# pylops.BlockDiag¶

class pylops.BlockDiag(ops, dtype=None)[source]

Block-diagonal operator.

Create a block-diagonal operator from N linear operators.

Parameters: ops : list Linear operators to be stacked dtype : str, optional Type of elements in input array.

Notes

A block-diagonal operator composed of N linear operators is created such as its application in forward mode leads to

$\begin{split}\begin{bmatrix} \mathbf{L_1} & \mathbf{0} & ... & \mathbf{0} \\ \mathbf{0} & \mathbf{L_2} & ... & \mathbf{0} \\ ... & ... & ... & ... \\ \mathbf{0} & \mathbf{0} & ... & \mathbf{L_N} \end{bmatrix} \begin{bmatrix} \mathbf{x}_{1} \\ \mathbf{x}_{2} \\ ... \\ \mathbf{x}_{N} \end{bmatrix} = \begin{bmatrix} \mathbf{L_1} \mathbf{x}_{1} \\ \mathbf{L_2} \mathbf{x}_{2} \\ ... \\ \mathbf{L_N} \mathbf{x}_{N} \end{bmatrix}\end{split}$

$\begin{split}\begin{bmatrix} \mathbf{L_1}^H \quad \mathbf{0} \quad ... \quad \mathbf{0} \\ \mathbf{0} \quad \mathbf{L_2}^H \quad ... \quad \mathbf{0} \\ ... \quad ... \quad ... \quad ... \\ \mathbf{0} \quad \mathbf{0} \quad ... \quad \mathbf{L_N}^H \end{bmatrix} \begin{bmatrix} \mathbf{y}_{1} \\ \mathbf{y}_{2} \\ ... \\ \mathbf{y}_{N} \end{bmatrix} = \begin{bmatrix} \mathbf{L_1}^H \mathbf{y}_{1} \\ \mathbf{L_2}^H \mathbf{y}_{2} \\ ... \\ \mathbf{L_N}^H \mathbf{y}_{N} \end{bmatrix}\end{split}$
Attributes: shape : tuple Operator shape explicit : bool Operator contains a matrix that can be solved explicitly (True) or not (False)
 __init__(self, ops[, dtype]) Initialize this LinearOperator. adjoint(self) Hermitian adjoint. cond(self, \*\*kwargs_eig) Condition number of linear operator. conj(self) Complex conjugate operator div(self, y[, niter]) Solve the linear problem $$\mathbf{y}=\mathbf{A}\mathbf{x}$$. dot(self, x) Matrix-matrix or matrix-vector multiplication. eigs(self[, neigs, symmetric, niter]) Most significant eigenvalues of linear operator. matmat(self, X) Matrix-matrix multiplication. matvec(self, x) Matrix-vector multiplication. rmatvec(self, x) Adjoint matrix-vector multiplication. transpose(self) Transpose this linear operator.