A novel modified TRSVD method for large-scale linear discrete ill-posed problems
نویسندگان
چکیده
The truncated singular value decomposition (TSVD) is a popular method for solving linear discrete ill-posed problems with small to moderately sized matrix A. This replaces the A by closest Ak of low rank k, and then computes minimal norm solution system equations rank-deficient so obtained. modified TSVD (MTSVD) improves method, replacing that closer than in unitarily invariant has same spectral condition number as Ak. Approximations SVD large can be computed quite efficiently using randomized (RSVD) method. paper presents novel (MTRSVD) computing approximate solutions large-scale problems. rank, determined aid discrepancy principle, but other techniques selecting suitable also used. Numerical examples illustrate effectiveness proposed compare it RSVD
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A modified TSVD method for discrete ill-posed problems
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ژورنال
عنوان ژورنال: Applied Numerical Mathematics
سال: 2021
ISSN: ['1873-5460', '0168-9274']
DOI: https://doi.org/10.1016/j.apnum.2020.08.019