pymoto.LinSolve

class pymoto.LinSolve(dep_tol: float = 1e-05, hermitian: bool = None, symmetric: bool = None, positive_definite: bool = None, solver: LinearSolver = None)

Solves linear system of equations \(\mathbf{A}\mathbf{x}=\mathbf{b}\)

Self-adjointness is automatically detected using LDAWrapper.

Input Signals:

• A (dense or sparse matrix): The system matrix \(\mathbf{A}\) of size (n, n)

• b (vector): Right-hand-side vector of size (n) or block-vector of size (n, Nrhs)

Output Signal:

• x (vector): Solution vector of size (n) or block-vector of size (n, Nrhs)

use_lda_solver

Use the linear-dependency-aware solver LDAWrapper to prevent redundant computations

__init__(dep_tol: float = 1e-05, hermitian: bool = None, symmetric: bool = None, positive_definite: bool = None, solver: LinearSolver = None)

Initialize the linear solver module

Parameters:

• dep_tol (float, optional) – Tolerance for detecting linear dependence of solution vectors. Defaults to 1e-5.

• hermitian (bool, optional) – Flag to omit the automatic detection for Hermitian matrix, saves some work for large matrices

• symmetric (bool, optional) – Flag to omit the automatic detection for symmetric matrix, saves some work for large matrices

• positive_definite (bool, optional) – Flag to specify if the matrix is positive definite

• solver (pymoto.solvers.LinearSolver, optional) – Manually override the linear solver used, instead of the the solver from pymoto.solvers.auto_determine_solver()

Methods

__init__([dep_tol, hermitian, symmetric, ...])	Initialize the linear solver module
connect(sig_in[, sig_out])	Connect without automatic adding to a function network
get_input_sensitivities([as_list])
get_input_states([as_list])
get_output_sensitivities([as_list])
get_output_states([as_list])
reset()	Reset the state of the sensitivities (they are set to zero or to None)
response()	Calculate the response from sig_in and output this to sig_out
sensitivity()	Calculate sensitivities using backpropagation

Attributes

n_in	Get the number of input signals
n_out	Get the number of output signals
sig_in
sig_out
use_lda_solver

use_lda_solver = True

connect(sig_in: Signal | Iterable[Signal], sig_out: Signal | Iterable[Signal] = None)

Connect without automatic adding to a function network

get_input_sensitivities(as_list=False)

get_input_states(as_list=False)

get_output_sensitivities(as_list=False)

get_output_states(as_list=False)

property n_in: int

Get the number of input signals

property n_out: int

Get the number of output signals

Note: Cannot be used in the initial __call__()

reset()

Reset the state of the sensitivities (they are set to zero or to None)

response()

Calculate the response from sig_in and output this to sig_out

sensitivity()

Calculate sensitivities using backpropagation

Based on the sensitivity we get from sig_out, reverse the process and output the new sensitivities to sig_in

sig_in: List = None

sig_out: List = None