diff options
| -rw-r--r-- | README.md | 6 | ||||
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/funcs.py | 9 | ||||
| -rwxr-xr-x | Wrappers/Python/test/test_run_test.py | 226 |
3 files changed, 127 insertions, 114 deletions
@@ -1,7 +1,7 @@ -| master build | pull request build | -|--------|-------------| -| [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Framework/) | [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Framework-dev/) | +| Master | Development | Anaconda binaries | +|--------|-------------|-------------------| +| [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Framework/) | [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Framework-dev/) |  [ ](https://anaconda.org/ccpi/ccpi-framework) | # CCPi-Framework 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) |