pylops.Sum#

class pylops.Sum(*args, **kwargs)[source]#

Sum operator.

Sum along axis of a multi-dimensional array (at least 2 dimensions are required) in forward model, and spread along the same axis in adjoint mode.

Parameters
dimstuple

Number of samples for each dimension

axisint, optional

New in version 2.0.0.

Axis along which model is summed.

dtypestr, optional

Type of elements in input array.

namestr, optional

New in version 2.0.0.

Name of operator (to be used by pylops.utils.describe.describe)

Notes

Given a two dimensional array, the Sum operator re-arranges the input model into a multi-dimensional array of size dims and sums values along axis:

\[y_j = \sum_i x_{i, j}\]

In adjoint mode, the data is spread along the same direction:

\[x_{i, j} = y_j \quad \forall i=0, N-1\]
Attributes
shapetuple

Operator shape

explicitbool

Operator contains a matrix that can be solved explicitly (True) or not (False)

Methods

__init__(dims[, axis, dtype, name])

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.

reset_count()

Reset counters

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.

Examples using pylops.Sum#

Sum

Sum

Sum