laywerrobot/lib/python3.6/site-packages/scipy/optimize/_trustregion_constr/projections.py
2020-08-27 21:55:39 +02:00

406 lines
13 KiB
Python

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