# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright (c) 2008-2024
# National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
from abc import ABCMeta, abstractmethod
import enum
from typing import Optional, Union, Tuple
from scipy.sparse import spmatrix
import numpy as np
from pyomo.contrib.pynumero.sparse.base_block import BaseBlockVector, BaseBlockMatrix
from pyomo.contrib.pynumero.sparse import BlockVector, BlockMatrix
[docs]class LinearSolverStatus(enum.Enum):
successful = 0
not_enough_memory = 1
singular = 2
error = 3
warning = 4
max_iter = 5
[docs]class LinearSolverResults(object):
def __init__(self, status: Optional[LinearSolverStatus] = None):
self.status = status
[docs]class LinearSolverInterface(object, metaclass=ABCMeta):
[docs] @abstractmethod
def solve(
self,
matrix: Union[spmatrix, BlockMatrix],
rhs: Union[np.ndarray, BlockVector],
raise_on_error: bool = True,
) -> Tuple[Optional[Union[np.ndarray, BlockVector]], LinearSolverResults]:
pass
[docs]class DirectLinearSolverInterface(LinearSolverInterface, metaclass=ABCMeta):
[docs] @abstractmethod
def do_symbolic_factorization(
self, matrix: Union[spmatrix, BlockMatrix], raise_on_error: bool = True
) -> LinearSolverResults:
pass
[docs] @abstractmethod
def do_numeric_factorization(
self, matrix: Union[spmatrix, BlockMatrix], raise_on_error: bool = True
) -> LinearSolverResults:
pass
[docs] @abstractmethod
def do_back_solve(
self, rhs: Union[np.ndarray, BlockVector], raise_on_error: bool = True
) -> Tuple[Optional[Union[np.ndarray, BlockVector]], LinearSolverResults]:
pass
[docs] def solve(
self,
matrix: Union[spmatrix, BlockMatrix],
rhs: Union[np.ndarray, BlockVector],
raise_on_error: bool = True,
) -> Tuple[Optional[Union[np.ndarray, BlockVector]], LinearSolverResults]:
symbolic_res = self.do_symbolic_factorization(
matrix, raise_on_error=raise_on_error
)
if symbolic_res.status != LinearSolverStatus.successful:
return None, symbolic_res
numeric_res = self.do_numeric_factorization(
matrix, raise_on_error=raise_on_error
)
if numeric_res.status != LinearSolverStatus.successful:
return None, numeric_res
return self.do_back_solve(rhs, raise_on_error=raise_on_error)