Removed scipy dependency from three examples.

.travis.yml

@@ -25,11 +25,10 @@ script:
   # Make sure the library installs.
   - python setup.py install
   - python -c "import ipopt"
-  # Make sure the test script runs. SciPy is required for this script.
-  - conda install scipy
   - python test/examplehs071.py
   - python test/exception_handling.py
   - python test/lasso.py
+  - conda install scipy
   - python test/rosen.py
   # Make sure the docs build.
   - cd doc && make html && cd ..

test/examplehs071.py

@@ -22,7 +22,6 @@
 from __future__ import print_function, unicode_literals
 
 import numpy as np
-import scipy.sparse as sps
 import ipopt
 
 
@@ -67,10 +66,8 @@ def hessianstructure(self):
         # this function is redundant. I include it as an example for structure
         # callback.
         #
-        global hs
 
-        hs = sps.coo_matrix(np.tril(np.ones((4, 4))))
-        return (hs.col, hs.row)
+        return np.nonzero(np.tril(np.ones((4, 4))))
 
     def hessian(self, x, lagrange, obj_factor):
         #
@@ -90,11 +87,9 @@ def hessian(self, x, lagrange, obj_factor):
 
         H += lagrange[1]*2*np.eye(4)
 
-        #
-        # Note:
-        #
-        #
-        return H[hs.row, hs.col]
+        row, col = self.hessianstructure()
+
+        return H[row, col]
 
     def intermediate(
             self,

test/exception_handling.py

@@ -20,7 +20,6 @@
 # Based on matlab code by Peter Carbonetto.
 
 import numpy as np
-import scipy.sparse as sps
 import ipopt
 
 
@@ -71,10 +70,8 @@ def hessianstructure(self):
         # this function is redundant. I include it as an example for structure
         # callback.
         #
-        global hs
 
-        hs = sps.coo_matrix(np.tril(np.ones((4, 4))))
-        return (hs.col, hs.row)
+        return np.nonzero(np.tril(np.ones((4, 4))))
 
     def hessian(self, x, lagrange, obj_factor):
         #
@@ -94,11 +91,9 @@ def hessian(self, x, lagrange, obj_factor):
 
         H += lagrange[1]*2*np.eye(4)
 
-        #
-        # Note:
-        #
-        #
-        return H[hs.row, hs.col]
+        row, col = self.hessianstructure()
+
+        return H[row, col]
 
     def intermediate(
             self,

test/lasso.py

@@ -96,35 +96,36 @@ def jacobianstructure(self):
         #
         # Create a sparse matrix to hold the jacobian structure
         #
-        self.js = sps.coo_matrix(np.tile(np.eye(self._m), (2, 2)))
-
-        return (self.js.row, self.js.col)
+        return np.nonzero(np.tile(np.eye(self._m), (2, 2)))
 
     def jacobian(self, x):
 
         I = np.eye(self._m)
 
         J = np.vstack((np.hstack((I, I)), np.hstack((-I, I))))
-        return J[self.js.row, self.js.col]
+
+        row, col = self.jacobianstructure()
+
+        return J[row, col]
 
     def hessianstructure(self):
 
         h = np.zeros((2*self._m, 2*self._m))
-        h[:self._m,:self._m] = np.tril(np.ones((self._m, self._m)))
+        h[:self._m, :self._m] = np.tril(np.ones((self._m, self._m)))
 
         #
         # Create a sparse matrix to hold the hessian structure
         #
-        self.hs = sps.coo_matrix(h)
-
-        return (self.hs.row, self.hs.col)
+        return np.nonzero(h)
 
     def hessian(self, x, lagrange, obj_factor):
 
         H = np.zeros((2*self._m, 2*self._m))
-        H[:self._m,:self._m] = np.tril(np.tril(np.dot(self._A.T, self._A)))
+        H[:self._m, :self._m] = np.tril(np.tril(np.dot(self._A.T, self._A)))
+
+        row, col = self.hessianstructure()
 
-        return obj_factor*H[self.hs.row, self.hs.col]
+        return obj_factor*H[row, col]
 
 
 if __name__ == '__main__':