PyLops/pylops

What it solves

PyLops addresses the challenge of handling large-scale linear inverse problems where the matrices involved are too massive to be explicitly stored in memory. It allows users to perform complex linear algebra and optimization without needing to compute or store the full matrix of numbers.

How it works

Instead of representing linear operators as arrays of numbers (matrices), PyLops represents them as functions that describe matrix-vector products. Because many iterative solvers (like conjugate gradient or LSQR) only require the result of these products rather than the individual matrix elements, PyLops can execute these operations efficiently. It provides a library of pre-defined operators—such as derivatives, convolutions, and FFTs—and includes built-in solvers for least-squares and sparse inversion.

Who it’s for

It is designed for researchers and engineers working in signal processing, image processing, and remote sensing who need to solve large-scale optimization and inverse problems.

Highlights

• Matrix-free computation: Operates on functions rather than explicit arrays to save memory.
• Backend-agnostic: Designed to be idiomatic and flexible across different Python numerical environments.
• Comprehensive operator library: Includes built-in support for derivatives, convolutions, and Fast Fourier Transforms (FFTs).
• Integrated solvers: Provides direct tools for least-squares and sparse inversion.