pylops.MemoizeOperator#

class pylops.MemoizeOperator(Op, max_neval=10)[source]#

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
Oppylops.LinearOperator

PyLops linear operator

max_nevalint, optional

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

Attributes
shapetuple

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

explicitbool

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

Methods

__init__(Op[, max_neval])

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.

reset_count()

Reset counters

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()

Examples using pylops.MemoizeOperator#

17. Real/Complex Inversion

17. Real/Complex Inversion