Merge remote-tracking branch 'origin/master' into composite_operator_datacontainer

2 files changed, 124 insertions, 111 deletions

diff --git a/Wrappers/Python/ccpi/optimisation/funcs.py b/Wrappers/Python/ccpi/optimisation/funcs.py
index 4f84889..8ce54c7 100755
--- a/Wrappers/Python/ccpi/optimisation/funcs.py
+++ b/Wrappers/Python/ccpi/optimisation/funcs.py
@@ -180,8 +180,13 @@ class Norm2sq(Function):
         #else:
         y = self.A.direct(x)
         y.__isub__(self.b)
-        y.__imul__(y)
-        return y.sum() * self.c
+        #y.__imul__(y)
+        #return y.sum() * self.c
+        try:
+            return y.squared_norm() * self.c
+        except AttributeError as ae:
+            # added for compatibility with SIRF 
+            return (y.norm()**2) * self.c
     
     def gradient(self, x, out = None):
         if self.memopt:
diff --git a/Wrappers/Python/test/test_run_test.py b/Wrappers/Python/test/test_run_test.py
index d0b87f5..3c7d9ab 100755
--- a/Wrappers/Python/test/test_run_test.py
+++ b/Wrappers/Python/test/test_run_test.py
@@ -62,120 +62,128 @@ class TestAlgorithms(unittest.TestCase):
 
     def test_FISTA_cvx(self):
         if not cvx_not_installable:
-            # Problem data.
-            m = 30
-            n = 20
-            np.random.seed(1)
-            Amat = np.random.randn(m, n)
-            A = LinearOperatorMatrix(Amat)
-            bmat = np.random.randn(m)
-            bmat.shape = (bmat.shape[0], 1)
-
-            # A = Identity()
-            # Change n to equal to m.
-
-            b = DataContainer(bmat)
-
-            # Regularization parameter
-            lam = 10
-            opt = {'memopt': True}
-            # Create object instances with the test data A and b.
-            f = Norm2sq(A, b, c=0.5, memopt=True)
-            g0 = ZeroFun()
-
-            # Initial guess
-            x_init = DataContainer(np.zeros((n, 1)))
-
-            f.grad(x_init)
-
-            # Run FISTA for least squares plus zero function.
-            x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0, opt=opt)
-
-            # Print solution and final objective/criterion value for comparison
-            print("FISTA least squares plus zero function solution and objective value:")
-            print(x_fista0.array)
-            print(criter0[-1])
-
-            # Compare to CVXPY
-
-            # Construct the problem.
-            x0 = Variable(n)
-            objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat.T[0]))
-            prob0 = Problem(objective0)
-
-            # The optimal objective is returned by prob.solve().
-            result0 = prob0.solve(verbose=False, solver=SCS, eps=1e-9)
-
-            # The optimal solution for x is stored in x.value and optimal objective value
-            # is in result as well as in objective.value
-            print("CVXPY least squares plus zero function solution and objective value:")
-            print(x0.value)
-            print(objective0.value)
-            self.assertNumpyArrayAlmostEqual(
-                numpy.squeeze(x_fista0.array), x0.value, 6)
+            try:
+                # Problem data.
+                m = 30
+                n = 20
+                np.random.seed(1)
+                Amat = np.random.randn(m, n)
+                A = LinearOperatorMatrix(Amat)
+                bmat = np.random.randn(m)
+                bmat.shape = (bmat.shape[0], 1)
+
+                # A = Identity()
+                # Change n to equal to m.
+
+                b = DataContainer(bmat)
+
+                # Regularization parameter
+                lam = 10
+                opt = {'memopt': True}
+                # Create object instances with the test data A and b.
+                f = Norm2sq(A, b, c=0.5, memopt=True)
+                g0 = ZeroFun()
+
+                # Initial guess
+                x_init = DataContainer(np.zeros((n, 1)))
+
+                f.grad(x_init)
+
+                # Run FISTA for least squares plus zero function.
+                x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0, opt=opt)
+
+                # Print solution and final objective/criterion value for comparison
+                print("FISTA least squares plus zero function solution and objective value:")
+                print(x_fista0.array)
+                print(criter0[-1])
+
+                # Compare to CVXPY
+
+                # Construct the problem.
+                x0 = Variable(n)
+                objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat.T[0]))
+                prob0 = Problem(objective0)
+
+                # The optimal objective is returned by prob.solve().
+                result0 = prob0.solve(verbose=False, solver=SCS, eps=1e-9)
+
+                # The optimal solution for x is stored in x.value and optimal objective value
+                # is in result as well as in objective.value
+                print("CVXPY least squares plus zero function solution and objective value:")
+                print(x0.value)
+                print(objective0.value)
+                self.assertNumpyArrayAlmostEqual(
+                    numpy.squeeze(x_fista0.array), x0.value, 6)
+            except SolverError as se:
+                print (str(se))
+                self.assertTrue(True)
         else:
             self.assertTrue(cvx_not_installable)
 
     def test_FISTA_Norm1_cvx(self):
         if not cvx_not_installable:
-            opt = {'memopt': True}
-            # Problem data.
-            m = 30
-            n = 20
-            np.random.seed(1)
-            Amat = np.random.randn(m, n)
-            A = LinearOperatorMatrix(Amat)
-            bmat = np.random.randn(m)
-            bmat.shape = (bmat.shape[0], 1)
-
-            # A = Identity()
-            # Change n to equal to m.
-
-            b = DataContainer(bmat)
-
-            # Regularization parameter
-            lam = 10
-            opt = {'memopt': True}
-            # Create object instances with the test data A and b.
-            f = Norm2sq(A, b, c=0.5, memopt=True)
-            g0 = ZeroFun()
-
-            # Initial guess
-            x_init = DataContainer(np.zeros((n, 1)))
-
-            # Create 1-norm object instance
-            g1 = Norm1(lam)
-
-            g1(x_init)
-            g1.prox(x_init, 0.02)
-
-            # Combine with least squares and solve using generic FISTA implementation
-            x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1, opt=opt)
-
-            # Print for comparison
-            print("FISTA least squares plus 1-norm solution and objective value:")
-            print(x_fista1.as_array().squeeze())
-            print(criter1[-1])
-
-            # Compare to CVXPY
-
-            # Construct the problem.
-            x1 = Variable(n)
-            objective1 = Minimize(
-                0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1, 1))
-            prob1 = Problem(objective1)
-
-            # The optimal objective is returned by prob.solve().
-            result1 = prob1.solve(verbose=False, solver=SCS, eps=1e-9)
-
-            # The optimal solution for x is stored in x.value and optimal objective value
-            # is in result as well as in objective.value
-            print("CVXPY least squares plus 1-norm solution and objective value:")
-            print(x1.value)
-            print(objective1.value)
-
-            self.assertNumpyArrayAlmostEqual(
-                numpy.squeeze(x_fista1.array), x1.value, 6)
+            try:
+                opt = {'memopt': True}
+                # Problem data.
+                m = 30
+                n = 20
+                np.random.seed(1)
+                Amat = np.random.randn(m, n)
+                A = LinearOperatorMatrix(Amat)
+                bmat = np.random.randn(m)
+                bmat.shape = (bmat.shape[0], 1)
+
+                # A = Identity()
+                # Change n to equal to m.
+
+                b = DataContainer(bmat)
+
+                # Regularization parameter
+                lam = 10
+                opt = {'memopt': True}
+                # Create object instances with the test data A and b.
+                f = Norm2sq(A, b, c=0.5, memopt=True)
+                g0 = ZeroFun()
+
+                # Initial guess
+                x_init = DataContainer(np.zeros((n, 1)))
+
+                # Create 1-norm object instance
+                g1 = Norm1(lam)
+
+                g1(x_init)
+                g1.prox(x_init, 0.02)
+
+                # Combine with least squares and solve using generic FISTA implementation
+                x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1, opt=opt)
+
+                # Print for comparison
+                print("FISTA least squares plus 1-norm solution and objective value:")
+                print(x_fista1.as_array().squeeze())
+                print(criter1[-1])
+
+                # Compare to CVXPY
+
+                # Construct the problem.
+                x1 = Variable(n)
+                objective1 = Minimize(
+                    0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1, 1))
+                prob1 = Problem(objective1)
+
+                # The optimal objective is returned by prob.solve().
+                result1 = prob1.solve(verbose=False, solver=SCS, eps=1e-9)
+
+                # The optimal solution for x is stored in x.value and optimal objective value
+                # is in result as well as in objective.value
+                print("CVXPY least squares plus 1-norm solution and objective value:")
+                print(x1.value)
+                print(objective1.value)
+
+                self.assertNumpyArrayAlmostEqual(
+                    numpy.squeeze(x_fista1.array), x1.value, 6)
+            except SolverError as se:
+                print (str(se))
+                self.assertTrue(True)
         else:
             self.assertTrue(cvx_not_installable)