to old API
+                m.postsolve()
 
         if basis_fn is not None:
             try:
-                m.writebasis(path_to_string(basis_fn))
-            except Exception as err:
+                try:  # Try new API first
+                    m.writeBasis(path_to_string(basis_fn))
+                except AttributeError:  # Fallback to old API
+                    m.writebasis(path_to_string(basis_fn))
+            except (xpress.SolverError, xpress.ModelError) as err:
                 logger.info("No model basis stored. Raised error: %s", err)
 
         if solution_fn is not None:
             try:
-                m.writebinsol(path_to_string(solution_fn))
-            except Exception as err:
+                try:  # Try new API first
+                    m.writeBinSol(path_to_string(solution_fn))
+                except AttributeError:  # Fallback to old API
+                    m.writebinsol(path_to_string(solution_fn))
+            except (xpress.SolverError, xpress.ModelError) as err:
                 logger.info("Unable to save solution file. Raised error: %s", err)
 
-        condition = m.getProbStatusString()
+        condition = m.attributes.solstatus
         termination_condition = CONDITION_MAP.get(condition, condition)
         status = Status.from_termination_condition(termination_condition)
         status.legacy_status = condition
 
         def get_solver_solution() -> Solution:
-            objective = m.getObjVal()
+            objective = m.attributes.objval
 
-            var = m.getnamelist(xpress_Namespaces.COLUMN, 0, m.attributes.cols - 1)
+            try:  # Try new API first
+                var = m.getNameList(xpress_Namespaces.COLUMN, 0, m.attributes.cols - 1)
+            except AttributeError:  # Fallback to old API
+                var = m.getnamelist(xpress_Namespaces.COLUMN, 0, m.attributes.cols - 1)
             sol = pd.Series(m.getSolution(), index=var, dtype=float)
 
             try:
-                _dual = m.getDual()
-                constraints = m.getnamelist(
-                    xpress_Namespaces.ROW, 0, m.attributes.rows - 1
-                )
+                _dual = m.getDuals()
+                try:  # Try new API first
+                    constraints = m.getNameList(
+                        xpress_Namespaces.ROW, 0, m.attributes.rows - 1
+                    )
+                except AttributeError:  # Fallback to old API
+                    constraints = m.getnamelist(
+                        xpress_Namespaces.ROW, 0, m.attributes.rows - 1
+                    )
                 dual = pd.Series(_dual, index=constraints, dtype=float)
             except (xpress.SolverError, xpress.ModelError, SystemError):
                 logger.warning("Dual values of MILP couldn't be parsed")