# Overview¶

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.

## Terminology¶

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)**

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

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.

## Implementation¶

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.

## History¶

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.