Hybrid Bidiagonalization Regularization (HyBR). Hybrid regularization methods have been proposed as effective approaches for solving large-scale ill-posed inverse problems. These methods restrict the solution to lie in a Krylov subspace, but they are hindered by semi-convergence behavior, in that the quality of the solution first increases and then decreases. Hybrid methods apply a standard regularization technique, such as Tikhonov regularization, to the projected problem at each iteration. Thus, regularization in hybrid methods is achieved both by Krylov filtering and by appropriate choice of a regularization parameter at each iteration.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Cho, Taewon; Chung, Julianne; Jiang, Jiahua: Hybrid projection methods for large-scale inverse problems with mixed Gaussian priors (2021)
- Jiang, Jiahua; Chung, Julianne; de Sturler, Eric: Hybrid projection methods with recycling for inverse problems (2021)
- Jia, Zhongxiao; Yang, Yanfei: A joint bidiagonalization based iterative algorithm for large scale general-form Tikhonov regularization (2020)
- Gazzola, Silvia; Hansen, Per Christian; Nagy, James G.: IR tools: a MATLAB package of iterative regularization methods and large-scale test problems (2019)
- Chung, Julianne; Saibaba, Arvind K.; Brown, Matthew; Westman, Erik: Efficient generalized Golub-Kahan based methods for dynamic inverse problems (2018)
- Chung, Julianne; Saibaba, Arvind K.: Generalized hybrid iterative methods for large-scale Bayesian inverse problems (2017)