`pymor.reductors.aaa`¶

Module Contents¶

class pymor.reductors.aaa.PAAAReductor(sampling_values, samples_or_fom, conjugate=True, nsp_tol=1e-16, post_process=True, L_rk_tol=1e-08)[source]¶

Bases: pymor.core.base.BasicObject

Reductor implementing the parametric AAA algorithm.

The reductor implements the parametric AAA algorithm and can be used either with data or a given full-order model, which can be a TransferFunction or any model which has a transfer_function attribute. MIMO and non-parametric data is accepted. See [NSeteT18] for the non-parametric and [CRBG20] for the parametric version of the algorithm. The reductor computes approximations based on the multivariate barycentric form $H (s, p, . . .)$ (see make_bary_func), where the inputs $s, p, . . .$ are referred to as the variables. Further, $s$ is the Laplace variable and $p$ as well as other remaining inputs are parameters.

Note

The dimension of the Loewner matrix which will be assembled in the algorithm has len(sampling_values[0])*...*len(sampling_values[-1]) rows. This reductor should only be used with a low number of variables.

Parameters

sampling_values: Values where sample data has been evaluated or the full-order model should be evaluated. Sampling values are represented as a nested list such that sampling_values[i] corresponds to sampling values of the i-th variable. The first variable is the Laplace variable.
samples_or_fom: Can be either a full-order model (TransferFunction or Model with a transfer_function attribute) or data sampled at the values specified in sampling_values. Samples are represented as a tensor S. E.g., for 3 inputs S[i,j,k] corresponds to the sampled value at (sampling_values[0][i],sampling_values[1][j],sampling_values[2][k]). In the MIMO case S[i,j,k] represents a matrix of dimension dim_output times dim_input.
conjugate: Whether to compute complex conjugates of first sampling variables and enforce interpolation in complex conjugate pairs (allows for constructing real system matrices).
nsp_tol: Tolerance for null space of higher-dimensional Loewner matrix to check for interpolation or convergence.
post_process: Whether to do post-processing or not. If the Loewner matrix has a null space of dimension greater than 1, it is assumed that the algorithm converged to a non-minimal order interpolant which may cause numerical issues. In this case, the post-processing procedure computes an interpolant of minimal order.
L_rk_tol: Tolerance for ranks of 1-D Loewner matrices computed in post-processing.

itpl_part[source]¶: A nested list such that itpl_part[i] corresponds to indices of interpolated values with respect to the i-th variable. I.e., self.sampling_values[i][itpl_part[i]] represents a list of all interpolated samples of the i-th variable.

Methods

reduce

Reduce using p-AAA.

reduce(tol=1e-07, itpl_part=None, max_itpl=None)[source]¶

Reduce using p-AAA.

Parameters

tol: Convergence tolerance for relative error of rom over the set of samples.
itpl_part: Initial partition for interpolation values. Should be None or a nested list such that itpl_part[i] corresponds to indices of interpolated values with respect to the i-th variable. I.e., self.sampling_values[i][itpl_part[i]] represents a list of all initially interpolated samples of the i-th variable. If None p-AAA will start with no interpolated values.
max_itpl: Maximum number of interpolation points to use with respect to each variable. Should be None or a list such that self.num_vars == len(max_itpl). If None max_itpl[i] will be set to len(self.sampling_values[i]) - 1.

Returns

rom: Reduced TransferFunction model.

pymor.reductors.aaa.full_nd_loewner(samples, svs, itpl_part)[source]¶

Compute higher-dimensional Loewner matrix using all combinations of partitions.

Note

For non-parametric data this is simply the regular Loewner matrix.

Parameters

samples: Tensor of samples (see PAAAReductor).
svs: List of sampling values (see PAAAReductor).
itpl_part: Nested list such that itpl_part[i] is a list of indices for interpolated sampling values in svs[i].

Returns

L: (Parametric) Loewner matrix based on all combinations of partitions.

pymor.reductors.aaa.make_bary_func(itpl_nodes, itpl_vals, coefs, removable_singularity_tol=1e-14)[source]¶

Return function for (multivariate) barycentric form.

The multivariate barycentric form for two variables is given by

H (s, p) = \frac{\sum_{i = 1}^{k} \sum_{j = 1}^{q} \frac{α_{i j} H (s_{i}, p_{j})}{(s - s_{i}) (p - p_{j})}}{\sum_{i = 1}^{k} \sum_{j = 1}^{q} \frac{α_{i j}}{(s - s_{i}) (p - p_{j})}}

where for $i = 1, \dots, k$ and $j = 1, \dots, q$ we have that $s_{i}$ and $p_{j}$ are interpolation nodes, $H (s_{i}, p_{j})$ are interpolation values and $α_{i j}$ represent the barycentric coefficients. This implementation can also handle single-variable barycentric forms as well as barycentric forms with more than two variables.

Parameters

itpl_nodes: Nested list such that itpl_nodes[i] contains interpolation nodes of the i-th variable.
itpl_vals: Vector of interpolation values with len(itpl_nodes[0])*...*len(itpl_nodes[-1]) entries.
coefs: Vector of barycentric coefficients with len(itpl_nodes[0])*...*len(itpl_nodes[-1]) entries.
removable_singularity_tol: Tolerance for evaluating the barycentric function at a removable singularity and performing pole cancellation.

Returns

bary_func: (Multi-variate) rational function in barycentric form.

pymor.reductors.aaa.nd_loewner(samples, svs, itpl_part)[source]¶

Compute higher-dimensional Loewner matrix using only LS partitions.

Note

For non-parametric data this is simply the regular Loewner matrix.

Parameters

samples: Tensor of samples (see PAAAReductor).
svs: List of sampling values (see PAAAReductor).
itpl_part: Nested list such that itpl_part[i] is a list of indices for interpolated sampling values in svs[i].

Returns

L: (Parametric) Loewner matrix based only on LS partition.

pymor.reductors.aaa¶

Module Contents¶

`pymor.reductors.aaa`¶