期刊
INVERSE PROBLEMS
卷 24, 期 5, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/0266-5611/24/5/055009
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资金
- Upper Austrian Technology and Research Promotion
The quasi-optimality criterion chooses the regularization parameter in inverse problems without taking into account the noise level. This rule works remarkably well in practice, although Bakushinskii has shown that there are always counterexamples with very poor performance. We propose an average case analysis of quasi-optimality for spectral cut-off estimators (also known as truncated singular value decomposition, TSVD) and we prove that the quasi-optimality criterion determines estimators which are rate-optimal on average. Its practical performance is illustrated with a calibration problem from mathematical finance.
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