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 - i.e., store the last max_neval model/data vector pairs for forward and adjoint evaluations. Note that cache hits require identical input values and dtype; nearby vectors are evaluated independently.

Parameters:

Oppylops.LinearOperator

PyLops linear operator

max_nevalint, optional

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

Attributes:

storelist

List of stored (x, y) pairs.

nevalint

Number of evaluations of the operator.

shapetuple

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

Methods

__init__(Op[, max_neval])
adjoint()
apply_columns(cols)	Apply subset of columns of operator
cond([uselobpcg, backend])	Condition number of linear operator.
conj()	Complex conjugate operator
div(y[, niter, densesolver])	Solve the linear problem or divide by a scalar
dot(x)	Dot-product
eigs([neigs, symmetric, niter, uselobpcg, ...])	N-most significant eigenvalues of a linear operator.
matmat(X[, pool])	Matrix-matrix multiplication.
matvec(x)	Matrix-vector multiplication.
reset_count()	Reset counters
rmatmat(X[, pool])	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

Memoized forward and adjoint evaluations

Memoized forward and adjoint evaluations

17. Real/Complex Inversion

17. Real/Complex Inversion