def estimate_spectral_norm(A, its=20):
"""
Estimate spectral norm of a matrix by the randomized power method.
.. This function automatically detects the matrix data type and calls the
appropriate backend. For details, see :func:`backend.idd_snorm` and
:func:`backend.idz_snorm`.
Parameters
----------
A : :class:`scipy.sparse.linalg.LinearOperator`
Matrix given as a :class:`scipy.sparse.linalg.LinearOperator` with the
`matvec` and `rmatvec` methods (to apply the matrix and its adjoint).
its : int, optional
Number of power method iterations.
Returns
-------
float
Spectral norm estimate.
"""
from scipy.sparse.linalg import aslinearoperator
A = aslinearoperator(A)
m, n = A.shape
matvec = lambda x: A.matvec(x)
matveca = lambda x: A.rmatvec(x)
if _is_real(A):
return backend.idd_snorm(m, n, matveca, matvec, its=its)
else:
return backend.idz_snorm(m, n, matveca, matvec, its=its)
def estimate_spectral_norm(A, its=20):
"""
Estimate spectral norm of a matrix by the randomized power method.
.. This function automatically detects the matrix data type and calls the
appropriate backend. For details, see :func:`backend.idd_snorm` and
:func:`backend.idz_snorm`.
Parameters
----------
A : :class:`scipy.sparse.linalg.LinearOperator`
Matrix given as a :class:`scipy.sparse.linalg.LinearOperator` with the
`matvec` and `rmatvec` methods (to apply the matrix and its adjoint).
its : int
Number of power method iterations.
Returns
-------
float
Spectral norm estimate.
"""
from scipy.sparse.linalg import aslinearoperator
A = aslinearoperator(A)
m, n = A.shape
matvec = lambda x: A. matvec(x)
matveca = lambda x: A.rmatvec(x)
if _is_real(A):
return backend.idd_snorm(m, n, matveca, matvec, its=its)
else:
return backend.idz_snorm(m, n, matveca, matvec, its=its)