def test_GN_quadratic(self):
GN = Optimization.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_GN_quadratic(self):
GN = Optimization.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 = Optimization.ProjectedGradient(debug=True)
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_quadraticBounded(self):
PG = Optimization.ProjectedGradient(debug=True)
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 = Optimization.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)
def test_ProjGradient_quadratic1Bound(self):
myB = np.array([-5,1])
PG = Optimization.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)
