fixes #250

pypfopt/base_optimizer.py

@@ -120,16 +120,17 @@ class BaseConvexOptimizer(BaseOptimizer):
     """
     The BaseConvexOptimizer contains many private variables for use by
     ``cvxpy``. For example, the immutable optimisation variable for weights
-    is stored as self._w. Interacting directly with these variables is highly
-    discouraged.
+    is stored as self._w. Interacting directly with these variables directly
+    is discouraged.
 
     Instance variables:
 
     - ``n_assets`` - int
     - ``tickers`` - str list
     - ``weights`` - np.ndarray
-    - ``solver`` - str
-    - ``solver_options`` - {str: str} dict
+    - ``_opt`` - cp.Problem
+    - ``_solver`` - str
+    - ``_solver_options`` - {str: str} dict
 
     Public methods:
 
@@ -175,6 +176,7 @@ class BaseConvexOptimizer(BaseOptimizer):
         self._upper_bounds = None
         self._map_bounds_to_constraints(weight_bounds)
 
+        self._opt = None
         self._solver = solver
         self._verbose = verbose
         self._solver_options = solver_options if solver_options else {}
@@ -227,19 +229,21 @@ class BaseConvexOptimizer(BaseOptimizer):
         :raises exceptions.OptimizationError: if problem is not solvable by cvxpy
         """
         try:
-            opt = cp.Problem(cp.Minimize(self._objective), self._constraints)
+            self._opt = cp.Problem(cp.Minimize(self._objective), self._constraints)
 
             if self._solver is not None:
-                opt.solve(
+                self._opt.solve(
                     solver=self._solver, verbose=self._verbose, **self._solver_options
                 )
             else:
-                opt.solve(verbose=self._verbose, **self._solver_options)
+                self._opt.solve(verbose=self._verbose, **self._solver_options)
         except (TypeError, cp.DCPError) as e:
             raise exceptions.OptimizationError from e
 
-        if opt.status not in {"optimal", "optimal_inaccurate"}:
-            raise exceptions.OptimizationError("Solver status: {}".format(opt.status))
+        if self._opt.status not in {"optimal", "optimal_inaccurate"}:
+            raise exceptions.OptimizationError(
+                "Solver status: {}".format(self._opt.status)
+            )
         self.weights = self._w.value.round(16) + 0.0  # +0.0 removes signed zero
         return self._make_output_weights()