406 lines
13 KiB
Python
406 lines
13 KiB
Python
"""Basic linear factorizations needed by the solver."""
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from scipy.sparse import (bmat, csc_matrix, eye, issparse)
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from scipy.sparse.linalg import LinearOperator
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import scipy.linalg
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import scipy.sparse.linalg
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try:
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from sksparse.cholmod import cholesky_AAt
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sksparse_available = True
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except ImportError:
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import warnings
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sksparse_available = False
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import numpy as np
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from warnings import warn
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__all__ = [
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'orthogonality',
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'projections',
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]
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def orthogonality(A, g):
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"""Measure orthogonality between a vector and the null space of a matrix.
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Compute a measure of orthogonality between the null space
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of the (possibly sparse) matrix ``A`` and a given vector ``g``.
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The formula is a simplified (and cheaper) version of formula (3.13)
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from [1]_.
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``orth = norm(A g, ord=2)/(norm(A, ord='fro')*norm(g, ord=2))``.
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References
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----------
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.. [1] Gould, Nicholas IM, Mary E. Hribar, and Jorge Nocedal.
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"On the solution of equality constrained quadratic
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programming problems arising in optimization."
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SIAM Journal on Scientific Computing 23.4 (2001): 1376-1395.
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"""
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# Compute vector norms
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norm_g = np.linalg.norm(g)
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# Compute Froebnius norm of the matrix A
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if issparse(A):
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norm_A = scipy.sparse.linalg.norm(A, ord='fro')
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else:
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norm_A = np.linalg.norm(A, ord='fro')
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# Check if norms are zero
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if norm_g == 0 or norm_A == 0:
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return 0
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norm_A_g = np.linalg.norm(A.dot(g))
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# Orthogonality measure
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orth = norm_A_g / (norm_A*norm_g)
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return orth
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def normal_equation_projections(A, m, n, orth_tol, max_refin, tol):
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"""Return linear operators for matrix A using ``NormalEquation`` approach.
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"""
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# Cholesky factorization
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factor = cholesky_AAt(A)
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# z = x - A.T inv(A A.T) A x
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def null_space(x):
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v = factor(A.dot(x))
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z = x - A.T.dot(v)
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# Iterative refinement to improve roundoff
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# errors described in [2]_, algorithm 5.1.
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k = 0
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while orthogonality(A, z) > orth_tol:
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if k >= max_refin:
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break
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# z_next = z - A.T inv(A A.T) A z
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v = factor(A.dot(z))
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z = z - A.T.dot(v)
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k += 1
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return z
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# z = inv(A A.T) A x
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def least_squares(x):
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return factor(A.dot(x))
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# z = A.T inv(A A.T) x
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def row_space(x):
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return A.T.dot(factor(x))
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return null_space, least_squares, row_space
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def augmented_system_projections(A, m, n, orth_tol, max_refin, tol):
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"""Return linear operators for matrix A - ``AugmentedSystem``."""
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# Form augmented system
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K = csc_matrix(bmat([[eye(n), A.T], [A, None]]))
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# LU factorization
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# TODO: Use a symmetric indefinite factorization
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# to solve the system twice as fast (because
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# of the symmetry).
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try:
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solve = scipy.sparse.linalg.factorized(K)
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except RuntimeError:
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warn("Singular Jacobian matrix. Using dense SVD decomposition to "
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"perform the factorizations.")
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return svd_factorization_projections(A.toarray(),
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m, n, orth_tol,
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max_refin, tol)
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# z = x - A.T inv(A A.T) A x
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# is computed solving the extended system:
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# [I A.T] * [ z ] = [x]
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# [A O ] [aux] [0]
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def null_space(x):
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# v = [x]
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# [0]
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v = np.hstack([x, np.zeros(m)])
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# lu_sol = [ z ]
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# [aux]
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lu_sol = solve(v)
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z = lu_sol[:n]
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# Iterative refinement to improve roundoff
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# errors described in [2]_, algorithm 5.2.
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k = 0
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while orthogonality(A, z) > orth_tol:
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if k >= max_refin:
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break
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# new_v = [x] - [I A.T] * [ z ]
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# [0] [A O ] [aux]
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new_v = v - K.dot(lu_sol)
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# [I A.T] * [delta z ] = new_v
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# [A O ] [delta aux]
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lu_update = solve(new_v)
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# [ z ] += [delta z ]
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# [aux] [delta aux]
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lu_sol += lu_update
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z = lu_sol[:n]
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k += 1
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# return z = x - A.T inv(A A.T) A x
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return z
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# z = inv(A A.T) A x
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# is computed solving the extended system:
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# [I A.T] * [aux] = [x]
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# [A O ] [ z ] [0]
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def least_squares(x):
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# v = [x]
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# [0]
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v = np.hstack([x, np.zeros(m)])
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# lu_sol = [aux]
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# [ z ]
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lu_sol = solve(v)
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# return z = inv(A A.T) A x
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return lu_sol[n:m+n]
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# z = A.T inv(A A.T) x
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# is computed solving the extended system:
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# [I A.T] * [ z ] = [0]
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# [A O ] [aux] [x]
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def row_space(x):
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# v = [0]
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# [x]
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v = np.hstack([np.zeros(n), x])
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# lu_sol = [ z ]
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# [aux]
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lu_sol = solve(v)
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# return z = A.T inv(A A.T) x
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return lu_sol[:n]
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return null_space, least_squares, row_space
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def qr_factorization_projections(A, m, n, orth_tol, max_refin, tol):
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"""Return linear operators for matrix A using ``QRFactorization`` approach.
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"""
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# QRFactorization
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Q, R, P = scipy.linalg.qr(A.T, pivoting=True, mode='economic')
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if np.linalg.norm(R[-1, :], np.inf) < tol:
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warn('Singular Jacobian matrix. Using SVD decomposition to ' +
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'perform the factorizations.')
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return svd_factorization_projections(A, m, n,
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orth_tol,
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max_refin,
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tol)
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# z = x - A.T inv(A A.T) A x
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def null_space(x):
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# v = P inv(R) Q.T x
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aux1 = Q.T.dot(x)
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aux2 = scipy.linalg.solve_triangular(R, aux1, lower=False)
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v = np.zeros(m)
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v[P] = aux2
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z = x - A.T.dot(v)
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# Iterative refinement to improve roundoff
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# errors described in [2]_, algorithm 5.1.
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k = 0
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while orthogonality(A, z) > orth_tol:
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if k >= max_refin:
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break
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# v = P inv(R) Q.T x
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aux1 = Q.T.dot(z)
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aux2 = scipy.linalg.solve_triangular(R, aux1, lower=False)
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v[P] = aux2
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# z_next = z - A.T v
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z = z - A.T.dot(v)
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k += 1
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return z
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# z = inv(A A.T) A x
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def least_squares(x):
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# z = P inv(R) Q.T x
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aux1 = Q.T.dot(x)
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aux2 = scipy.linalg.solve_triangular(R, aux1, lower=False)
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z = np.zeros(m)
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z[P] = aux2
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return z
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# z = A.T inv(A A.T) x
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def row_space(x):
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# z = Q inv(R.T) P.T x
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aux1 = x[P]
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aux2 = scipy.linalg.solve_triangular(R, aux1,
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lower=False,
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trans='T')
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z = Q.dot(aux2)
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return z
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return null_space, least_squares, row_space
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def svd_factorization_projections(A, m, n, orth_tol, max_refin, tol):
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"""Return linear operators for matrix A using ``SVDFactorization`` approach.
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"""
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# SVD Factorization
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U, s, Vt = scipy.linalg.svd(A, full_matrices=False)
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# Remove dimensions related with very small singular values
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U = U[:, s > tol]
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Vt = Vt[s > tol, :]
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s = s[s > tol]
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# z = x - A.T inv(A A.T) A x
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def null_space(x):
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# v = U 1/s V.T x = inv(A A.T) A x
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aux1 = Vt.dot(x)
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aux2 = 1/s*aux1
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v = U.dot(aux2)
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z = x - A.T.dot(v)
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# Iterative refinement to improve roundoff
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# errors described in [2]_, algorithm 5.1.
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k = 0
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while orthogonality(A, z) > orth_tol:
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if k >= max_refin:
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break
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# v = U 1/s V.T x = inv(A A.T) A x
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aux1 = Vt.dot(z)
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aux2 = 1/s*aux1
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v = U.dot(aux2)
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# z_next = z - A.T v
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z = z - A.T.dot(v)
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k += 1
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return z
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# z = inv(A A.T) A x
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def least_squares(x):
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# z = U 1/s V.T x = inv(A A.T) A x
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aux1 = Vt.dot(x)
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aux2 = 1/s*aux1
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z = U.dot(aux2)
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return z
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# z = A.T inv(A A.T) x
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def row_space(x):
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# z = V 1/s U.T x
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aux1 = U.T.dot(x)
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aux2 = 1/s*aux1
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z = Vt.T.dot(aux2)
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return z
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return null_space, least_squares, row_space
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def projections(A, method=None, orth_tol=1e-12, max_refin=3, tol=1e-15):
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"""Return three linear operators related with a given matrix A.
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Parameters
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----------
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A : sparse matrix (or ndarray), shape (m, n)
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Matrix ``A`` used in the projection.
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method : string, optional
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Method used for compute the given linear
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operators. Should be one of:
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- 'NormalEquation': The operators
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will be computed using the
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so-called normal equation approach
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explained in [1]_. In order to do
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so the Cholesky factorization of
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``(A A.T)`` is computed. Exclusive
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for sparse matrices.
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- 'AugmentedSystem': The operators
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will be computed using the
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so-called augmented system approach
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explained in [1]_. Exclusive
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for sparse matrices.
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- 'QRFactorization': Compute projections
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using QR factorization. Exclusive for
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dense matrices.
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- 'SVDFactorization': Compute projections
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using SVD factorization. Exclusive for
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dense matrices.
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orth_tol : float, optional
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Tolerance for iterative refinements.
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max_refin : int, optional
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Maximum number of iterative refinements.
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tol : float, optional
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Tolerance for singular values.
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Returns
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-------
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Z : LinearOperator, shape (n, n)
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Null-space operator. For a given vector ``x``,
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the null space operator is equivalent to apply
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a projection matrix ``P = I - A.T inv(A A.T) A``
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to the vector. It can be shown that this is
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equivalent to project ``x`` into the null space
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of A.
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LS : LinearOperator, shape (m, n)
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Least-squares operator. For a given vector ``x``,
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the least-squares operator is equivalent to apply a
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pseudoinverse matrix ``pinv(A.T) = inv(A A.T) A``
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to the vector. It can be shown that this vector
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``pinv(A.T) x`` is the least_square solution to
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``A.T y = x``.
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Y : LinearOperator, shape (n, m)
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Row-space operator. For a given vector ``x``,
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the row-space operator is equivalent to apply a
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projection matrix ``Q = A.T inv(A A.T)``
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to the vector. It can be shown that this
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vector ``y = Q x`` the minimum norm solution
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of ``A y = x``.
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Notes
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-----
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Uses iterative refinements described in [1]
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during the computation of ``Z`` in order to
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cope with the possibility of large roundoff errors.
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References
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----------
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.. [1] Gould, Nicholas IM, Mary E. Hribar, and Jorge Nocedal.
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"On the solution of equality constrained quadratic
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programming problems arising in optimization."
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SIAM Journal on Scientific Computing 23.4 (2001): 1376-1395.
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"""
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m, n = np.shape(A)
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# The factorization of an empty matrix
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# only works for the sparse representation.
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if m*n == 0:
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A = csc_matrix(A)
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# Check Argument
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if issparse(A):
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if method is None:
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method = "AugmentedSystem"
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if method not in ("NormalEquation", "AugmentedSystem"):
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raise ValueError("Method not allowed for sparse matrix.")
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if method == "NormalEquation" and not sksparse_available:
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warnings.warn(("Only accepts 'NormalEquation' option when"
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" scikit-sparse is available. Using "
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"'AugmentedSystem' option instead."),
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ImportWarning)
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method = 'AugmentedSystem'
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else:
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if method is None:
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method = "QRFactorization"
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if method not in ("QRFactorization", "SVDFactorization"):
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raise ValueError("Method not allowed for dense array.")
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if method == 'NormalEquation':
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null_space, least_squares, row_space \
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= normal_equation_projections(A, m, n, orth_tol, max_refin, tol)
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elif method == 'AugmentedSystem':
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null_space, least_squares, row_space \
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= augmented_system_projections(A, m, n, orth_tol, max_refin, tol)
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elif method == "QRFactorization":
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null_space, least_squares, row_space \
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= qr_factorization_projections(A, m, n, orth_tol, max_refin, tol)
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elif method == "SVDFactorization":
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null_space, least_squares, row_space \
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= svd_factorization_projections(A, m, n, orth_tol, max_refin, tol)
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Z = LinearOperator((n, n), null_space)
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LS = LinearOperator((m, n), least_squares)
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Y = LinearOperator((n, m), row_space)
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return Z, LS, Y
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