# 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: 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)

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.