A priorconditioned generalized Krylov subspace method (PS-GKS) accelerates iteratively reweighted least-squares solvers for sparse inverse problems, adds restarted and recycled variants, and beats existing projection solvers on 1D and tomography benchmarks.
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Priorconditioned Sparsity-Promoting Projection Methods for Deterministic and Bayesian Linear Inverse Problems
A priorconditioned generalized Krylov subspace method (PS-GKS) accelerates iteratively reweighted least-squares solvers for sparse inverse problems, adds restarted and recycled variants, and beats existing projection solvers on 1D and tomography benchmarks.