def testGoldsteinConditionsWithSuppliedGradientTwo(self):
problem = OptimizationProblem(f, grad)
op = ClassicalNewton(problem)
actual = op.solve((1))
self.assert_almost_equal(expected, actual, 0.0001)
def testRosenBrockWithEasyInitialGuess(self):
problem = OptimizationProblem(f)
op = ClassicalNewton(problem)
res = op.solve((2,2), search='inexact', cond = 'GS')
np.testing.assert_almost_equal(self.ros_res, res)
def testRosenBrockWithHardInitialGuessAndSuppliedGradient(self):
problem = OptimizationProblem(f, grad)
op = ClassicalNewton(problem)
res = op.solve((-1.2, 1), search='exact')
np.testing.assert_almost_equal(self.ros_res, res)
def testRosenBrockWithStandardInitialGuess(self):
problem = OptimizationProblem(f)
op = ClassicalNewton(problem)
res = op.solve(search='exact')
np.testing.assert_almost_equal(self.ros_res, res)
def testHessianFunctionWithGradient(self):
problem = OptimizationProblem(f, grad)
op = ClassicalNewton(problem)
hessian = op.get_hessian(f, (1,2))
np.testing.assert_allclose(self.man_hess, hessian, 0.0001)
def testSuppliedGradient(self):
problem = OptimizationProblem(f, grad)
op = ClassicalNewton(problem)
gradient = op.get_gradient(f, (1,2))
np.testing.assert_allclose(gradient, self.man_gradient)