The low rank approximations and Ritz values in LSQR for linear discrete ill-posed problem
Sign Up to like & getrecommendations! Published in 2020 at "Inverse Problems"
DOI: 10.1088/1361-6420/ab6f42
Abstract: LSQR and its mathematically equivalent CGLS have been popularly used over the decades for large-scale linear discrete ill-posed problems, where the iteration number $k$ plays the role of the regularization parameter. It has been long… read more here.
Keywords: linear discrete; ritz values; ill posed; rank approximations ... See more keywords
Regularization properties of LSQR for linear discrete ill-posed problems in the multiple singular value case and best, near best and general low rank approximations
Sign Up to like & getrecommendations! Published in 2020 at "Inverse Problems"
DOI: 10.1088/1361-6420/ab9c45
Abstract: For the large-scale linear discrete ill-posed problem $\min\|Ax-b\|$ or $Ax=b$ with $b$ contaminated by white noise, the Golub-Kahan bidiagonalization based LSQR method and its mathematically equivalent CGLS, the Conjugate Gradient (CG) method applied to $A^TAx=A^Tb$,… read more here.
Keywords: linear discrete; rank approximations; ill posed; discrete ill ... See more keywords
Low tensor train and low multilinear rank approximations of 3D tensors for compression and de-speckling of optical coherence tomography images.
Sign Up to like & getrecommendations! Published in 2023 at "Physics in medicine and biology"
DOI: 10.1088/1361-6560/acd6d1
Abstract: Objective. Many methods for compression and/or de-speckling of 3D optical coherence tomography (OCT) images operate on a slice-by-slice basis and, consequently, ignore spatial relations between the B-scans. Thus, we develop compression ratio (CR)-constrained low tensor… read more here.
Keywords: rank approximations; low rank; speckling optical; compression speckling ... See more keywords