# Source code for pymor.algorithms.basic

```
# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2018 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
"""Module containing some basic but generic linear algebra algorithms."""
import numpy as np
import scipy
from pymor.core.defaults import defaults
from pymor.operators.constructions import induced_norm
[docs]@defaults('rtol', 'atol')
def almost_equal(U, V, product=None, norm=None, rtol=1e-14, atol=1e-14):
"""Compare U and V for almost equality.
The vectors of `U` and `V` are compared in pairs for almost equality.
Two vectors `u` and `v` are considered almost equal iff
||u - v|| <= atol + ||v|| * rtol.
The norm to be used can be specified via the `norm` or `product`
parameter.
If the length of `U` resp. `V` is 1, the single specified
vector is compared to all vectors of the other array.
Otherwise, the lengths of both indexed arrays have to agree.
Parameters
----------
U, V
|VectorArrays| to be compared.
product
If specified, use this inner product |Operator| to compute the norm.
`product` and `norm` are mutually exclusive.
norm
If specified, must be a callable which is used to compute the norm
or, alternatively, one of the strings 'l1', 'l2', 'sup', in which case the
respective |VectorArray| norm methods are used.
`product` and `norm` are mutually exclusive. If neither is specified,
`norm='l2'` is assumed.
rtol
The relative tolerance.
atol
The absolute tolerance.
"""
assert product is None or norm is None
assert not isinstance(norm, str) or norm in ('l1', 'l2', 'sup')
norm = induced_norm(product) if product is not None else norm
if norm is None:
norm = 'l2'
if isinstance(norm, str):
norm_str = norm
norm = lambda U: getattr(U, norm_str + '_norm')()
X = V.copy()
V_norm = norm(X)
# broadcast if necessary
if len(X) == 1:
if len(U) > 1:
X.append(X[np.zeros(len(U) - 1, dtype=np.int)])
X -= U
ERR_norm = norm(X)
return ERR_norm <= atol + V_norm * rtol
[docs]def relative_error(U, V, product=None):
"""Compute error between U and V relative to norm of U."""
return (U - V).norm(product) / U.norm(product)
[docs]def project_array(U, basis, product=None, orthonormal=True):
"""Orthogonal projection of |VectorArray| onto subspace.
Parameters
----------
U
The |VectorArray| to project.
basis
|VectorArray| of basis vectors for the subspace onto which
to project.
product
Inner product |Operator| w.r.t. which to project.
orthonormal
If `True`, the vectors in `basis` are assumed to be orthonormal
w.r.t. `product`.
Returns
-------
The projected |VectorArray|.
"""
if orthonormal:
return basis.lincomb(U.inner(basis, product))
else:
gramian = basis.gramian(product)
rhs = basis.inner(U, product)
coeffs = scipy.linalg.solve(gramian, rhs, sym_pos=True, overwrite_a=True, overwrite_b=True).T
return basis.lincomb(coeffs)
```