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Chung, Julianne; Gazzola, Silvia

Computational methods for large-scale inverse problems: a survey on hybrid projection methods. (English) Zbl 1539.65053

SIAM Rev. 66, No. 2, 205-284 (2024).
Summary: This paper surveys an important class of methods that combine iterative projection methods and variational regularization methods for large-scale inverse problems. Iterative methods such as Krylov subspace methods are invaluable in the numerical linear algebra community and have proved important in solving inverse problems due to their inherent regularizing properties and their ability to handle large-scale problems. Variational regularization describes a broad and important class of methods that are used to obtain reliable solutions to inverse problems, whereby one solves a modified problem that incorporates prior knowledge. Hybrid projection methods combine iterative projection methods with variational regularization techniques in a synergistic way, providing researchers with a powerful computational framework for solving very large inverse problems. Although the idea of a hybrid Krylov method for linear inverse problems goes back to the 1980s, several recent advances on new regularization frameworks and methodologies have made this field ripe for extensions, further analyses, and new applications. In this paper, we provide a practical and accessible introduction to hybrid projection methods in the context of solving large (linear) inverse problems.

MSC:

65F22 Ill-posedness and regularization problems in numerical linear algebra
15A29 Inverse problems in linear algebra
68U10 Computing methodologies for image processing
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory

Keywords:

inverse problems; projection methods; regularization; Krylov methods; Tikhonov regularization; variational regularization; image deconvolution; computed tomography

Software:

ODL; GitHub; ASTRA; GKB-FP; AIR tools; TomoPy; TIGRE; IR Tools; HyBR; Parallel Colt; hIPPYlib; LSQR; CIL; LSMR; Regularization tools; CCPi-Regularisation
Cite Review PDF
Full Text: DOI arXiv

References:

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