353 lines
13 KiB
Python
353 lines
13 KiB
Python
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"""
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Unit tests for trust-region iterative subproblem.
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To run it in its simplest form::
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nosetests test_optimize.py
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"""
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import numpy as np
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from scipy.optimize._trustregion_exact import (
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estimate_smallest_singular_value,
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singular_leading_submatrix,
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IterativeSubproblem)
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from scipy.linalg import (svd, get_lapack_funcs, det, qr, norm)
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from numpy.testing import (assert_array_equal,
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assert_equal, assert_array_almost_equal)
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def random_entry(n, min_eig, max_eig, case):
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# Generate random matrix
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rand = np.random.uniform(-1, 1, (n, n))
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# QR decomposition
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Q, _, _ = qr(rand, pivoting='True')
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# Generate random eigenvalues
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eigvalues = np.random.uniform(min_eig, max_eig, n)
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eigvalues = np.sort(eigvalues)[::-1]
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# Generate matrix
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Qaux = np.multiply(eigvalues, Q)
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A = np.dot(Qaux, Q.T)
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# Generate gradient vector accordingly
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# to the case is being tested.
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if case == 'hard':
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g = np.zeros(n)
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g[:-1] = np.random.uniform(-1, 1, n-1)
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g = np.dot(Q, g)
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elif case == 'jac_equal_zero':
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g = np.zeros(n)
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else:
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g = np.random.uniform(-1, 1, n)
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return A, g
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class TestEstimateSmallestSingularValue:
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def test_for_ill_condiotioned_matrix(self):
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# Ill-conditioned triangular matrix
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C = np.array([[1, 2, 3, 4],
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[0, 0.05, 60, 7],
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[0, 0, 0.8, 9],
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[0, 0, 0, 10]])
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# Get svd decomposition
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U, s, Vt = svd(C)
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# Get smallest singular value and correspondent right singular vector.
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smin_svd = s[-1]
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zmin_svd = Vt[-1, :]
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# Estimate smallest singular value
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smin, zmin = estimate_smallest_singular_value(C)
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# Check the estimation
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assert_array_almost_equal(smin, smin_svd, decimal=8)
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assert_array_almost_equal(abs(zmin), abs(zmin_svd), decimal=8)
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class TestSingularLeadingSubmatrix:
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def test_for_already_singular_leading_submatrix(self):
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# Define test matrix A.
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# Note that the leading 2x2 submatrix is singular.
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A = np.array([[1, 2, 3],
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[2, 4, 5],
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[3, 5, 6]])
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# Get Cholesky from lapack functions
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cholesky, = get_lapack_funcs(('potrf',), (A,))
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# Compute Cholesky Decomposition
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c, k = cholesky(A, lower=False, overwrite_a=False, clean=True)
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delta, v = singular_leading_submatrix(A, c, k)
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A[k-1, k-1] += delta
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# Check if the leading submatrix is singular.
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assert_array_almost_equal(det(A[:k, :k]), 0)
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# Check if `v` fullfil the specified properties
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quadratic_term = np.dot(v, np.dot(A, v))
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assert_array_almost_equal(quadratic_term, 0)
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def test_for_simetric_indefinite_matrix(self):
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# Define test matrix A.
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# Note that the leading 5x5 submatrix is indefinite.
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A = np.asarray([[1, 2, 3, 7, 8],
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[2, 5, 5, 9, 0],
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[3, 5, 11, 1, 2],
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[7, 9, 1, 7, 5],
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[8, 0, 2, 5, 8]])
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# Get Cholesky from lapack functions
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cholesky, = get_lapack_funcs(('potrf',), (A,))
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# Compute Cholesky Decomposition
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c, k = cholesky(A, lower=False, overwrite_a=False, clean=True)
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delta, v = singular_leading_submatrix(A, c, k)
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A[k-1, k-1] += delta
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# Check if the leading submatrix is singular.
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assert_array_almost_equal(det(A[:k, :k]), 0)
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# Check if `v` fullfil the specified properties
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quadratic_term = np.dot(v, np.dot(A, v))
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assert_array_almost_equal(quadratic_term, 0)
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def test_for_first_element_equal_to_zero(self):
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# Define test matrix A.
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# Note that the leading 2x2 submatrix is singular.
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A = np.array([[0, 3, 11],
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[3, 12, 5],
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[11, 5, 6]])
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# Get Cholesky from lapack functions
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cholesky, = get_lapack_funcs(('potrf',), (A,))
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# Compute Cholesky Decomposition
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c, k = cholesky(A, lower=False, overwrite_a=False, clean=True)
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delta, v = singular_leading_submatrix(A, c, k)
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A[k-1, k-1] += delta
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# Check if the leading submatrix is singular
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assert_array_almost_equal(det(A[:k, :k]), 0)
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# Check if `v` fullfil the specified properties
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quadratic_term = np.dot(v, np.dot(A, v))
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assert_array_almost_equal(quadratic_term, 0)
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class TestIterativeSubproblem:
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def test_for_the_easy_case(self):
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# `H` is chosen such that `g` is not orthogonal to the
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# eigenvector associated with the smallest eigenvalue `s`.
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H = [[10, 2, 3, 4],
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[2, 1, 7, 1],
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[3, 7, 1, 7],
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[4, 1, 7, 2]]
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g = [1, 1, 1, 1]
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# Trust Radius
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trust_radius = 1
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# Solve Subproblem
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subprob = IterativeSubproblem(x=0,
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fun=lambda x: 0,
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jac=lambda x: np.array(g),
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hess=lambda x: np.array(H),
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k_easy=1e-10,
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k_hard=1e-10)
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p, hits_boundary = subprob.solve(trust_radius)
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assert_array_almost_equal(p, [0.00393332, -0.55260862,
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0.67065477, -0.49480341])
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assert_array_almost_equal(hits_boundary, True)
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def test_for_the_hard_case(self):
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# `H` is chosen such that `g` is orthogonal to the
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# eigenvector associated with the smallest eigenvalue `s`.
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H = [[10, 2, 3, 4],
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[2, 1, 7, 1],
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[3, 7, 1, 7],
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[4, 1, 7, 2]]
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g = [6.4852641521327437, 1, 1, 1]
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s = -8.2151519874416614
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# Trust Radius
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trust_radius = 1
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# Solve Subproblem
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subprob = IterativeSubproblem(x=0,
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fun=lambda x: 0,
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jac=lambda x: np.array(g),
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hess=lambda x: np.array(H),
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k_easy=1e-10,
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k_hard=1e-10)
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p, hits_boundary = subprob.solve(trust_radius)
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assert_array_almost_equal(-s, subprob.lambda_current)
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def test_for_interior_convergence(self):
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H = [[1.812159, 0.82687265, 0.21838879, -0.52487006, 0.25436988],
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[0.82687265, 2.66380283, 0.31508988, -0.40144163, 0.08811588],
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[0.21838879, 0.31508988, 2.38020726, -0.3166346, 0.27363867],
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[-0.52487006, -0.40144163, -0.3166346, 1.61927182, -0.42140166],
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[0.25436988, 0.08811588, 0.27363867, -0.42140166, 1.33243101]]
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g = [0.75798952, 0.01421945, 0.33847612, 0.83725004, -0.47909534]
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# Solve Subproblem
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subprob = IterativeSubproblem(x=0,
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fun=lambda x: 0,
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jac=lambda x: np.array(g),
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hess=lambda x: np.array(H))
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p, hits_boundary = subprob.solve(1.1)
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assert_array_almost_equal(p, [-0.68585435, 0.1222621, -0.22090999,
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-0.67005053, 0.31586769])
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assert_array_almost_equal(hits_boundary, False)
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assert_array_almost_equal(subprob.lambda_current, 0)
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assert_array_almost_equal(subprob.niter, 1)
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def test_for_jac_equal_zero(self):
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H = [[0.88547534, 2.90692271, 0.98440885, -0.78911503, -0.28035809],
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[2.90692271, -0.04618819, 0.32867263, -0.83737945, 0.17116396],
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[0.98440885, 0.32867263, -0.87355957, -0.06521957, -1.43030957],
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[-0.78911503, -0.83737945, -0.06521957, -1.645709, -0.33887298],
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[-0.28035809, 0.17116396, -1.43030957, -0.33887298, -1.68586978]]
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g = [0, 0, 0, 0, 0]
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# Solve Subproblem
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subprob = IterativeSubproblem(x=0,
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fun=lambda x: 0,
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jac=lambda x: np.array(g),
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hess=lambda x: np.array(H),
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k_easy=1e-10,
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k_hard=1e-10)
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p, hits_boundary = subprob.solve(1.1)
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assert_array_almost_equal(p, [0.06910534, -0.01432721,
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-0.65311947, -0.23815972,
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-0.84954934])
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assert_array_almost_equal(hits_boundary, True)
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def test_for_jac_very_close_to_zero(self):
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H = [[0.88547534, 2.90692271, 0.98440885, -0.78911503, -0.28035809],
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[2.90692271, -0.04618819, 0.32867263, -0.83737945, 0.17116396],
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[0.98440885, 0.32867263, -0.87355957, -0.06521957, -1.43030957],
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[-0.78911503, -0.83737945, -0.06521957, -1.645709, -0.33887298],
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[-0.28035809, 0.17116396, -1.43030957, -0.33887298, -1.68586978]]
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g = [0, 0, 0, 0, 1e-15]
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# Solve Subproblem
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subprob = IterativeSubproblem(x=0,
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fun=lambda x: 0,
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jac=lambda x: np.array(g),
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hess=lambda x: np.array(H),
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k_easy=1e-10,
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k_hard=1e-10)
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p, hits_boundary = subprob.solve(1.1)
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assert_array_almost_equal(p, [0.06910534, -0.01432721,
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-0.65311947, -0.23815972,
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-0.84954934])
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assert_array_almost_equal(hits_boundary, True)
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def test_for_random_entries(self):
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# Seed
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np.random.seed(1)
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# Dimension
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n = 5
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for case in ('easy', 'hard', 'jac_equal_zero'):
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eig_limits = [(-20, -15),
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(-10, -5),
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(-10, 0),
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(-5, 5),
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(-10, 10),
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(0, 10),
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(5, 10),
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(15, 20)]
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for min_eig, max_eig in eig_limits:
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# Generate random symmetric matrix H with
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# eigenvalues between min_eig and max_eig.
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H, g = random_entry(n, min_eig, max_eig, case)
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# Trust radius
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trust_radius_list = [0.1, 0.3, 0.6, 0.8, 1, 1.2, 3.3, 5.5, 10]
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for trust_radius in trust_radius_list:
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# Solve subproblem with very high accuracy
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subprob_ac = IterativeSubproblem(0,
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lambda x: 0,
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lambda x: g,
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lambda x: H,
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k_easy=1e-10,
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k_hard=1e-10)
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p_ac, hits_boundary_ac = subprob_ac.solve(trust_radius)
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# Compute objective function value
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J_ac = 1/2*np.dot(p_ac, np.dot(H, p_ac))+np.dot(g, p_ac)
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stop_criteria = [(0.1, 2),
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(0.5, 1.1),
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(0.9, 1.01)]
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for k_opt, k_trf in stop_criteria:
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# k_easy and k_hard computed in function
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# of k_opt and k_trf accordingly to
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# Conn, A. R., Gould, N. I., & Toint, P. L. (2000).
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# "Trust region methods". Siam. p. 197.
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k_easy = min(k_trf-1,
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1-np.sqrt(k_opt))
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k_hard = 1-k_opt
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# Solve subproblem
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subprob = IterativeSubproblem(0,
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lambda x: 0,
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lambda x: g,
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lambda x: H,
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k_easy=k_easy,
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k_hard=k_hard)
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p, hits_boundary = subprob.solve(trust_radius)
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# Compute objective function value
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J = 1/2*np.dot(p, np.dot(H, p))+np.dot(g, p)
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# Check if it respect k_trf
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if hits_boundary:
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assert_array_equal(np.abs(norm(p)-trust_radius) <=
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(k_trf-1)*trust_radius, True)
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else:
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assert_equal(norm(p) <= trust_radius, True)
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# Check if it respect k_opt
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assert_equal(J <= k_opt*J_ac, True)
|
||
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|
|