pymoto.solvers.ILU

class pymoto.solvers.ILU(A=None, **kwargs)

Incomplete LU factorization

__init__(A=None, **kwargs)

Initialize the ILU preconditioner

Parameters:

  • A (optional) – The matrix

  • **kwargs (optional) – Keyword arguments passed to scipy.sparse.linalg.spilu

Methods

__init__([A])

Initialize the ILU preconditioner

residual(A, x, b[, trans])

Calculates the (relative) residual of the linear system of equations

solve(rhs[, x0, trans])

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

update(A)

Updates with a new matrix of the same structure

Attributes

defined

update(A)

Updates with a new matrix of the same structure

Parameters:

A (matrix) – The new matrix of size (N, N)

Returns:

self

solve(rhs, x0=None, trans='N')

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

Parameters:

  • rhs – Right hand side \(\mathbf{b}\) of shape (N) or (N, K) for multiple right-hand-sides

  • x0 (optional) – Initial guess for the solution

  • trans (optional) – Option to transpose matrix ‘N’: A @ x == rhs (default) Normal matrix ‘T’: A^T @ x == rhs Transposed matrix ‘H’: A^H @ x == rhs Hermitian transposed matrix (conjugate transposed)

Returns:

Solution vector \(\mathbf{x}\) of same shape as \(\mathbf{b}\)

defined = True
static residual(A, x, b, trans='N')

Calculates the (relative) residual of the linear system of equations

The residual is calculated as \(r = \frac{\left| \mathbf{A} \mathbf{x} - \mathbf{b} \right|}{\left| \mathbf{b} \right|}\)

Parameters:

  • A – The matrix

  • x – Solution vector

  • b – Right-hand side

  • trans (optional) – Matrix tranformation (N is normal, T is transposed, H is hermitian transposed)

Returns:

Residual value