Pycsou: a Python 3 package for solving linear inverse problems

Rich collection of linear operators, loss functionals and penalty functionals commonly used in practice. Arithmetic operations for linear operators, loss functionals and penalty functionals, hence allowing to add, substract, scale, compose, exponentiate or stack those various objects with one another and hence quickly design custom complex optimisation problems. Rich collection of state-of-the-art iterative proximal algorithms, including efficient primal-dual splitting methods which involve only gradient steps, proximal steps and simple linear evaluations. Support for matrix-free linear operators, making it easy to work with large scale linear operators that may not necessarily fit in memory. Matrix-free linear operators can be implemented from scratch by subclassing the asbtract class LinearOperator, or built from Scipy sparse matrices, distributed Dask arrays or Pylops matrix-free operators (which now support GPU computations). Automatic differentiation/proximation rules, allowing to automatically compute the derivative/proximal operators of functionals constructed from arithmetic operations on common functionals shipped with Pycsou. Rule-of-thumbs for setting automatically the hyper-parameters of the provided proximal algorithms. Routines for interfacing with the Python packages scipy.sparse and Pylops. This allows to use the sparse linear algebra routines from scipy.sparse on Pycsou LinearOperator, and benefit from the large catalogue of linear operators and solvers from Pylops.Documentation: https://matthieumeo.github.io/pycsou/html/inde