# pylops.signalprocessing.Fredholm1¶

class pylops.signalprocessing.Fredholm1(G, nz=1, saveGt=True, usematmul=True, dtype='float64')[source]

Fredholm integral of first kind.

Implement a multi-dimensional Fredholm integral of first kind. Note that if the integral is two dimensional, this can be directly implemented using pylops.basicoperators.MatrixMult. A multi-dimensional Fredholm integral can be performed as a pylops.basicoperators.BlockDiag operator of a series of pylops.basicoperators.MatrixMult. However, here we take advantage of the structure of the kernel and perform it in a more efficient manner.

Parameters: G : numpy.ndarray Multi-dimensional convolution kernel of size $$[n_{slice} \times n_x \times n_y]$$ nz : numpy.ndarray, optional Additional dimension of model saveGt : bool, optional Save G and G^H to speed up the computation of adjoint (True) or create G^H on-the-fly (False) Note that saveGt=True will double the amount of required memory usematmul : bool, optional Use numpy.matmul (True) or for-loop with numpy.dot (False). As it is not possible to define which approach is more performant (this is highly dependent on the size of G and input arrays as well as the hardware used in the compution), we advise users to time both methods for their specific problem prior to making a choice. dtype : str, optional Type of elements in input array.

Notes

A multi-dimensional Fredholm integral of first kind can be expressed as

$d(sl, x, z) = \int{G(sl, x, y) m(sl, y, z) dy} \quad \forall sl=1,n_{slice}$

on the other hand its adjoin is expressed as

$m(sl, y, z) = \int{G^*(sl, y, x) d(sl, x, z) dx} \quad \forall sl=1,n_{slice}$

In discrete form, this operator can be seen as a block-diagonal matrix multiplication:

$\begin{split}\begin{bmatrix} \mathbf{G}_{sl1} & \mathbf{0} & ... & \mathbf{0} \\ \mathbf{0} & \mathbf{G}_{sl2} & ... & \mathbf{0} \\ ... & ... & ... & ... \\ \mathbf{0} & \mathbf{0} & ... & \mathbf{G}_{slN} \end{bmatrix} \begin{bmatrix} \mathbf{m}_{sl1} \\ \mathbf{m}_{sl2} \\ ... \\ \mathbf{m}_{slN} \end{bmatrix}\end{split}$
Attributes: shape : tuple Operator shape explicit : bool Operator contains a matrix that can be solved explicitly (True) or not (False)

Methods

 __init__(G[, nz, saveGt, usematmul, dtype]) 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]) 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. transpose() Transpose this linear operator.