sGKS matches standard GKS reconstruction quality for Tikhonov regularization while reducing costs via sketching for QR factorizations and skipping reorthogonalization, with theoretical guarantees on iterate identity and quasi-optimal residuals.
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A Sketched Generalized Krylov Subspace Method for Large-Scale Regularization
sGKS matches standard GKS reconstruction quality for Tikhonov regularization while reducing costs via sketching for QR factorizations and skipping reorthogonalization, with theoretical guarantees on iterate identity and quasi-optimal residuals.