PyLops is an open-source Python library focused on providing a backend-agnostic, idiomatic, matrix-free library of linear operators and related computations. It is inspired by the iconic MATLAB Spot – A Linear-Operator Toolbox project.

Linear operators and inverse problems are at the core of many of the most used algorithms in signal processing, image processing, and remote sensing. For small-scale problems, matrices can be explicitly computed and manipulated with Python numerical scientific libraries such as NumPy and SciPy.

Large-scale problems often feature matrices that are prohibitive in size—but whose operations can be described by simple functions. PyLops operators exploit this to represent a linear operator not as array of numbers, but by functions which describe matrix-vector products in forward and adjoint modes. Moreover, many iterative methods (e.g. cg, lsqr) are designed to not rely on the elements of the matrix, only these matrix-vector products. PyLops offers such solvers for many different types of problems, in particular least-squares and sparsity-promoting inversions.

Get started by installing PyLops and following our quick tour.


A common terminology is used within the entire documentation of PyLops. Every linear operator and its application to a model will be referred to as forward model (or operation)

\[\mathbf{y} = \mathbf{A} \mathbf{x}\]

while its application to a data is referred to as adjoint model (or operation)

\[\mathbf{x} = \mathbf{A}^H \mathbf{y}\]

where \(\mathbf{x}\) is called model and \(\mathbf{y}\) is called data. The operator \(\mathbf{A}:\mathbb{F}^m \to \mathbb{F}^n\) effectively maps a vector of size \(m\) in the model space to a vector of size \(n\) in the data space, conversely the adjoint operator \(\mathbf{A}^H:\mathbb{F}^n \to \mathbb{F}^m\) maps a vector of size \(n\) in the data space to a vector of size \(m\) in the model space. As linear operators mimics the effect a matrix on a vector we can also loosely refer to \(m\) as the number of columns and \(n\) as the number of rows of the operator.

Ultimately, solving an inverse problems accounts to removing the effect of \(\mathbf{A}\) from the data \(\mathbf{y}\) to retrieve the model \(\mathbf{x}\).

For a more detailed description of the concepts of linear operators, adjoints and inverse problems in general, you can head over to one of Jon Claerbout’s books such as Basic Earth Imaging.


PyLops is build on top of the scipy class scipy.sparse.linalg.LinearOperator.

This class allows in fact for the creation of objects (or interfaces) for matrix-vector and matrix-matrix products that can ultimately be used to solve any inverse problem of the form \(\mathbf{y}=\mathbf{A}\mathbf{x}\).

As explained in the scipy LinearOperator official documentation, to construct a scipy.sparse.linalg.LinearOperator, a user is required to pass appropriate callables to the constructor of this class, or subclass it. More specifically one of the methods _matvec and _matmat must be implemented for the forward operator and one of the methods _rmatvec or _adjoint may be implemented to apply the Hermitian adjoint. The attributes/properties shape (pair of integers) and dtype (may be None) must also be provided during __init__ of this class.

Any linear operator developed within the PyLops library follows this philosophy. As explained more in details in Implementing new operators section, a linear operator is created by subclassing the scipy.sparse.linalg.LinearOperator class and _matvec and _rmatvec are implemented.


PyLops was initially written by Equinor It is a flexible and scalable python library for large-scale optimization with linear operators that can be tailored to our needs, and as contribution to the free software community. Since June 2021, PyLops is a NUMFOCUS Affiliated Project.