add objective module and objective class in order to allow maximization problems

README.md

@@ -98,7 +98,7 @@ Finally, we can solve the problem and get the optimal solution:
 
 ```python
 >>> m.solve()
->>> m.objective_value
+>>> m.objective.value
 ```
 ```
 17.166

benchmark/scripts/benchmark_jump.jl

@@ -14,7 +14,7 @@ function basic_model(n, solver)
     @constraint(m, x + y .>= 0)
     @objective(m, Min, 2 * sum(x) + sum(y))
     optimize!(m)
-    return objective_value(m)
+    return objective.value(m)
 end
 
 function knapsack_model(n, solver)
@@ -25,7 +25,7 @@ function knapsack_model(n, solver)
     @constraint(m, weight' * x <= 200)
     @objective(m, Max, value' * x)
     optimize!(m)
-    return objective_value(m)
+    return objective.value(m)
 end
 
 

benchmark/scripts/benchmark_linopy.py

@@ -27,7 +27,7 @@ def basic_model(n, solver):
     m.add_objective(2 * x.sum() + y.sum())
     # m.to_file(f"linopy-model.lp")
     m.solve(solver)
-    return m.objective_value
+    return m.objective.value
 
 
 def knapsack_model(n, solver):
@@ -38,7 +38,7 @@ def knapsack_model(n, solver):
     m.add_constraints((weight * packages).sum() <= 200)
     m.add_objective(-(value * packages).sum())  # use minus because of minimization
     m.solve(solver_name=solver)
-    return -m.objective_value
+    return -m.objective.value
 
 
 if __name__ == "__main__":

doc/release_notes.rst

@@ -1,8 +1,11 @@
 Release Notes
 =============
 
-.. Upcoming Release
-.. ----------------
+Upcoming Release
+----------------
+
+* It is now possible to set the sense of the objective function to `minimize` or `maximize`. Therefore, a new class `Objective` was introduced which is used in `Model.objective`. It supports the same arithmetic operations as `LinearExpression` and `QuadraticExpression` and contains a `sense` attribute which can be set to `minimize` or `maximize`.
+
 
 Version 0.2.5
 -------------

linopy/__init__.py

@@ -16,5 +16,6 @@
 from linopy.expressions import LinearExpression, QuadraticExpression, merge
 from linopy.io import read_netcdf
 from linopy.model import Model, Variable, available_solvers
+from linopy.objective import Objective
 from linopy.remote import RemoteHandler
 from linopy.version import version as __version__

linopy/expressions.py

@@ -1102,6 +1102,9 @@ class QuadraticExpression(LinearExpression):
     def __init__(self, data, model):
         super().__init__(data, model)
 
+        if data is None:
+            da = xr.DataArray([[], []], dims=[FACTOR_DIM, TERM_DIM])
+            data = Dataset({"coeffs": da, "vars": da, "const": 0})
         if FACTOR_DIM not in data.vars.dims:
             raise ValueError(f"Data does not include dimension {FACTOR_DIM}")
         elif data.sizes[FACTOR_DIM] != 2:

linopy/io.py

@@ -93,7 +93,8 @@ def objective_to_file(m, f, log=False, batch_size=10000):
     if log:
         logger.info("Writing objective.")
 
-    f.write("min\n\nobj:\n\n")
+    sense = m.objective.sense
+    f.write(f"{sense}\n\nobj:\n\n")
     df = m.objective.flat
 
     if np.isnan(df.coeffs).any():
@@ -354,6 +355,9 @@ def to_gurobipy(m, env=None):
     else:
         model.setObjective(M.c @ x)
 
+    if m.objective.sense == "max":
+        model.ModelSense = -1
+
     if len(m.constraints):
         names = "c" + M.clabels.astype(str).astype(object)
         c = model.addMConstr(M.A, x, M.sense, M.b)
@@ -421,6 +425,10 @@ def to_highspy(m):
         num_vars = Q.shape[0]
         h.passHessian(num_vars, Q.nnz, 1, Q.indptr, Q.indices, Q.data)
 
+    # change objective sense
+    if m.objective.sense == "max":
+        h.changeObjectiveSense(highspy.highs_bindings.ObjSense.kMaximize)
+
     return h
 
 

linopy/matrices.py

@@ -110,7 +110,7 @@ def c(self):
         "Vector of objective coefficients of all non-missing variables."
         m = self._parent
         ds = m.objective.flat
-        if isinstance(m.objective, expressions.QuadraticExpression):
+        if isinstance(m.objective.expression, expressions.QuadraticExpression):
             ds = ds[(ds.vars1 == -1) | (ds.vars2 == -1)]
             ds["vars"] = ds.vars1.where(ds.vars1 != -1, ds.vars2)
 
@@ -125,4 +125,4 @@ def Q(self):
         if m.is_linear:
             return None
 
-        return m.objective.to_matrix()[self.vlabels][:, self.vlabels]
+        return m.objective.expression.to_matrix()[self.vlabels][:, self.vlabels]

linopy/model.py

@@ -8,7 +8,6 @@
 import logging
 import os
 import re
-import warnings
 from pathlib import Path
 from tempfile import NamedTemporaryFile, gettempdir
 
@@ -20,13 +19,7 @@
 from xarray import DataArray, Dataset
 
 from linopy import solvers
-from linopy.common import (
-    as_dataarray,
-    best_int,
-    maybe_replace_signs,
-    replace_by_map,
-    save_join,
-)
+from linopy.common import as_dataarray, best_int, maybe_replace_signs, replace_by_map
 from linopy.constants import TERM_DIM, ModelStatus, TerminationCondition
 from linopy.constraints import AnonymousScalarConstraint, Constraint, Constraints
 from linopy.expressions import (
@@ -36,6 +29,7 @@
 )
 from linopy.io import to_block_files, to_file, to_gurobipy, to_highspy, to_netcdf
 from linopy.matrices import MatrixAccessor
+from linopy.objective import Objective
 from linopy.solvers import available_solvers, quadratic_solvers
 from linopy.variables import ScalarVariable, Variable, Variables
 
@@ -75,7 +69,6 @@
         "_varnameCounter",
         "_connameCounter",
         "_blocks",
-        "_objective_value",
         # TODO: check if these should not be mutable
         "_chunk",
         "_force_dim_names",
@@ -110,11 +103,9 @@
         """
         self._variables = Variables(model=self)
         self._constraints = Constraints(model=self)
-        self._objective = LinearExpression(None, self)
+        self._objective = Objective(LinearExpression(None, self), self)
         self._parameters = Dataset()
 
-        self._objective_value = nan
-
         self._status = "initialized"
         self._termination_condition = ""
         self._xCounter = 0
@@ -151,8 +142,23 @@
         return self._objective
 
     @objective.setter
-    def objective(self, value) -> LinearExpression:
-        self.add_objective(value, overwrite=True)
+    def objective(self, obj) -> Objective:
+        if not isinstance(obj, Objective):
+            obj = Objective(obj, self)
+
+        self._objective = obj
+        return self._objective
+
+    @property
+    def sense(self):
+        """
+        Sense of the objective function.
+        """
+        return self.objective.sense
+
+    @sense.setter
+    def sense(self, value):
+        self.objective.sense = value
 
     @property
     def parameters(self):
@@ -205,15 +211,16 @@
         self._termination_condition = TerminationCondition[value].value
 
     @property
+    @deprecated("0.2.7", "Use `objective.value` instead.")
     def objective_value(self):
         """
         Objective value of the model.
         """
-        return self._objective_value
+        return self._objective.value
 
     @objective_value.setter
     def objective_value(self, value):
-        self._objective_value = float(value)
+        self._objective.value = value
 
     @property
     def chunk(self):
@@ -276,7 +283,6 @@
     @property
     def scalar_attrs(self):
         return [
-            "objective_value",
             "status",
             "_xCounter",
             "_cCounter",
@@ -586,14 +592,14 @@
         self.constraints.add(constraint)
         return constraint
 
-    def add_objective(self, expr, overwrite=False):
+    def add_objective(self, expr, overwrite=False, sense="min"):
         """
-        Add a linear objective function to the model.
+        Add an objective function to the model.
 
         Parameters
         ----------
-        expr : linopy.LinearExpression
-            Linear Expressions describing the objective function.
+        expr : linopy.LinearExpression, linopy.QuadraticExpression
+            Expression describing the objective function.
         overwrite : False, optional
             Whether to overwrite the existing objective. The default is False.
 
@@ -603,31 +609,12 @@
             The objective function assigned to the model.
         """
         if not overwrite:
-            assert self.objective.empty(), (
+            assert self.objective.expression.empty(), (
                 "Objective already defined."
                 " Set `overwrite` to True to force overwriting."
             )
-
-        if isinstance(expr, (list, tuple)):
-            expr = self.linexpr(*expr)
-
-        if not isinstance(expr, (LinearExpression, QuadraticExpression)):
-            raise ValueError(
-                f"Invalid type of `expr` ({type(expr)})."
-                " Must be a LinearExpression or QuadraticExpression."
-            )
-
-        if self.chunk is not None:
-            expr = expr.chunk(self.chunk)
-
-        if len(expr.coord_dims):
-            expr = expr.sum()
-
-        if expr.const != 0:
-            raise ValueError("Constant values in objective function not supported.")
-
-        self._objective = expr
-        return self._objective
+        self.objective.expression = expr
+        self.objective.sense = sense
 
     def remove_variables(self, name):
         """
@@ -697,11 +684,11 @@
 
     @property
     def is_linear(self):
-        return type(self.objective) is LinearExpression
+        return self.objective.is_linear
 
     @property
     def is_quadratic(self):
-        return type(self.objective) is QuadraticExpression
+        return self.objective.is_quadratic
 
     @property
     def type(self):
@@ -995,7 +982,7 @@
                 **solver_options,
             )
 
-            self.objective_value = solved.objective_value
+            self.objective.value = solved.objective.value
             self.status = solved.status
             self.termination_condition = solved.termination_condition
             for k, v in self.variables.items():
@@ -1057,7 +1044,7 @@
 
         result.info()
 
-        self.objective_value = result.solution.objective
+        self.objective._value = result.solution.objective
         self.status = result.status.status.value
         self.termination_condition = result.status.termination_condition.value
         self.solver_model = result.solver_model