TRIPs-Py: Techniques for regularization of inverse problems in python
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11075_2024_Article_1878.pdf
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Author(s)
Pasha, Mirjeta
Gazzola, Silvia
Sanderford, Connor
Ugwu, Ugochukwu O.
Date Issued
July 22, 2024
Journal
Numerical Algorithms
Publisher
Springer Science and Business Media LLC
Citation
Pasha, M., Gazzola, S., Sanderford, C. et al. TRIPs-Py: Techniques for regularization of inverse problems in python. Numer Algor (2024).
Version
Final published version
Abstract
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 ℓ𝑝
-ℓ𝑞
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 real 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.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
https://doi.org/10.1007/s11075-024-01878-w
