PyLops
swMATH ID:  30606 
Software Authors:  Matteo Ravasi, Ivan Vasconcelos 
Description:  PyLops  A LinearOperator Python Library for large scale optimization. Linear operators and optimisation are at the core of many algorithms used in signal and image processing, remote sensing, and inverse problems. For small to mediumscale problems, existing software packages (e.g., MATLAB, Python numpy and scipy) allow for explicitly building dense (or sparse) matrices and performing algebraic operations (e.g., computation of matrixvector products and manipulation of matrices) with syntax that closely represents their corresponding analytical forms. However, many real application, largescale operators do not lend themselves to explicit matrix representations, usually forcing practitioners to forego of the convenient linearalgebra syntax available for their explicitmatrix counterparts. PyLops is an opensource Python library providing a flexible and scalable framework for the creation and combination of socalled linear operators, classbased entities that represent matrices and inherit their associated syntax convenience, but do not rely on the creation of explicit matrices. We show that PyLops operators can dramatically reduce the memory load and CPU computations compared to explicitmatrix calculations, while still allowing users to seamlessly use their existing knowledge of compact matrixbased syntax that scales to any problem size because no explicit matrices are required. Subjects: 
Homepage:  https://pylops.readthedocs.io/en/latest/ 
Source Code:  https://github.com/ElsevierSoftwareX/SOFTX_2019_106 
Keywords:  Mathematical Software; arXiv_cs.MS; Python; large scale optimization; linear operators 
Related Software:  NumPy; SciPy; Python; emcee; PyMultiNest; PyAutoFit; Numba; Matplotlib; dynesty; corner.py; COLOSSUS; Astropy; pyquad; PySwarms; scikitimage; Scikit; AutoLens; gravlens; lenstronomy; visilens 
Cited in:  0 Publications 
Standard Articles
1 Publication describing the Software  Year 

PyLops  A LinearOperator Python Library for large scale optimization Matteo Ravasi, Ivan Vasconcelos 
2019
