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.

翻译：本文介绍了TRIPs-Py，一个用于线性离散反问题求解器和测试问题的新Python软件包。该软件包具有双重目标：1）提供求解小规模和大规模反问题的工具；2）引入源自广泛应用的测试问题。TRIPs-Py中的求解器包括直接正则化方法（如截断奇异值分解和吉洪诺夫正则化）以及迭代正则化技术（如Krylov子空间方法和针对$\ell_p$-$\ell_q$形式的最新求解器，这些方法能够实现稀疏或边缘保持解并处理不同噪声类型）。所有求解器均内置了正则化参数确定策略。TRIPs-Py中的部分测试问题源于模拟图像去模糊和计算机断层成像，而其他测试问题则模拟动态计算机断层成像中的实际问题。文中包含数值示例以说明所述方法在提供的测试问题上的应用及性能表现。据我们所知，TRIPs-Py是首个此类Python软件包，可同时服务于科研与教学目的。