pymor.algorithms.krylov
¶
Module for computing (rational) Krylov subspaces’ bases.
Module Contents¶
- pymor.algorithms.krylov.arnoldi(A, E, b, r)[source]¶
Block Arnoldi algorithm.
Computes an orthonormal basis for the Krylov subspace
\[\mathrm{span}\left\{ E^{-1} b, E^{-1} A E^{-1} b, {\left(E^{-1} A\right)}^2 E^{-1} b, \ldots, {\left(E^{-1} A\right)}^{r - 1} E^{-1} b, \right\}.\]Parameters
- A
The
Operator
A.- E
The
Operator
E. IfNone
, the identity operator is assumed.- b
The
VectorArray
b.- r
Order of the Krylov subspace (positive integer).
Returns
- V
Orthonormal basis for the Krylov subspace as a
VectorArray
withlen(V) <= r*len(b)
(strict inequality in case of deflation).
- pymor.algorithms.krylov.rational_arnoldi(A, E, b, sigma, trans=False)[source]¶
Rational Arnoldi algorithm.
If
trans == False
, using Arnoldi process, computes a real orthonormal basis for the rational Krylov subspace\[\mathrm{span}\{ (\sigma_1 E - A)^{-1} b, (\sigma_2 E - A)^{-1} b, \ldots, (\sigma_r E - A)^{-1} b \},\]otherwise, computes the same for
\[\mathrm{span}\{ (\sigma_1 E - A)^{-T} b^T, (\sigma_2 E - A)^{-T} b^T, \ldots, (\sigma_r E - A)^{-T} b^T \}.\]Interpolation points in
sigma
are allowed to repeat (in any order). Then, in the above expression,\[\underbrace{ (\sigma_i E - A)^{-1} b, \ldots, (\sigma_i E - A)^{-1} b }_{m \text{ times}}\]is replaced by
\[(\sigma_i E - A)^{-1} b, (\sigma_i E - A)^{-1} E (\sigma_i E - A)^{-1} b, \ldots, \left((\sigma_i E - A)^{-1} E\right)^{m - 1} (\sigma_i E - A)^{-1} b.\]Analogously for the
trans == True
case.Parameters
Returns
- V
Orthonormal basis for the Krylov subspace as a
VectorArray
.
- pymor.algorithms.krylov.tangential_rational_krylov(A, E, B, b, sigma, trans=False, orth=True)[source]¶
Tangential Rational Krylov subspace.
If
trans == False
, computes a real basis for the rational Krylov subspace\[\mathrm{span}\{ (\sigma_1 E - A)^{-1} B b_1, (\sigma_2 E - A)^{-1} B b_2, \ldots, (\sigma_r E - A)^{-1} B b_r \},\]otherwise, computes the same for
\[\mathrm{span}\{ (\sigma_1 E - A)^{-T} B^T b_1, (\sigma_2 E - A)^{-T} B^T b_2, \ldots, (\sigma_r E - A)^{-T} B^T b_r \}.\]Interpolation points in
sigma
are assumed to be pairwise distinct.Parameters
- A
Real
Operator
A.- E
Real
Operator
E.- B
Real
Operator
B.- b
VectorArray
fromB.source
, iftrans == False
, orB.range
, iftrans == True
.
- sigma
Sequence of interpolation points (closed under conjugation), of the same length as
b
.- trans
Boolean, see above.
- orth
If
True
, orthonormalizes the basis usingpymor.algorithms.gram_schmidt.gram_schmidt
.
Returns
- V
Optionally orthonormal basis for the Krylov subspace as a
VectorArray
.