pymor.reductors.bt

Module Contents

class pymor.reductors.bt.BRBTReductor(fom, gamma=1, mu=None)[source]

Bases: GenericBTReductor

Bounded Real (BR) Balanced Truncation reductor.

See [Ant05] (Section 7.5.3) and [OJ88].

Parameters:

  • fom – The full-order LTIModel to reduce.

  • gamma – Upper bound for the \(\mathcal{H}_\infty\)-norm.

  • mu – Parameter values.

Methods

error_bounds

Return error bounds for all possible reduced orders.
error_bounds()[source]

Return error bounds for all possible reduced orders.

class pymor.reductors.bt.BTReductor(fom, mu=None)[source]

Bases: GenericBTReductor

Standard (Lyapunov) Balanced Truncation reductor.

See Section 7.3 in [Ant05].

Parameters:

Methods

error_bounds

Return error bounds for all possible reduced orders.
error_bounds()[source]

Return error bounds for all possible reduced orders.

class pymor.reductors.bt.FDBTReductor(fom, mu=None)[source]

Bases: GenericBTReductor

Balanced Truncation reductor using frequency domain representation of Gramians.

See [ZSW99].

Parameters:

Methods

error_bounds

L-infinity error bounds for reduced order models.
error_bounds()[source]

L-infinity error bounds for reduced order models.

class pymor.reductors.bt.GenericBTReductor(fom, mu=None)[source]

Bases: pymor.core.base.BasicObject

Generic Balanced Truncation reductor.

Parameters:

Methods

error_bounds

Return error bounds for all possible reduced orders.

reconstruct

Reconstruct high-dimensional vector from reduced vector u.

reduce

Generic Balanced Truncation.
abstract error_bounds()[source]

Return error bounds for all possible reduced orders.

reconstruct(u)[source]

Reconstruct high-dimensional vector from reduced vector u.

reduce(r=None, tol=None, projection='bfsr')[source]

Generic Balanced Truncation.

Parameters:

  • r – Order of the reduced model if tol is None, maximum order if tol is specified.

  • tol – Tolerance for the error bound if r is None.

  • projection –

    Projection method used:

    • 'sr': square root method

    • 'bfsr': balancing-free square root method (default, since it avoids scaling by singular values and orthogonalizes the projection matrices, which might make it more accurate than the square root method)

    • 'biorth': like the balancing-free square root method, except it biorthogonalizes the projection matrices (using gram_schmidt_biorth)

Returns:

rom – Reduced-order model.

class pymor.reductors.bt.LQGBTReductor(fom, mu=None)[source]

Bases: GenericBTReductor

Linear Quadratic Gaussian (LQG) Balanced Truncation reductor.

See Section 3 in [MG91].

Parameters:

Methods

error_bounds

Return error bounds for all possible reduced orders.
error_bounds()[source]

Return error bounds for all possible reduced orders.

class pymor.reductors.bt.PRBTReductor(fom, mu=None)[source]

Bases: GenericBTReductor

Positive Real (PR) Balanced Truncation reductor.

See [DP84] and [BU22].

Parameters:

Methods

error_bounds

Return error bounds for all possible reduced orders.
abstract error_bounds()[source]

Return error bounds for all possible reduced orders.