# pylops.HStack¶

class pylops.HStack(ops, nproc=1, dtype=None)[source]

Horizontal stacking.

Stack a set of N linear operators horizontally.

Parameters: ops : list Linear operators to be stacked. Alternatively, numpy.ndarray or scipy.sparse matrices can be passed in place of one or more operators. nproc : int, optional Number of processes used to evaluate the N operators in parallel using multiprocessing. If nproc=1, work in serial mode. dtype : str, optional Type of elements in input array. ValueError If ops have different number of columns

Notes

An horizontal stack of N linear operators is created such as its application in forward mode leads to

$\begin{split}\begin{bmatrix} \mathbf{L}_{1} & \mathbf{L}_{2} & \ldots & \mathbf{L}_{N} \end{bmatrix} \begin{bmatrix} \mathbf{x}_{1} \\ \mathbf{x}_{2} \\ \vdots \\ \mathbf{x}_{N} \end{bmatrix} = \mathbf{L}_{1} \mathbf{x}_1 + \mathbf{L}_{2} \mathbf{x}_2 + \ldots + \mathbf{L}_{N} \mathbf{x}_N\end{split}$

while its application in adjoint mode leads to

$\begin{split}\begin{bmatrix} \mathbf{L}_{1}^H \\ \mathbf{L}_{2}^H \\ \vdots \\ \mathbf{L}_{N}^H \end{bmatrix} \mathbf{y} = \begin{bmatrix} \mathbf{L}_{1}^H \mathbf{y} \\ \mathbf{L}_{2}^H \mathbf{y} \\ \vdots \\ \mathbf{L}_{N}^H \mathbf{y} \end{bmatrix} = \begin{bmatrix} \mathbf{x}_{1} \\ \mathbf{x}_{2} \\ \vdots \\ \mathbf{x}_{N} \end{bmatrix}\end{split}$
Attributes: shape : tuple Operator shape explicit : bool Operator contains a matrix that can be solved explicitly (True) or not (False)

Methods

 __init__(ops[, nproc, 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, 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.