pylops.MemoizeOperator

class pylops.MemoizeOperator(Op, max_neval=10)

Memoize Operator.

This operator can be used to wrap any PyLops operator and add a memoize functionality and stores the last max_neval model/data vector pairs

Parameters:

Op : pylops.LinearOperator

PyLops linear operator

max_neval : int, optional

Maximum number of previous evaluations stored, use np.inf for infinite memory

Attributes:

shape : tuple

Operator shape \([n \times m]\)

explicit : bool

Operator contains a matrix that can be solved explicitly (True) or not (False)

Methods

__init__(Op[, max_neval])	Initialize this LinearOperator.
adjoint()	Hermitian adjoint.
apply_columns(cols)	Apply subset of columns of operator
cond([uselobpcg])	Condition number of linear operator.
conj()	Complex conjugate operator
div(y[, niter, densesolver])	Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(x)	Matrix-matrix or matrix-vector multiplication.
eigs([neigs, symmetric, niter, uselobpcg])	Most significant eigenvalues of linear operator.
matmat(X)	Matrix-matrix multiplication.
matvec(x)	Matrix-vector multiplication.
rmatmat(X)	Matrix-matrix multiplication.
rmatvec(x)	Adjoint matrix-vector multiplication.
todense([backend])	Return dense matrix.
toimag([forw, adj])	Imag operator
toreal([forw, adj])	Real operator
tosparse()	Return sparse matrix.
trace([neval, method, backend])	Trace of linear operator.
transpose()	Transpose this linear operator.

Examples using pylops.MemoizeOperator

17. Real/Complex Inversion