# Source code for pymor.algorithms.riccati

# This file is part of the pyMOR project (http://www.pymor.org).

import numpy as np

from pymor.algorithms.lyapunov import mat_eqn_sparse_min_size
from pymor.core.config import config
from pymor.core.defaults import defaults
from pymor.operators.interface import Operator

_DEFAULT_RICC_LRCF_SPARSE_SOLVER_BACKEND = ('pymess' if config.HAVE_PYMESS else

_DEFAULT_RICC_LRCF_DENSE_SOLVER_BACKEND = ('pymess' if config.HAVE_PYMESS else
'slycot' if config.HAVE_SLYCOT else
'scipy')

[docs]@defaults('default_sparse_solver_backend', 'default_dense_solver_backend')
def solve_ricc_lrcf(A, E, B, C, R=None, S=None, trans=False, options=None,
default_sparse_solver_backend=_DEFAULT_RICC_LRCF_SPARSE_SOLVER_BACKEND,
default_dense_solver_backend=_DEFAULT_RICC_LRCF_DENSE_SOLVER_BACKEND):
"""Compute an approximate low-rank solution of a Riccati equation.

Returns a low-rank Cholesky factor :math:Z such that :math:Z Z^T
approximates the solution :math:X of a (generalized)
continuous-time algebraic Riccati equation:

- if trans is False

.. math::
A X E^T + E X A^T
- (E X C^T + S) R^{-1} (E X C^T + S)^T
+ B B^T = 0.

- if trans is True

.. math::
A^T X E + E^T X A
- (E^T X B + S) R^{-1} (E^T X B + S)^T
+ C^T C = 0.

If E is None, it is taken to be identity, and similarly for R.
If S is None, it is taken to be zero.

We assume:

- A and E are real |Operators|,
- B, C and S are real |VectorArrays| from A.source,
- R is a real |NumPy array|,
- (E, A, B, C) is stabilizable and detectable,
- R is symmetric positive definite, and
- :math:B B^T - S R^{-1} S^T (:math:C^T C - S R^{-1} S^T) is
positive semi-definite trans is False (True).

For large-scale problems, we additionally assume that len(B) and
len(C) are small.

If the solver is not specified using the options argument, a solver
backend is chosen based on availability in the following order:

- for sparse problems (minimum size specified by
:func:~pymor.algorithms.lyapunov.mat_eqn_sparse_min_size)

1. pymess (see :func:pymor.bindings.pymess.solve_ricc_lrcf),
2. lrradi (see :func:pymor.algorithms.lrradi.solve_ricc_lrcf),

- for dense problems (smaller than
:func:~pymor.algorithms.lyapunov.mat_eqn_sparse_min_size)

1. pymess (see :func:pymor.bindings.pymess.solve_ricc_lrcf),
2. slycot (see :func:pymor.bindings.slycot.solve_ricc_lrcf),
3. scipy (see :func:pymor.bindings.scipy.solve_ricc_lrcf).

Parameters
----------
A
The non-parametric |Operator| A.
E
The non-parametric |Operator| E or None.
B
The operator B as a |VectorArray| from A.source.
C
The operator C as a |VectorArray| from A.source.
R
The operator R as a 2D |NumPy array| or None.
S
The operator S as a |VectorArray| from A.source or None.
trans
Whether the first |Operator| in the Riccati equation is
transposed.
options
The solver options to use.
See:

- :func:pymor.bindings.scipy.ricc_lrcf_solver_options,
- :func:pymor.bindings.slycot.ricc_lrcf_solver_options,
- :func:pymor.bindings.pymess.ricc_lrcf_solver_options.
- :func:pymor.algorithms.lrradi.ricc_lrcf_solver_options.

default_sparse_solver_backend
Default sparse solver backend to use (pymess, lrradi).
default_dense_solver_backend
Default dense solver backend to use (pymess, slycot, scipy).

Returns
-------
Z
Low-rank Cholesky factor of the Riccati equation solution,
|VectorArray| from A.source.
"""

_solve_ricc_check_args(A, E, B, C, R, S, trans)
if options:
solver = options if isinstance(options, str) else options['type']
backend = solver.split('_')
else:
if A.source.dim >= mat_eqn_sparse_min_size():
backend = default_sparse_solver_backend
else:
backend = default_dense_solver_backend
if backend == 'scipy':
from pymor.bindings.scipy import solve_ricc_lrcf as solve_ricc_impl
elif backend == 'slycot':
from pymor.bindings.slycot import solve_ricc_lrcf as solve_ricc_impl
elif backend == 'pymess':
from pymor.bindings.pymess import solve_ricc_lrcf as solve_ricc_impl
from pymor.algorithms.lrradi import solve_ricc_lrcf as solve_ricc_impl
else:
raise ValueError(f'Unknown solver backend ({backend}).')
return solve_ricc_impl(A, E, B, C, R, S, trans=trans, options=options)

_DEFAULT_POS_RICC_LRCF_DENSE_SOLVER_BACKEND = ('pymess' if config.HAVE_PYMESS else
'slycot' if config.HAVE_SLYCOT else
'scipy')

[docs]@defaults('default_dense_solver_backend')
def solve_pos_ricc_lrcf(A, E, B, C, R=None, S=None, trans=False, options=None,
default_dense_solver_backend=_DEFAULT_RICC_LRCF_DENSE_SOLVER_BACKEND):
"""Compute an approximate low-rank solution of a positive Riccati equation.

Returns a low-rank Cholesky factor :math:Z such that :math:Z Z^T
approximates the solution :math:X of a (generalized) positive
continuous-time algebraic Riccati equation:

- if trans is False

.. math::
A X E^T + E X A^T
+ (E X C^T + S) R^{-1} (E X C^T + S)^T
+ B B^T = 0.

- if trans is True

.. math::
A^T X E + E^T X A
+ (E^T X B + S) R^{-1} (E^T X B + S)^T
+ C^T C = 0.

If E is None, it is taken to be identity, and similarly for R.
If S is None, it is taken to be zero.

If the solver is not specified using the options argument, a solver
backend is chosen based on availability in the following order:

1. pymess (see :func:pymor.bindings.pymess.solve_pos_ricc_lrcf),
2. slycot (see :func:pymor.bindings.slycot.solve_pos_ricc_lrcf),
3. scipy (see :func:pymor.bindings.scipy.solve_pos_ricc_lrcf).

Parameters
----------
A
The non-parametric |Operator| A.
E
The non-parametric |Operator| E or None.
B
The operator B as a |VectorArray| from A.source.
C
The operator C as a |VectorArray| from A.source.
R
The operator R as a 2D |NumPy array| or None.
S
The operator S as a |VectorArray| from A.source or None.
trans
Whether the first |Operator| in the positive Riccati equation is
transposed.
options
The solver options to use.
See:

- :func:pymor.bindings.scipy.pos_ricc_lrcf_solver_options,
- :func:pymor.bindings.slycot.pos_ricc_lrcf_solver_options,
- :func:pymor.bindings.pymess.pos_ricc_lrcf_solver_options.

default_dense_solver_backend
Default dense solver backend to use (pymess, slycot, scipy).

Returns
-------
Z
Low-rank Cholesky factor of the positive Riccati equation
solution, |VectorArray| from A.source.
"""

_solve_ricc_check_args(A, E, B, C, R, S, trans)
if options:
solver = options if isinstance(options, str) else options['type']
backend = solver.split('_')
else:
backend = default_dense_solver_backend
if backend == 'scipy':
from pymor.bindings.scipy import solve_pos_ricc_lrcf as solve_ricc_impl
elif backend == 'slycot':
from pymor.bindings.slycot import solve_pos_ricc_lrcf as solve_ricc_impl
elif backend == 'pymess':
from pymor.bindings.pymess import solve_pos_ricc_lrcf as solve_ricc_impl
else:
raise ValueError(f'Unknown solver backend ({backend}).')
return solve_ricc_impl(A, E, B, C, R, S, trans=trans, options=options)

def _solve_ricc_check_args(A, E, B, C, R, S, trans):
assert isinstance(A, Operator) and A.linear
assert not A.parametric
assert A.source == A.range
if E is not None:
assert isinstance(E, Operator) and E.linear
assert not E.parametric
assert E.source == E.range == A.source
assert B in A.source
assert C in A.source
if R is not None:
assert isinstance(R, np.ndarray) and R.ndim == 2
assert R.shape == R.shape
if not trans:
assert R.shape == len(C)
else:
assert R.shape == len(B)
if S is not None:
assert S in A.source
if not trans:
assert len(S) == len(C)
else:
assert len(S) == len(B)