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

Op : pylops.LinearOperator

PyLops linear operator

max_neval : int, optional

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

shape : tuple

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

explicit : bool

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


__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