Updates to Optimization Framework. Testing. Bug Fixes.

SimPEG/tests/TestUtils.py

@@ -275,6 +275,28 @@ def checkDerivative(fctn, x0, num=7, plotIt=True, dx=None):
     return passTest
 
 
+
+def getQuadratic(A, b):
+    """
+        Given A and b, this returns a quadratic, Q
+
+        .. math::
+
+            \mathbf{Q( x ) = 0.5 x A x + b x}
+    """
+    def Quadratic(x, return_g=True, return_H=True):
+        f = 0.5 * x.dot( A.dot(x)) + b.dot( x )
+        out = (f,)
+        if return_g:
+            g = A.dot(x) + b
+            out += (g,)
+        if return_H:
+            H = A
+            out += (H,)
+        return out if len(out) > 1 else out[0]
+    return Quadratic
+
+
 if __name__ == '__main__':
 
     def simplePass(x):

SimPEG/tests/__init__.py

@@ -1,2 +1,2 @@
 import TestUtils
-from TestUtils import checkDerivative, Rosenbrock, OrderTest
+from TestUtils import checkDerivative, Rosenbrock, OrderTest, getQuadratic

SimPEG/tests/test_optimizers.py

@@ -0,0 +1,54 @@
+import unittest
+from SimPEG import Solver
+from SimPEG.mesh import TensorMesh
+from SimPEG.utils import sdiag
+import numpy as np
+import scipy.sparse as sp
+from SimPEG import inverse
+from SimPEG.tests import getQuadratic, Rosenbrock
+
+TOL = 1e-2
+
+class TestOptimizers(unittest.TestCase):
+
+    def setUp(self):
+        self.A = sp.identity(2).tocsr()
+        self.b = np.array([-5,-5])
+
+    def test_GN_Rosenbrock(self):
+        GN = inverse.GaussNewton()
+        xopt = GN.minimize(Rosenbrock,np.array([0,0]))
+        x_true = np.array([1.,1.])
+        print 'xopt: ', xopt
+        print 'x_true: ', x_true
+        self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
+
+    def test_GN_quadratic(self):
+        GN = inverse.GaussNewton()
+        xopt = GN.minimize(getQuadratic(self.A,self.b),np.array([0,0]))
+        x_true = np.array([5.,5.])
+        print 'xopt: ', xopt
+        print 'x_true: ', x_true
+        self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
+
+    def test_ProjGradient_quadraticBounded(self):
+        PG = inverse.ProjectedGradient()
+        PG.lower, PG.upper = -2, 2
+        xopt = PG.minimize(getQuadratic(self.A,self.b),np.array([0,0]))
+        x_true = np.array([2.,2.])
+        print 'xopt: ', xopt
+        print 'x_true: ', x_true
+        self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
+
+    def test_ProjGradient_quadratic1Bound(self):
+        myB = np.array([-5,1])
+        PG = inverse.ProjectedGradient()
+        PG.lower, PG.upper = -2, 2
+        xopt = PG.minimize(getQuadratic(self.A,myB),np.array([0,0]))
+        x_true = np.array([2.,-1.])
+        print 'xopt: ', xopt
+        print 'x_true: ', x_true
+        self.assertTrue(np.linalg.norm(xopt-x_true,2) < TOL, True)
+
+if __name__ == '__main__':
+    unittest.main()