  In this paper, we describe TRIPs-Py, a new Python package of linear discrete
inverse problems solvers and test problems. The goal of the package is
two-fold: 1) to provide tools for solving small and large-scale inverse
problems, and 2) to introduce test problems arising from a wide range of
applications. The solvers available in TRIPs-Py include direct regularization
methods (such as truncated singular value decomposition and Tikhonov) and
iterative regularization techniques (such as Krylov subspace methods and recent
solvers for $\ell_p$-$\ell_q$ formulations, which enforce sparse or
edge-preserving solutions and handle different noise types). All our solvers
have built-in strategies to define the regularization parameter(s). Some of the
test problems in TRIPs-Py arise from simulated image deblurring and
computerized tomography, while other test problems model realistic problems in
dynamic computerized tomography. Numerical examples are included to illustrate
the usage as well as the performance of the described methods on the provided
test problems. To the best of our knowledge, TRIPs-Py is the first Python
software package of this kind, which may serve both research and didactical
purposes.
